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ISSN : 2249 - 8958 Website: www.ijeat.org Volume-10 Issue-1, OCTOBER 2020 Published by: Blue Eyes Intelligence Engineering and Sciences Publication
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www.ijeat.org Exploring Innovation Editor-In-Chief Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel CEO, Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), Bhopal (MP), India
Associate Editor-In-Chief Dr. Takialddin Al Smadi Professor, Department of Communication and Electronics, Jerash Universtiy, Jerash, Jordan
Dr. Vo Quang Minh Senior Lecturer and Head, Department of Land Resources, College of Environment and Natural Resources (CENRes), Can Tho City, Vietnam.
Dr. Stamatis Papadakis Lecturer, Department of Preschool Education, University of Crete, Greece.
Dr. Ali OTHMAN Al Janaby Lecturer, Department of Communications Engineering, College of Electronics Engineering University of Ninevah, Iraq.
Dr. Rabiul Ahasan Professor, Department of Industrial Engineering, King Saud University, Saudi Arabia.
Dr. Hakimjon Zaynidinov Professor and Head, Department of Computer Science, Tashkent University of Information Technologies, Uzbekistan.
Prof. MPS Chawla Ex-Chairman, IEEE MP Sub-Section, India, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology & Science Indore, Madhya Pradesh, India.
Associate Editor-In-Chief Members Dr. Anil Kumar Yadav Ph.D(ME), ME(ME), BE(ME) Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science, Technology and Maritime Transport, Egypt.
Dr. Ahmed Daabo Lecturer and Researcher, Department of Mining Engineering, University of Mosul, Iraq
Dr. Carlo H. Godoy Jr Professor, Department of Support, Human Edge Software Philippines, Philippines
Dr. Morteza Pakdaman Assistant Professor, Department of CRI, Climatology of Atmospheric Disasters Research Group, Climatological Research Institute (CRI), Mashhad, Iran.
Scientific Editors Dr. Fadiya Samson Oluwaseun Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern Cyprus, Turkey.
Dr. Robert Brian Smith International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia
Dr. Durgesh Mishra Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India
Dr. Vinod Kumar Singh Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India
Dr. Rachana Dubey Ph.D.(CSE), MTech(CSE), B.E(CSE) Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India
Executive Editor Chair Dr. Deepak Garg Professor, Department of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India
Members of Executive Editor Chair Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran.
Dr. Saber Mohamed Abd-Allah Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.
Dr. Xiaoguang Yue Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.
Dr. Sunandan Bhunia Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia (Bengal), India.
Dr. Awatif Mohammed Ali Elsiddieg Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan, Saudi Arabia.
Technical Program Committee Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.
Technical Program Committee Members Dr. Wilson Udo Udofia Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.
Dr. Ch. Ravi Kumar Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra Pradesh), India.
Dr. Sanjay Pande MB FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR) Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University Belgaum (Karnataka), India.
Dr. Hany Elazab Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.
Dr. M.Varatha Vijayan Principal, Department of Mechanical Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai (Tamil Nadu) India.
Dr. S. Balamurugan Director, Research and Development, Intelligent Research Consultancy Services (IRCS), Coimbatore (Tamil Nadu), India.
Dr. Rajalakshmi Rahul FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR) Founder and CEO Talaash Research Consultants, Chennai (Tamil Nadu), India.
Reviewer Chair Dr. Arun Murlidhar Ingle Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar (Maharashtra) India.
Members of Reviewer Chair Dr. S. A. Mohan Krishna Associate Professor, Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India
Dr. Ashok Koujalagi Assistant Professor & Postdoctoral Researcher, Department of Computer Science, Basaveshwar Science College, Bagalkot (Karnataka), India
Dr. A. Baradeswaran Principal, Department of Electronics & Communication Engineering, Madha Engineering College, Chennai (Tamil Nadu), India
Dr. R. Padma Priya Professor, Department of Electronics & Communication Engineering, Madha Engineering College, Chennai (Tamil Nadu), India
Dr. Raghunath Satpathy Assistant Professor, Department of Biotechnology, Majhighariani Institute of Technology and Science, Odisha, India
Dr. Hema Chandran K. Professor & HOD, Department of Electronics & Communication Engineering, Ashoka Institute of Engineering and Technology, Hyderabad (Telangana), India
Dr. M. Rakesh Assistant Professor, Department of Electronics and Communication Engineering, Vignan’s Institute of Management and Technology for Women, Ghatkeser (Hyderabad), India
Dr. Parul Mishra Assistant Professor, Department of English, GD Goenka University Gurugram, Gurgaon (Haryana), India
Dr. Sunil Kumar Mishra Associate Professor, Department of Communication Skills (English), Amity University, Gurgaon (Haryana), India
Dr. Kaushik Mukherjee Faculty of Management Studies, Assistant Professor, AKS University, Satna(MP), India
Dr. Siva Reddy Sheri Associate Professor, Department of Mathematics, School of Technology Hyderabad Campus, GITAM University, Visakhapatnam (Andhra Pradesh), India.
Dr. Nitasha Soni Assistant Professor, Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.
Dr. Pushpender Sarao Professor, Department of Computer Science & Engineering, Hyderabad Institute of Technology and Management, Hyderabad (Telangana), India.
Dr. K. Priya Professor & Head, Department of Commerce, Vivekanandha College of Arts & Sciences for Women (Autonomous, Elayampalayam, Namakkal (Tamil Nadu), India.
Dr. Sunil Tekale Professor, Dean Academics, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.
Dr. Brijesh Singh Senior Yoga Expert and Head, Department of Yoga, Samutakarsha Academy of Yoga, Music & Holistic Living, Prahladnagar, Ahmedabad (Gujarat), India.
Dr. Vinita Kumar Department of Physics, Dr. D. Ram D A V Public School, Danapur, Patna(Bihar), India
Dr. Rama Shanker Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea
Dr. Subhash Laxman Gadhave Professor, Department of Mechanical Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, (Rajasthan) India.
Dr. Raja Mohammad Latif Professor, Department of Mathematics and Natural Sciences, University of Alberta Edmonton, Canada.
Dr. S.N. Ramaswamy Professor, Department of Civil Engineering, Kalasalingam University, (Krishnankoil) India.
Volume-10 Issue-1, October 2020, ISSN: 2249-8958 (Online) S. No Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Page No.
Authors: Arabi Madharshan, Aravinth, Dheneshraajan, Gokul, P. Praveena
Paper Title: Hybrid Electric Charging Station using Raspberry Pi Abstract: Our world is running out of fossil fuel so people start to change themselves and started to use an electric vehicle. In electric vehicles the charging is a big deal, this project includes solar and wind energy charging mechanism to generate power for electric vehicle both day and night. And it contains Raspberry pi that is programmed to calculate the amount of power charged for an electric vehicle, then the user can know that the information via Blynk application. The power generated by solar panel setup is given to the battery via DC-DC converter because the power from solar panel setup is a variable DC, so that is converted into pure DC. And the power generated by wind generator setup is given to battery via AC – DC converter, the power from a wind generator is AC, so that is converted into DC. 1. Keywords: Battery, Charge Controller, Raspberry Pi, Solar Panel. 1-3 References: 1. B. Koushik, A. Safaee, P. Jain, and A. Bakhshai, “A bi-directional single-stage isolated ac-dc converter for EV charging and v2g,” in Electrical Power and Energy Conference (EPEC), 2015 IEEE. 2. P. Goli and W. Shireen, “PV Integrated Smart Charging of PHEVs Based on DC-Link Voltage Sensing,” IEEE Trans. Smart Grid, vol. 5, no. 3, May 2014. 3. D. P. Birnie, “Solar-to-vehicle (S2V) systems for powering commuters of the future,” J. Power Sources, vol. 186, Jan. 2009. 4. M. Yilmaz and P. T. Krein, “Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles,” IEEE Trans. Power Electron., vol. 28, May 2013. 5. G. R. Chandra Mouli, P. Bauer, and M. Zeman, “System design for a solar-powered electric vehicle charging station for workplaces,” Appl. Energy, vol. 168, pp. 434–443, Apr. 2016. 6. O. Hafez and K. Bhattacharya, “Optimal design of electric vehicle charging stations considering various energy resources,” Renewable Energy, vol. 107, pp. 576–589, 2017. Authors: Avula Rohitha, B.K. Hem Charan
Paper Title: Human Task Recognition using CNN Abstract: In this fast pacing world, computers are also getting better in terms of their performance and speed. It is capable of solving very complex problems like understanding an image, understanding videos and live capturing and processing of data. Due to advancement in technologies like computer vision, machine learning techniques, deep learning methods, artificial intelligence, etc., various models are being made so that prediction of outputs is made simpler and of high accuracy and precision. Our project model is built using a convolutional neural network (CNN). Our dataset consists of 599 videos in which 100 videos was assigned to each category of basic human actions like Running, Boxing, walking etc. In this project, we have used a set of labelled videos which was used to train our three models. The CNN is used as a base network in all the three models to process all the videos from the dataset, that is, to read all the frames and convert into heat maps. Out of our three models, the best model is used to give prediction for the actions performed in the video. The results show that with better algorithm techniques, the performance of the model is also improved.
Keywords: Computer Vision, Heat maps, Accuracy, Precision, Artificial Intelligence, Machine learning, Deep 2. learning, Performance, Convolutional Neural network. 4-8 References: 1. M. S. Ryoo, “Human activity prediction: Early recognition of ongoing activities from streaming videos,” in ICCV, 2011. 2. M. Ryoo and J. Aggarwal, “Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities,” in ICCV, 2009, pp. 1593–1600 3. S. Singh, S. A. Velastin, and H. Ragheb, “Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods,” in Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on. IEEE, 2010, pp. 48–55 4. S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2013. 5. J. Hou, X. Wu, J. Chen, J. Luo, and Y. Jia, “Unsupervised deep learning of mid-level video representation for action recognition,” in AAAI, 2018. 6. J. Sung, C. Ponce, B. Selman, and A. Saxena, “Human activity detection from rgbd images,” in AAAI workshop on Pattern, Activity and Intent Recognition, 2011. 7. J. L. Jingen Liu and M. Shah, “Recognizing realistic actions from videos “in the wild”,” in CVPR, 2009 8. I. C. Duta, B. Ionescu, K. Aizawa, and N. Sebe, “spatio-temporal vector of locally max pooled features for action recognition in videos,” in CVPR, 2017. 9. L. Wang, Y. Qiao, and X. Tang, “Action recognition with trajectorypooled deep-convolutional descriptors,” in CVPR, 2015 10. K. Jia and D.-Y. Yeung, “Human action recognition using local spatiotemporal discriminant embedding,” in CVPR, 2008. Authors: Kurian Thomas, Pranav E., Supriya M.H.
3. Paper Title: A Generalized Deep Learning Model for Denoising Image Datasets Abstract: Advance in technology world has lots of contributions from artificial intelligence which is a highly 9-14 growing area. The failure of traditional algorithms has led to the employment of deep learning algorithms in various fields like pattern recognition, recommendation systems and classification systems. Removal of noise from images can be done using traditional noise removal filters. These filters can either remove more noise that wanted or leave unwanted noise than what is needed in the data. Utilization of Convolutional neural networks designed based on the dataset requirements along with the noise removal filter can yield better results. In this work, evaluation of the performance of convolutional neural network (CNN) against existing image denoising algorithms has been successfully executed . The proposed model is a generalized CNN model which can recognize and classify any type of noisy image given. Two types of model were compared where one model 1 uses the Adam optimizer and model 2 uses the Stochastic Gradient Descent (SGD) optimizer. The image dataset used here is MNIST handwritten dataset, which is trained, tested and validated with both the models by adding three different types of noise viz, Poisson, Salt and Pepper as well as Gaussian Noise. More accuracy and better results were given by the model 2 which uses the SGD optimizer.
Keywords: Adam, Convolutional Neural Network, Classification, SGD.
References: 1. O. Sheremet, K. Sheremet, O. Sadovoi and Y. Sokhina, "Convolutional neural networks for image denoising in infocommunication systems," 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, 2018, pp. 429-432, doi: 10.1109/INFOCOMMST.2018.8632109. 2. Q. Xiang and X. Pang, "Improved denoising auto-encoders for image denoising," 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China, 2018, pp. 1-9, doi: 10.1109/CISP- BMEI.2018.8633143. 3. S. Suresh, H. T. P Mithun and M. H. Supriya, "Sign language recognition system using deep neural network," 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 614-618. doi: 10.1109/ICACCS.2019.8728411 4. Z. Liu, W. Q. Yan and M. L. Yang, "Image denoising based on a CNN model," 2018 4th International Conference on Control, Automation and `Robotics (ICCAR), Auckland, 2018, pp. 389-393, doi: 10.1109/ICCAR.2018.8384706. 5. E. Pranav, S. Kamal, C. Satheesh Chandran and M. H. Supriya, "Facial emotion recognition using deep convolutional neural network," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 317-320, doi: 10.1109/ICACCS48705.2020.9074302. 6. Ç. P. Dautov and M. S. Özerdem, "Wavelet transform and signal denoising using Wavelet method," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404418 Authors: Anitha Raghavendra, Mahesh K. Rao
Paper Title: A Computer Vision Based System for Classification of Chemically and Naturally Ripened Mangoes Abstract: Recently there was news indicating that mangoes might cause cancer. The news was based on the fact that mangoes were being artificially ripened using a chemical- calcium carbide and Ethrel, a well- known carcinogenic. The consumers hence have to be careful in buying the mangoes. In this study, we have proposed a model for classification of artificially and naturally ripened mangoes using k-NN and SVM classifiers. In order to improve the efficacy of the model, color space features such like RGB, HSV, L*a*b are extracted. Along with the color space features, 14 Haralick texture features are also extracted here. An mango is automatically segmented in an image using modified K-means clustering segmentation method. For the experimental study, mangoes of 2 varieties such as Badami and Raspuri have been taken. In each variety, three different classes of ripened mangoes are taken such as naturally and in chemical, two artificial ripening treatments were applied like calcium carbide and Ethrel solution. The obtained experimental result in terms of F-measure is ranging from 64% to 84% for two different varieties of mangoes using two different chemicals. Further this proposed model can be implemented for different variety of mangoes.
Keywords: Calcium carbide; Ethrel; SVM; KNN.
References: 4. 1. Anoopa Ravindran, Mrs. Anitha R, Ajith Ravindran, “A Review on Non-Destructive Techniques for Evaluating Quality of Fruits” International Journal of Engineering Research & Technology (IJERT), Sept-2015 ,Vol. 4 Issue 09. 2. Effect of ethrel spray on the ripening behaviour of mango (Mangifera indica L.) variety 'Dashehari'- Journal of Applied and Natural 15-19 Science 3. Live Chennai.com http://www.livechennai.com/healthnews.asp?newsid=10973 4. Mehnaz Mursalat, Asif Hasan Rony, AbulHasnat, Md. SazedurRahman, Md. Nazibul Islam, MohidusSamad Khan, “A Critical Analysis of Artificial Fruit Ripening: Scientific, Legislative and Socio-Economic Aspects”, Chemical Engineering &Science Magazine., Dec- 2013, Vol-4, Issue-1. 5. https://www.mid-day.com/articles/cac2-may-cause-cancer-blindness-seizures/15346168 6. NutritionalTalk https://nwg-works.blogspot.in/2013/04/how-to-identify-banana-ripened-using.html 7. Inter Institutional Inclusive Innovations Centre www.i4c.co.in/idea/getIdeaProfile/idea_id/2969 8. Md. Nazibul Islam , Mollik Yousuf Imtiaz , Sabrina Shawreen Alam , Farrhin Nowshad , Swarit Ahmed Shadman1 and Mohidus Samad Khan1” Artificial ripening on banana (Musa Spp.) samples: Analyzing ripening agents and change in nutritional parameters”, Cogent Food & Agriculture. 2018. 9. SergioCubero, Nuria Aleixos, Enrique Molto,Juan Gomes-Sanchis, Jose Blasco” Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables”, Fodd bioprocess Techno-l Springer 2011 10. Vani Ashok, Dr.D.S.Vinod “Using K-means cluster and fuzzy c means for defect segmentation in fruits” International journal of computer engineering & technology(IJCET-2014) 11. Ankur M Vyas, Bjial Talati, Sapan Naik. “Color feature extraction techniques of fruits: A survey” , International Journal of Computer Applications, December (IJCA-2013) 12. Sumithra R, MahamadSuhil, Dr.D.S.Guru. “ Segmentation and Classification of Skin Lesions for Disease Diagnosis” International Conference on Advanced Computing Technologies and Applications (ICACTA-2015) 13. Robert M. Haralick, K Shanmugam and Its’HakDinstein( 1979). “Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics vol. SMC-3, No. 6, November 1973, pp. 610-621. 14. D S Guru, Y.H.Sharath, S.Manjunath,” Texture Features and KNN in Classification of Flower Images”, IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition” 15. RTIPPR, 2010 16. Ms. Snehal S. Joshi, Mr. Navnath D. Kale,” “Survey: Support Vector Machine and Its Deviations in Classification Techniques” International Journal of Advanced Research in Computer Science and Software Engineering. December 2014, Vol-4, Issue-12, 17. Suchithra A. Khoje, S.K.Bodhe, “Application of color texture moments to detect external skin damages in Guavas”. World applied sciences Journal-Research gate-2013. 18. Richard O. Duda, Peter E. Hart., and David G.Stork. Pattern Classification, 2nd edition, Wiley-India edition, , 2007 19. Manali Kshirsagar, Parul Arora,” Classification Techniques for Computer Vision Based Fruit Quality Inspection: A Review”, International Journal of Recent Advances in Engineering & Technology(IJRAET-2014) 20. C.S. Nandi, Bipin Tudu, Chiranjib Koley, “A Machine Vision-Based Maturity Prediction System for sorting of Harvested Mangoes.IEEE Transactions.2014, Vol. 63, No. 7 Authors: Aldrin Jose, Andrew Franklin Raj A, Arunmozhi T M, Kuttiraj G, Dr. Karpagam J
Paper Title: Dual Source Self Displaying Water Pumps Abstract: Energy may be a key ingredient for the development of a nation. India is a country that is profusely endued with renewable energy sources. It is an outsized nation and the rate of electrification have not unsubdued speed with the increasing people, development and industrialisation has resulted in the increasing shortage between need and supply of electricity. Individuals who are not provided the facility grid need to be dependent on fossil fuels like diesel and petrol for his or her power wants and additionally incur significant revenant expenditure. We have taken initiative to design and implement a pump which will be operated on multiple energy sources. The pump is operated by taking power from the prevailing AC grid and facility taken from the standalone electrical photovoltaic system. The pump works on renewable solar power and whenever there is a shortage of solar power, it is switched to AC grid. Additionally, to the system, a self-display unit has been put in within the pump. This unit helps the buyer to observe the motor parameters like voltage, current and frequency 5. any time. This unit helps in reducing the value for putting in a separate meter close to the starter of the pump. This increases the compactness of the pump. 20-24
Keywords: AC grid, Photovoltaic system, Solar energy, Self- displaying unit, Water pump.
References: 1. Solar Water Pump. Int. Journal of Engineering Research and Application ISSN: 2248- 9622, Vol. 7, Issue 5, (Part -3) May 2017. 2. Eker. B and Akdogan A, Protection methods of corrosion on Solar Systems, TMMOB Machinery Engineering Society, Mersin, Turkey, 2015. 3. M. Dubey, S. Sharma, and R. Saxena, “Solar PV Standalone Water Pumping System Employing PMSM Drive,” Electrical Electronics and Computer Science (SCEECS), IEEE 2014. 4. Jose M Corberan, Antonio Cazorla- Marin, Javier Marchante- Avellaneda, Carla Montagud, International Journal of Low-Carbon Technologies, Volume 13, Issue 2, June 2018. 5. Luqman Maraaba, Zakariya Al-Hamouz and Mohammad Ahido,”An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors”, 2018. 6. Yugo Santos and Marta, “Comment on Draft Pumps for Power Papers”, October 7, 2013. Authors: Harmandeep Kour, Lal Chand
Paper Title: Healthy and Unhealthy Leaf Classification using Convolution Neural Network and Cslbp Features Abstract: Once applied to real world images, most machine learning models for the automated identification of diseases have limited efficiency. Plant diseases cause major agricultural production and economic loss. These illnesses also show visible signs, including lines, streaks and shift in color, on leaf surfaces. Many researchers have recently researched the potential use of image treatment and computer processing in plants and leaves to diagnose disease. There is space for improved performance though several methods and computer procedures have been developed in this area of investigation. Several previous models only deal with a few morphological features of the diseased regions. A new method for detecting plant leave's disease using the segmentation, and CNN approach based on GLCM and LPQ features of the Basil and Guava leaves feedback imagery has been established in the present paper. The findings revealed that the suggested model is as effective as possible, for both basil and guava leaves, to better distinguish healthy and unhealthy leaves. The overall accuracy of the Guava dataset is 97.1% and the basil dataset is 92.1%. 6. Keywords: Bilateral Filter, CNN, GLCM, Leaf Disease Classification, LPQ. 25-31 References: 1. A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology Available at: http://dx.doi.org/10.17632/hb74ynkjcn.4#folder-d2a758f3-cac5-4a2a-8c9f-90efbd0df308 2. A Gargade, A., & Khandekar, M. S. (2019). A Review: Custard Apple Leaf Parameter Analysis and Leaf Disease Detection using Digital Image Processing. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 267- 271). IEEE. 3. Izzo, A. A., Borrelli, F., Capasso, R., Di Marzo, V., & Mechoulam, R. (2009). Non-psychotropic plant cannabinoids: new therapeutic opportunities from an ancient herb. Trends in pharmacological sciences, 30(10), 515-527. 4. Bai, X., Li, X., Fu, Z., Lv, X., & Zhang, L. (2017). A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Computers and Electronics in Agriculture, 136, 157-165. 5. Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271) (pp. 839-846). IEEE. 6. Dhall, A., Asthana, A., Goecke, R., & Gedeon, T. (2011, March). Emotion recognition using PHOG and LPQ features. In Face and Gesture 2011 (pp. 878-883). IEEE. 7. Dhingra, G., Kumar, V., & Joshi, H. D. (2019). A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement, 135, 782-794. 8. Kambale, G., & Bilgi, D. N. (2017). A Survey Paper On Crop Disease Identification And Classification Using Pattern Recognition And Digital Image Processing Techniques. 9. Hu, G., Wu, H., Zhang, Y., & Wan, M. (2019). A low shot learning method for tea leaf’s disease identification. Computers and Electronics in Agriculture, 163, 104852. 10. Kumar, K. V., & Jayasankar, T. (2019). An identification of crop disease using image segmentation. Int. J. Pharm. Sci. Res, 10(3), 1054-1064. 11. Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., & Saba, T. (2018). CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Computers and electronics in agriculture, 155, 220-236. 12. Kumar, J. P., & Domnic, S. (2019). Image based leaf segmentation and counting in rosette plants. Information Processing In Agriculture, 6(2), 233-246. 13. Chahal, N. (2015). A study on agricultural image processing along with classification model. In 2015 IEEE International Advance Computing Conference (IACC) (pp. 942-947). IEEE. 14. Dhaygude, S. B., & Kumbhar, N. P. (2013). Agricultural plant leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(1), 599-602. 15. Nikbakhsh, N., Baleghi, Y., & Agahi, H. (2019). Maximum mutual information and Tsallis entropy for unsupervised segmentation of tree leaves in natural scenes. Computers and Electronics in Agriculture, 162, 440-449. 16. Anand, R., Veni, S., & Aravinth, J. (2016, April). An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In 2016 international conference on recent trends in information technology (ICRTIT) (pp. 1- 6). IEEE. 17. Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621. 18. Rico-Fernández, M. P., Rios-Cabrera, R., Castelán, M., Guerrero-Reyes, H. I., & Juarez-Maldonado, A. (2019). A contextualized approach for segmentation of foliage in different crop species. Computers and Electronics in Agriculture, 156, 378-386. 19. Khirade, S. D., & Patil, A. B. (2015). Plant disease detection using image processing. In 2015 International conference on computing communication control and automation (pp. 768-771). IEEE. 20. Smith, S. M., & Brady, J. M. (1997). SUSAN—a new approach to low level image processing. International journal of computer vision, 23(1), 45-78. 21. Raghavendra, B. K. (2019). Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 313-316). IEEE. 22. Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157, 270-280. 23. Singh, V. (2019). Sunflower leaf diseases detection using image segmentation based on particle swarm optimization. Artificial Intelligence in Agriculture, 3, 62-68. 24. Uliyan, D. M., Jalab, H. A., & Wahab, A. W. A. (2015). Copy move image forgery detection using Hessian and center symmetric local binary pattern. In 2015 IEEE Conference on Open Systems (ICOS) (pp. 7-11). IEEE. 25. Varshney, P., & Suresh, S. Integration of organic farming practices in cultivation of Ayurveda herbs: An innovative approach. 26. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture, 4(1), 41-49. 27. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175, 105456. 28. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. Authors: T.Samina, A.Bisharathu Beevi, S. RamaIyer Application of Dynamic Voltage Restorer for Enhancing low voltage Ride-through capability of Paper Title: Doubly Fed Induction Generator Abstract: Doubly Fed Induction Generator (DFIG) based wind Energy System are very sensitive to grid disturbance such as Symmetrical voltage sag. In this paper the authors propose a new method for application of Dynamic Voltage Restorer for enhancing the low voltage ride through capability of wind turbine driven Doubly Fed Induction Generator.
7. Keywords: Doubly Fed Induction Generator, Dynamic Voltage Restorer. Voltage sag, Rotor side controller, Grid side controller. 32-35 References: 1. Singh, Bhim, Shiv Kumar Aggarwal, and Tara Chandra Kandpal. "Performanceof Wind Energy Conversion System Using a Doubly Fed Induction Generator for Maximum Power Point Tracking", IEEE Industry Applications SocietyAnnual Meeting, vol. 139, no. 5, pp. 429-442, July 2010. 2. Bimal K Bose,”Modern Power electronics and ac drives”PHI Learning Private Limited. 3. Ned Mohan, Ted K. A. Brekken “Control of a Doubly Fed Induction Wind Generator Under Unbalanced Grid Voltage Conditions” IEEE Transaction Energy conversion, vol.no22. 1, March 2007 page 129-135 4. Heng Nian, Member, IEEE, and Yipeng Song,” Direct Power Control of Doubly Fed Induction Generator Under Distorted Grid Voltage” IEEE Transactions on Power Electronics, vol. 29, no. 2, February 2014 Authors: Abhishek Singh, Sarthak Bansal, Madhav Chaturvedi
Paper Title: Earthquake Analyzer using Prediction Commands Abstract: Destructive earthquakes usually causes gargantuan casualties. So, to cut back these inimical casualties’ analysis are made to reduce despicable and forlorn impacts which they left upon others to just ponder and become lugubrious. These factors measure the decisive casualties it brings and also earthquake and 8. therefore the development of rational prediction model to casualties become a crucial analysis topic, as a result of quality and cognitive content of gift prediction methodology of price, an additional correct prediction model 36-38 is mentioned by gray correlation theory and BP neural networks. The earthquake can be analyzed succinct by using various technique mainly predictive commands to marshal all the calculated time and magnitude of a potential earthquake have been the topic of the many studies varied ways are tried mistreatment several input variables like temperature exorable, seismic movements and particularly the variable climatic conditions. The relation between recorded seismal-acoustic information associate degreed occurring an abnormal seismic process (ASP). However, it's obstreperous to predict all parameters the placement, time and magnitude of the earthquake by mistreatment this information. This model description is different from others as with the help of the prediction commands most of the paragons and domains are identified and tend to explore the activity of serious Earthquakes. We use the preemptive data information which is collected around the planet. We retrieved the data to perceive that associate degree earthquake reaches the class of exceeds a grade range of eight on Richter Scale. The two main affected areas are in the field of Data Exploration and Data Mapping. Number of occurrences of an earthquake with different magnitude ranges, severity of an earthquake. Mapping is thereby crucial to identify highly affected areas based on Magnitude and Correlation between depth and magnitude. So, based on the above explorations we have made the following predictions. Predictions Magnitude based on depth. Magnitude based on Latitude and Longitude. Depth based on Latitude and Longitude The primitive algorithm used here are the Machine Learning Algorithm I.e. Linear Regression and K- Means Clustering. Firstly, we have made all the predictions via Linear Regression and made different clusters of the Earthquakes which belong to the same subdivision as that of Magnitude or Depth
Keywords: Data Exploration and Data Mapping.
References: 1. T. A. Aliev, A. M. Abbasov, Q. A. Guluyev, F. H. Pashaev, U. E. Sattarova. "System of robust noise monitoring of anomalous seismic processes", Soil Dynamics and Earthquake Engineering, vol:53, pp 11–25, 2013. 2. T.A. Aliev, A.M. Abbasov, E.R. Aliev, G.A. Guluev, "Digital technology and systems for generating and analyzing information from deep strata of the Earth for the purpose of interference monitoring of the technical state of major structures", Automatic Control and Computer Sciences vol: 41, pp: 59–67, 2007. 3. T.A. Aliev, A. M. Alizade, G.D. Etirmishli, G. A. Guluev, F. G. Pashaev, A. G. Rzaev, "Intelligent seismoacoustic system for monitoring the beginning of the anomalous seismic process", Seismic Instruments vol: 47, pp: 27–41, 2011. Authors: Pooja Mahindrakar, Uma Pujeri Security Implications for Json web Token Used in MERN Stack for Developing E-Commerce Web Paper Title: Application Abstract: In almost every organization where user sensitive data is available, security and privacy of the data plays a vital role..As storage of these information is overhead in database, Tokens are generated which handles sessions and also self contains user details. One of such widely used stateless token is Json Web Token. This paper deals with the research that follows implementation of authentication and authorization technique using JSON web token which will make web service a role based one .In the project under taken, Json web token is generated in a more secured way by choosing the secret key for web token wisely. Usually key for the token was a mere string or the set of keys stored in a key ring in the database and used alternately for the users to create the token. Or one more trial model is created where captcha was used in short a random number was generated and used as secret key for token generation but the main issue was increased storage. Thus storage is tried to reduce also less predictive secret key is generated in this project.
9. Keywords: token, authentication, JWT , security, privacy, sessions, encryption.
References: 39-45 1. Muhamad Haekal,Eliyane ,”Token based authentication using Json webtoken on SIKASIR RESTful web service”. International Conference on Informatics and Computing (ICIC) IEEE(2016) 2. Yjvesa Balaj,”A Survey: Token-Based vs Session-Based Authentication ” Article September 2017 3. Hardt, D.: “The OAuth 2.0 Authorization Framework.” RFC 6749, RFC Editor, October 2012 4. Yung Shulin,Wang Shaopeng,Hu Jeiping,Cai Hungwai, ”Implementation on Permission Management Framework based on token through Shiro” 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC) 5. Ch.Jhansi Rani ,SK.Shammi Munnisa ”A Survey on Web Authentication Methods for Web Applications”(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 7 (4) , 2016 6. Xiang-Wen Huang, Chin-Yun Hsieh, Cheng Hao Wu and Yu Chin Cheng, ”A Token-Based User Authentication Mechanism for Data Exchange in RESTful API” , vol. 00, no. , pp. 601-606, 2015, doi:10.1109/NBiS.2015.89 7. Brachmann E., DittmannG., Schubert KD. (2012) “Simplified Authentication and Authorization for RESTful Services” in G. (eds) ServiceOriented and Cloud Computing. ESOCC 2012. Lecture Notes in Computer Science, vol 7592. Springer, Berlin, Heidelberg. 8. Obinna Ethelbert,Faraz Fatemi Moghaddam, Philipp Wieder, Ramin Yahyapour,”A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Application” 2017 IEEE 5th International Conference on Future Internet of Things and Cloud 9. Jones, M.B., Hardt, D.:”The OAuth 2.0 Authorization ” October 2012 10. Jit dhulam,”Json Web Token In Django REST API” (article) 2018 Authors: Nainika Kaushik, Manjot Kaur Bhatia, Sonali Rastogi
Paper Title: SVM and Cross-validation using RStudio 10. Abstract: Each passing day data is getting multiplied. It is difficult to extract useful information from such big data. Data Mining is used to extract useful information. Data mining is used in majorly all fields like healthcare, 46-54 marketing, social media platforms and so on. In this paper, data is loaded and preprocessed by dealing with some missing values. The dataset used is of Airbnb, the platform used for lodging and tourism industry.Analyzing the data by plotting correlation using spearman method. Further, applying PCA and Support Vector Machine classification technique on the dataset. There are various applications of SVM, it is used in face-detection, text and hypertext categorization, classification of images, bioinformatics and so on. SVM has high dimensional input space, sparse document vectors and regularization parameters therefore it is appropriate to use SVM. Cross-validation gives more accurate result. The dataset is divided into folds. The end product is the test set which is similar to full dataset. Confusion matrix is evaluated, grid approach is followed for building the matrix at various seeds and kernels (RBF, Polynomial). The aim of this research is to see which is the best kernel for the dataset.
Keywords: Big Data, Data mining, Machine learning Rattle, RStudio, Support Vector Machine.
References: 1. Mayor, Shweta & Pant, Bhasker. (2012). Document Classification Using Support Vector Machine. International Journal of Engineering Science and Technology. 2. Duan, Kai-Bo; Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study" (PDF). Multiple Classifier Systems. LNCS. 3541.pp. 278-285. CiteSeerX 10.1.1.110.6789. doi:10.1007/11494683_28. ISBN 978-3-540-26306-7. 3. B. Choi, B. Chung and J. Ryou, "Adult Image Detection Using Bayesian Decision Rule Weighted by SVM Probability," 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, 2009, pp. 659-662. 4. Vani Kapoor Nijhawan, Mamta Madan, Meenu Dave (2009). A Comparative Analysis Using RStudio for Churn Prediction .International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075,Volume-8 Issue-7S2, May 2019 5. Pawar A., Jape V.S., Mathew S. (2019) Wind Power Forecasting Using Support Vector Machine Model in RStudio. In: Mallick P., Balas V., Bhoi A., Zobaa A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore 6. Joshi S. (2019) Sentiment Analysis on WhatsApp Group Chat Using R. In: Shukla R., Agrawal J., Sharma S., Singh Tomer G. (eds) Data, Engineering and Applications. Springer, Singapore 7. Qian C., Li Y., Zuo W., Wang Y. (2020) Analysis of Driving Safety and Cellphone Use Based on Social Media. In: Stanton N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham 8. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. (2017). URL https://www.R-project.org/ 9. Zhao, Y.: R Reference Card for Data Mining. http://www.Rdatamining.com Online. Access 10 June 2017 10. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Cambridge (2016) 11. Patil, S.: WhatsApp group data analysis with R. Int. J. Comput. Appl. 154(4) (2016) 12. Torgo, L.: Data Mining with R Learning with Case Studies. CRC Press, Taylor & Francis Group an Informa Business (2011) Authors: Desh Deepk Sharma, Atul Sarolwal
Paper Title: Hierarchical Structure of Active Distribution Network in Power System Abstract: The active distribution network (ADN) is an integral component of the smart grid. The ADN improves reliability and resiliency in the power grid integrated with many distributed energy resources (DERs). This is possible that, during outage, the ADN can be isolated from the main grid and it can continue to operate in island mode with indeterminate broken links and scarce generation resources. With the active management of increasing DERs, the distribution network is changed to active distribution network from passive network. This paper reviews the characteristics and challenges of deployment of distributed power plants (DPPs) in hierarchical active distribution network
Keywords: Active distribution network, Distributed Power Plants, Distributed Energy Resources, Hierarchical Control, Competitive Control
References: 1. J. Svensson, "Active Distributed Power Systems Functional Structures for Real-Time Operation of Sustainable Energy Systems," Department of Industrial Electrical Engineering and Automation, Lund Institute of Technology., Lund, 2006. 2. T. Sansawat, J. O'donnel, L. F. Ochoa and G. P. Harrison, "Decentralized voltage control for active distribution networks," in 2009 11. 44th International Universities Power Engineering Conference (UPEC), Glasgow, UK, 2009. 3. L. Wu, L. Jiang, X. Hao and T. Zheng, "Research on distributed cooperartive optimization control strategy for active distribution network based on combine-then-adapt diffusion algorithm," The Journal of Engineering, vol. 2019, no. 16, pp. 1911-1917, 2018. 4. P. Li, H. Ji, C. Wang, J. Zhao, G. Song, F. Ding and J. Wu, "Coordinated control method of voltage and reactive power for active 55-61 distribution networks based on soft open point," IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1430-1442, 2017. 5. J. Zhou, D. Liu, B. Shen, J. Liu and C. Fang, "Active distribution network layered and distributed control strategy and implementation," in 2014 China International Conference on Electricity Distribution (CICED 2014), Shenzhen , 2014. 6. Y. Liu, H. Xin, Z. Qu and D. Gan, "An attack-resilient cooperative control strategy of multiple distributed generators in distribution networks," IEEE Transaction on Smart Grid, vol. 7, no. 6, pp. 2923-2932, 2016. 7. S. H. Bidgoli and T. V. Cutsem, "Combined local and centralized voltage control in active distribution networks," IEEE Transactions on power systems , vol. 33, no. 2, pp. 1374-1384, 2018. 8. P. Li, H. Ji, C. Wang, J. Zhao, G. Song, F. Ding and J. Wu, "Coordinated control method of voltage and reactive power for active distribution networks based on soft open point," IEEE Transactions on Sustainable Energy , vol. 8, no. 4, pp. 1430-1441, 2017. 9. M. Bahramipanah, D. Torregrossa, R. Cherkaoui and M. Paolone, "A decentralized adaptive model-based real-time control for active distribution networks using battery energy storage systems," IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3406-3418, 2018. 10. D. Zarrilli, A. Giannitrapani, S. Paoletti and A. Vicino, "Energy storage operation for voltage control in distribution networks : A receding horizon approach," IEEE Transactions on Control Systems Technology, vol. 26, no. 2, pp. 599-609, 2018. 11. N. C. Koutsoukis and P. S. H. N. D. Georgilakis, "Multistage coordinated planning of active distribution networks," IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 32-44, 2018. 12. A. M. Cantarellas, D. Remon, J. M. Garcia and P. Rodriguez, "Competitive control of wave power plants though price-signal optimum allocation of available resources," in IEEE Energy Conversion Congress and Exposition (ECCE), Cincinati, OH, USA , 2017. 13. R. H.A. Zubo, G. Mokryani, “Active distribution network operation: A market based approach” IEEE Systems Journal, vol. 1, no. 1, pp. 405-1416, 2020. 14. S. Hu, Y. Xiang, J. Liu, C. Gu, X. Zhang, Y. Tian, Z. Liu, J. Xiong, “ Agent-based coordinated operation strategy for active distribution network with distributed energy resources” IEEE Transactions on Industry Application, vol.55, no. 1, pp.3310-3320, 2019. 15. L. Zhao, Y. Huang, Q. Dai, L. Yang, F. Chen, L. Wang, K. Sun, J. Huang, A. Z. Lin “Multistage active distribution network planning with restricted operation scenario selection” IEEE Access, vol. 7, pp. 121067-121080, 2019. 16. C. Li, S. Miao, Y. Li, D. Zhang, C. Ye, Z. Liu, L Li, “Coordinating dynamic network reconfiguration with ANM in active distribution network optimization considering system structure security evaluation” IET Generation, Transmission & Distribution, vol. 13, no. 19, pp. 4355-4363, 2019. 17. L. Wu, L. Jaing, X. Hao and T. Zheng, "Research on distributed cooperative optimization control strategy for active distribution network based on combine-then-adapt diffusion algorithm," Journal of Engineering , vol. 2019, no. 16, pp. 1911-1917, 2019. 18. Y. Dexiang and L. Dong, "Application of model predictive control in active distribution network," in China International Conference on Electricity Distribution (CICED 2014) , Shenzhen, 2014. 19. X. Xing, J. Lin, C. Wan and Y. Song, "Model predictive control of LPC-looped active distribution network with high penetration of distributed generation," IEEE Transactions on Sustainable Energy , vol. 8, no. 3, pp. 1051-1063, 2017. 20. P. Li, H. Ji, C. Wang, J. Zhao, C. Wang, J. Zhao, G. Song, J. Wu, “Coordinated control method of voltage and reactive power for active distribution networks based on soft open point” IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1430-1442, 2017. 21. M. Bahramipanah, D. Torregrosa, R. Cherkaoui, M. Paolone, “A decentralized adaptive model- based real- time control for active distribution network using battery energy storage systems, IEEE Transactions on Smart Grid, vol. 9,no. 4, pp. 3406- 3418, July 2018. 22. A.S. Bouhouras, C. Iraklis, G. Evmiridis, and D. P. Labridis, “Mitigating distribution network congestion due to high DG penetration,” in Proceedings 9th Mediterrr exhibition Power Generation Transmission Distribution Energy Converters (Med Power), Athens, Greece, Nov, 2014. 23. N. C. Koutsoukis, P. S. Georgilakis, N. D. Hatziargyriou, “Multistage coordinated planning of active distribution network,” vol. 33, no. 1, pp. 32-44, 2018. 24. Y. Jiang , C. Wan , J. Wang , Y. Song, and Z. Y. Dong, “Stochastic receding horizon control of active distribution networks with distributed renewables” vol. 34, no. 2, pp. 1325-1341, 2019. 25. L. Wei, L. Dong, and D. Hui, ‘Research of area coordinate control based on cyber-physical fusion modeling in active distribution network’ in 2016 China International Conference on Electricity Distribution (CICED 2016), Xi'an. Authors: Suspended 12. Paper Title: 62-65 Authors: Sebghatullah Karimi, Zabihullah Zhakfar, Mohammad Ismail Sarwary Study of Excessive Bureaucracy in Construction Projects – Causes of Low Level of Competition and Paper Title: Lengthy Tendering Process: A Case Study of Afghanistan Abstract: Excessive bureaucracy has been one of the most challenging issue for infrastructure sector in many countries. Countries are different in terms of their institutional settings, organizational cultures and political balance, and therefore, Afghanistan infrastructure/construction sector is no exception. This phenomenon has negatively impacted the delivery of infrastructure projects and hindering the country to reach its strategic economic goals. There are national projects that have been delayed for several years and the average tendering duration, based on existing researches, is almost 3 times more than the normal practices. This research is aimed to identify major causes of excessive bureaucracy in infrastructure sector that influence the level of competition and tendering duration, and provide technical recommendations for improvements. To do so, 17 factors causing low level of competition and lengthy tendering process have been identified through literature review and interviews. The factors are categorized under two groups; causes of; 1) low level of competition and, 2) lengthy tendering process. A questionnaire was developed and distributed to 80 construction firms. As a result, a response rate of 40% was achieved. Relative importance index (RII) is used to analyze the survey result. The research findings indicate that the top 5 factors causing excessive bureaucracy in delivering infrastructure/construction projects in Afghanistan and causing low level of competition and lengthy tendering process are: 1) Using traditional methods of procurement instead of electronic system, 2) Lack of accountability by procuring entities, 3) Delay in payments to companies, 4) Slow decision – making by procuring entities and 5) Corruption during the project lifecycle (inception to completion). The outcome of this research will help the 13. government to take necessary actions for eliminating unnecessary steps in the procurement of public infrastructure projects and ultimately improve project delivery. In addition, the research findings will help the construction companies to be fully aware of bureaucracy risks in the procurement process and develop necessary 66-73 risk mitigation plan for the successful completion of construction projects.
Keywords: Causes of bureaucracy, Infrastructure projects, Level of competition, Lengthy tendering process
References: 1. The Economist Intelligence Unit, The Critical Role of Infrastructure for the Sustainable Development Goals, London, UK, 2019. May 01, 2020. [Online]. Available: https://www.unops.org/news-and-stories/publications/the-critical-role-of-infrastructure-for-the-sdgs).\ 2. Ministry of Finance, National Infrastructure Plan 2016-21. May 01, 2020. [Online]. Available: http://policymof.gov.af/home/wp- content/uploads/2019/01/Natioal-Infrastructure-NPP.pdf 3. CoST – the Infrastructure Transparency Initiative, The Second Assurance Report: Transparency and Accountability in Public Infrastructure Projects, Kabul, Afghanistan, 2019. May 01, 2020. [Online]. Available: www.cost.af 4. Special Inspector General for Afghanistan Reconstruction, Quarterly Report, p.43, SIGAR, Kabul, Afghanistan, 2019. May 01, 2020. Available: https://www.sigar.mil/pdf/quarterlyreports/2019-04-30qr.pdf 5. L. V. MISES, “Introduction,” in BUREAUCRACY, USA.: YALE UNIVERSITY PRESS, 1944. 6. M. j. Hashi, The Effect of Bureaucracy on Public Service Delivery in Somalia: Case Study Banadir Region, P.A Dissertation, Dept. of Pub. Adm, MOGADISHU Univ., Somalia, 2015. 7. Prof. P. GRIGORIOU, Bureaucracy: administrative structure and set of regulations in place to control organizational or governmental activities, University of the Aegean. 8. G. Regonini, “Administrative Simplification Between Utopia and Nightmare,” in Utopian Discourses Across Cultures: Scenarios in Effective Communication to Citizens and Corporations, M. Bait, M. Brambilla, V. Crestani, Ed., Frankfurt am Main: Peter Lang AG, 2016, pp. 105-124. [Online]. Accessed May 01, 2020. Available: http://www.jstor.org/stable/j.ctv2t4bv7.10 9. R. Tamrakar, Impact of Citizen Charter in Service Delivery: A Case of District Administration Office, Kathmandu, M.S. Thesis, Dept. of General & Continuing Edu, North South University, Bangladesh, 2010. 10. S. Z. S. Abdalmenem, The Impact of Bureaucracy on Public Servant’s Perspective: Case Study on Land Authority in Gaza, M.S. Thesis, Dept. B. A, Islamic Univ. in Gaza, Gaza, Palestine, 2014. 11. L. G. FRANCU, the effects of bureaucracy over the business environment from Romania, Theoretical and Applied Economics, Volume XXI, No. 2(591), pp. 115-125, 2014. 12. F. Decarolis, L. M. Giuffrida, E. Lossa, V. Mollisi, G. Spagnolo, Bureaucratic Competence and Procurement Outcomes, Aug 13, 2018. 13. B. John, O. Oyeyipo, & O. M. Ajayi, Risk involved in Design and Build Procurement in Nigeria, Proc. of RICS Construction & Property Conf., Manchester, England, 2011, pp. 957-68. 14. Olusola F., & A. Oluwaseyi A., An Appraisal of Risks Associated with Contractor’s Cash Flow and their Impact on Project Delivery in Nigeria, Proc. of RICS Construction & Property Conf., Manchester, England, 2011, pp. 1601-1611. 15. M. M. Archer, Prof. JJP. Verster, Ethics, Leadership and Education, RICS Construction and Property Conference, Proc. of RICS Construction & Property Conf., Manchester, England, 2011, pp. 708-714. 16. B. Julius, State, Bureaucracy and Government: Uganda’s Opportunities, Challenges and Possible Solutions, Candidate, M.A. Democratic Governance and Civil Society, Univ. of Osnabrueck, Germany, 2013. 17. H. Silaban, Bureaucratic Reform in Indonesia: Lesson Learned from Bureaucratic Model in Japan, Int. J. Adv. Res, 5(1), 2096-2105, Hang Lekir I No.8, Senayan, Jakarta 10270, Indonesia, 2016. 18. K. Afridi, “Significant factors of Delay in Construction Projects in Afghanistan,” M.S. Thesis, Faculty of Economy, Yamaguchi Univ., Yamaguchi, Japan, 2016. 19. L.H. Long, Y.D. Lee & J. Y. Lee, Delay and Cost Overruns in Vietnam Large Construction Projects: A Comparison with Other Selected Countries. J. of Civil Eng., 12(6), 367-377, 2008. 20. S. Durdyev1, M. Omarov, & S. Ismail, Causes of delay in residential construction projects in Cambodia, Cogent. Eng., 4: 1291117, 2017. 21. P. McIntyre, Integrity in infrastructure and public procurement: Protecting the public interest, Water Integrity Brief, June 2016. 22. The Organization for Economic Cooperation & Development, Recommendation of the Council on Public Procurement, Paris, 2015. A.K. Arun Raja, K. Arun Vasantha Geethan, P. Sabarish Kumar, A. Shagul Hameed, Authors: S.Vivekanandan Paper Title: Analogy of Isophthalic Polyester Based Bamboo Fabric Mat and E-glass Reinforced Composite Abstract: Bamboo (Bambusoideae) normally found abundant in South Asian region is known to have a better fabric property which is now researched as a natural alternative in various applications and field. Various research are being carried out to find a suitable replacement for the non-bio degradable plastic reinforced composites, which has a negative impact on the Environment. A similar attempt is made to present an overview of recent research efforts addressing the properties of isophthalic polyester based Bamboo fabric mat as a replacement for E-glass fabric. The Bamboo fabric is made water repellent and the properties are promoted through mercerization process. The mercerization process is done using 8% of sodium in water to form the sodium hydroxide (NaOH). The property of the fabric is further promoted by adding charcoal, during the hand moulding process. The composite material is developed with the help of isophthalic polyester resin with 2% of accelerator and hardener used along with it. Experiments are carried out as per ASTM standards to find the mechanical properties namely, tensile strength and modulus, flexural strength and modulus, and impact strength. In addition to mechanical properties, water absorption capacity and the rate of burning of the composites is also studied. Further, fractured surface of the specimen is subjected to morphological study using scanning electron microscope. With help of the research and study, it can be found whether the isophthalic resin-based bamboo fabric mat can be used as an alternate for E-Glass fabric composite in various applications such as automobile 14. and structural applications.
Keywords: Bambusoideae, E-Glass, Isophthalic, Mercerization, Reinforcement. 74-80
References: 1. A.K. Mohanty, A. Wibowo, M. Misra, L.T. Drzal, “Effect of process engineering on the performance of natural fibre reinforced cellulose acetate bio composites”. 2. Ir. Eduardo Trujillo, Ir. Lina Osorio, Dr. Aart Van Vuure, Prof. Jan Ivens, Prof. Ignaas Verpoest “Bamboo (Guadua angustifolia) fibres for STRONG-light composite materials”. 3. Kazuya Okubo, Toru Fujii, Yuzo Yamamoto “Development of Bamboo-based polymer composites and their mechanical properties”. 4. Moe Yuzu Thwea Kin Liaob “Durability of Bamboo-glass fibre reinforced polymer matrix hybrid composites”. 5. Mohd Yussni Hashim, Mohd Nazrul Roslan, Azriszul Mohd Amin, Ahmad Mujahid Ahmad Zaidi, Saparudin Ariffin “Mercerization Treatment Parameter Effect on Natural Fiber Reinforced Polymer Matrix Composite: A Brief Review”. 6. Anuj Kumar A, Arun Gupta, K.V. Sharma, Mohammed Nasir, Tanveer Ahamed Khan “Influence of activated charcoal as filler on the properties of wood composites”. 7. Toshihiko HOJO, Zhilan XU, Yuqiu YANG, Hiroyuki HAMADA “Tensile Properties of Bamboo, Jute and Kenaf Mat-Reinforced Composite”. 8. C. Elanchezhian, B. Vijaya Ramnath, G. Ramakrishnan, M. Rajendrakumar, V. Naveenkumar, M. K. Saravanakumar. “Review on mechanical properties of natural fiber composites”. 9. Sahas Bansal, M. Ramachandran “Comparative Analysis of Bamboo using Jute and Coir Fiber Reinforced Polymeric Composites”. 10. Lawrence C Banka, T. Russell, Gentry Benjamin, P. Thompson Jeffrey, S Russell “A model specification for FRP composites for civil engineering structures”. 11. Pei Beia, Chen Liweia, Lu Changa “An Experimental Study on the Burning Behavior of Fabric used Indoor”. 12. Khan Z, Yousif BF, Islam MM “Fracture behavior of Bamboo fiber reinforced epoxy composites, Composites”. Authors: Oluwasegun J. Aroba, Nalindren Naicker, Timothy T. Adeliyi, Ropo E. Ogunsakin Meta-Analysis of Heuristic Approaches for Optimizing Node Localization and Energy Efficiency in Paper Title: Wireless Sensor Networks 15. Abstract: Background: In the literature node localization and energy efficiency are intrinsic problems often experienced in wireless sensor networks (WSNs). Consequently, various heuristic approaches have been proposed to allay the challenges faced by WSNs. However, there is little to nothing in the literature to support 81-88 which of the heuristic approaches is best in optimizing node localization and energy efficiency problems in WSN. The aim of this paper is to assess the best heuristic approach to date on resolving the node localization and energy efficiency in WSNs. Method: The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar and Web of Science. All statistical analysis was performed with the fixed-effects model and the random-effects model implementation in RStudio. The overall pooled global estimate and categorization of performance for the heuristic approaches were presented in forest plots. Results: A total of 18 studies were included in this meta-analysis and the overall pooled estimated categorization of the heuristic approaches was 35% (95% CI (13%, 67%)). According to subgroup analysis the pooled estimation of heuristic approach with hyper-heuristic was 71% (95% CI: 6% to 99%), I2 = 100%) while the hybrid heuristic, was 31% (95% CI: 3% to 87%, I2 = 100%) and metaheuristic was 21%(95% CI: 9% to 41%, I2 = 100%). Conclusion: It can be concluded based on the experimental results that hyper- heuristic approach outclassed the hybrid heuristic and metaheuristic approaches in optimizing node localization and energy efficiency in WSNs.
Keywords: Hyper-Heuristic, Hybrid Heuristic, Metaheuristic, Node Localization, Wireless Sensor Network
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Paper Title: Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy Abstract:Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG 16. waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, 89-93 minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage
Keywords: Epilepsy, EEG, Decision Tree model, Logistic regression, seizures
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Paper Title: Machine Learning Based Prediction of Suicide Probability Abstract: Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using 17. different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) model for prediction of suicides, involving classification of the data into separate categories. The proposed model is contrasted against similar computationally-inexpensive techniques 94-97 such as spline regression. The model is found to generate appreciably accurate results for the dataset considered in this work. The application of Bayesian estimation allows the prediction of causation to a greater degree than the standard spline regression models, which is reflected by the comparatively low root mean square error (RMSE) for all estimates obtained by the proposed model.
Keywords: Bayesian model, classification, machine learning, spline regression, suicide prediction.
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Paper Title: Haptic Structuring Assistive Innovation for Individuals Who Are Visually Impaired Abstract: Powerful correspondence for the visually impaired can be upgraded by haptic interfaces with the world around individuals living with visual deficiency. This writing audit will endeavor to respond to extreme inquiries concerning where haptic innovation is going throughout the following decade predicted by an overview of bleeding edge haptic innovation blended in with a discourse of the upsides and downsides of deliberately chose high effect research articles at last, hypothesizing the eventual fate of haptics through designing patterns and development of arrangement changes.
Keywords: haptic, technology, touch, visually impaired
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Web Page. 19. Chen, Y.-C., Chiang, C.-H., & Chiu, H.-C. (2010). The recognition of 3D basic patterns and tactile icons for the blind. Paper presented at the Society for Social Management Systems (SSMS) International Symposium. 20. Hubel, D. H. (1995). Eye, brain, and vision. New York, NY, US: Scientific American Library/Scientific American Books. 21. International, L. (2014). Visual Impairment Prevalence. 22. Jones, L. A., & Lederman, S. J. (2006). Human hand function: Oxford University Press. 23. Kaczmarek, K. A. (2000). Electrotactile adaptation on the abdomen: preliminary results. Rehabilitation Engineering, IEEE Transactions on, 8(4), 499-505. doi: 10.1109/86.895953 24. Kaczmarek, K. A., Webster, J. G., Bach-y-Rita, P., & Tompkins, W. J. (1991). Electrotactile and vibrotactile displays for sensory substitution systems. Biomedical Engineering, IEEE Transactions on, 38(1) 25. Kajimoto, H., Kawakami, N., Maeda, T., & Tachi, S. (2004). 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G. (2012). A Refreshable Braille Cell Based on Pneumatic Microbubble Actuators. Microelectromechanical Systems, Journal of, 21(4), 908-916. doi: 10.1109/JMEMS.2012.2190043 38. Xie, X., Zaitsev, Y., Vel?squez-Garc?a, L. F., Teller, S. J., & Livermore, C. (2014). Scalable, MEMS-enabled, vibrational tactile actuators for high resolution tactile displays. Journal of Micromechanics and Microengineering, 24(12), 125014. 39. Xu, C., Israr, A., Poupyrev, I., Bau, O., & Harrison, C. (2011). Tactile display for the visually impaired using TeslaTouch. Paper presented at the CHI '11 Extended Abstracts on Human Factors in Computing Systems, Vancouver, BC, Canada. 40. Yu, W., Guffie, K., & Brewster, S. (2001). Image to haptic data conversion: A first step to improving blind people’s accessibility to printed graphs. Paper presented at the Proceedings of Eurohaptics. 41. Yu, W., Ramloll, R., & Brewster, S. (2001). Haptic graphs for blind computer users. In S. Brewster & R. Murray-Smith (Eds.), Haptic Human-Computer Interaction (Vol. 2058, pp. 41-51): Springer Berlin Heidelberg. Authors: Sairaju Rakesh, B.SankerRam Design, Modeling and Simulation DC/DC Converters with PV Cell Fed Switched Reluctance Motor Paper Title: for Agriculture Field Abstract: Latest advances in the field of renewable energy sources and the power electronic circuits have seen their applicability in many fields. One such application is water pumping system in the field of agriculture. The use of PV cells fed by SRM(Switched Reluctance Motor) has created research interest as they have high conversion efficiency at low voltage and medium voltage levels respectively. To combine their features together DC/DC converters are incorporated. This paper gives the design, modeling and simulation DC/DC converters with PV cell fed switched reluctance motor for agriculture field. This is specifically applied for the water pumping motor. The simulation is done MATLAB- Simulink Environment. Various DC/DC converter are simulated and their effect with respect to various parameters such as number of switching devices, smooth and reliable operation is studied. 19. Keywords: SRM(Switched Reluctance Motor), DC-DC Converter, zeta converter 103-107
References: 1. Technical Note No. 28, Natural Resources Conservation Service, October 2010 released by United States Department of Agriculture. 2. S.S. Chandela,, M. Nagaraju Naika, Rahul Chandel “Review of solar photovoltaic water pumping system technology for irrigation and community drinking water supplies”, Elsevier journal on Renewable and Sustainable Energy Reviews 49, 1085- 1099, 2015 3. V. V. N. Murthy, S. S. Tulasiram, J. Amarnath “A New Converter Topology for Switched Reluctance Drive with Reduced Active Switching Devices”, International Journal of Recent Technology and Engineering (IJRTE) , ISSN: 2277-3878, Volume-3 Issue-3, July 2012. 4. Vijay Babu Koreboina, Narasimharaju B L, D M Vinod Kumar “Performance Evaluation of Switched Reluctance Motor PWM Control in PV-fed Water Pump System 5. ”, International Journal Of Renewable Energy Research Vol.6, No.3, 2016 6. Kiran R. Dhumal and S. S. Dhamse “A Solar Pv Array Powered Switched Reluctance Motor Drive For Water Pumping System”, International Journal of Electrical Engineering & Technology (IJEET) Volume 9, Issue 4, July- August 2018, pp. 94–10. 7. Xiaoshu Zan , Ning Wu ,et.al. “Design and Analysis of a Novel Converter Topology for Photovoltaic Pumps Based on Switched Reluctance Motor”, article on energies by MDPI Published: 1 July 2019 Authors: Edy Budiman Importance-weighted Ranking Methods for Preference the Covid-19 Pandemic Paper Title: Social Assistance Abstract: Issues importance-weighted value is a critical aspect of decision making. Differences in weight, even the slightest change in weight assignment, can drastically change the final decision. Moreover, in the case of distributing social assistance during the Covid-19 pandemic, objectivity and accuracy of weighting the criteria for potential recipients are very important applied for the welfare of the community. The proposes study 3 popular models of ranking methods for weighting criteria in the internet data package assistance cases. Weighting is given to 390 alternatives with 5 decision-making criteria based on online learning needs and economic cost capabilities. The decision analysis method uses the reference point and optimization from Moora. The study results were found accuracy, precision and error rate performance each method using a confusion matrix approach. The study results discussed raised several important points of findings, that the three ranking methods (RS, RR, ROD) have their respective characteristics in weighting importance, where the level of accuracy and precision of the rank-sum method is better than the RR and ROD methods (for the case: 5 criteria; 390 alternatives). Other things in giving weight value from important to most important are comparable, and the weight value of the non-benefit (cost) criteria in the ranking method have a significant effect on performance results. These three methods are simple in use and with the assessment of replacement weights that can be determined how important these variables are to the principal of these criteria.
Keywords: Weighting, Rank method, criterion, decision.
References: 1. G. O. Odu, “Weighting methods for multi-criteria decision making technique,” Journal of Applied Sciences and Environmental Management, 2019, doi: 10.4314/jasem.v23i8.7. 2. A. Toloie-Eshlaghy, M. Homayonfar, M. Aghaziarati, and P. Arbabiun, “A subjective weighting method based on group decision 20. making for ranking and measuring criteria values,” Australian Journal of Basic and Applied Sciences. 2011. 3. M. Danielson and L. Ekenberg, “Trade-offs for ordinal ranking methods in multi criteria decisions,” 2017, doi: 10.1007/978-3-319- 52624-9_2. 108-115 4. E. Triantaphyllou and A. Sánchez, “A sensitivity analysis approach for some deterministic multi-criteria decision-making methods,” Decision Sciences, 1997, doi: 10.1111/j.1540-5915.1997.tb01306.x. 5. M. Riabacke, M. Danielson, and L. Ekenberg, “State-of-the-art prescriptive criteria weight elicitation,” Advances in Decision Sciences. 2012, doi: 10.1155/2012/276584. 6. A. Molnar, B. Nemeth, A. Inotai, and Z. Kaló, “Comparison of Weighting Methods Used During The Construction of Multiple-Criteria Decision Analysis Tool for Repeated Use In Lower Income Countries,” Value in Health, 2017, doi: 10.1016/j.jval.2017.08.2240. 7. E. Budiman, N. Dengen, Haviluddin, and W. Indrawan, “Integrated multi criteria decision making for a destitute problem,” in 2017 3rd International Conference on Science in Information Technology (ICSITech), Oct. 2017, pp. 342–347, doi: 10.1109/ICSITech.2017.8257136. 8. J. M. Keisler, “The value of assessing weights in multi-criteria portfolio decision analysis,” Journal of Multi-Criteria Decision Analysis, 2008, doi: 10.1002/mcda.427. 9. N. H. Zardari, K. Ahmed, S. M. Shirazi, and Z. Bin Yusop, Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. 2014. 10. E. Budiman, “Decision Optimization : Internet Data Assistance for Students during Learning from Home,” no. 11, pp. 372–378, 2020, doi: 10.35940/ijitee.K7845.0991120. 11. M. B. H. Ibrahim, M. T. Jufri, S. N. Alam, Zakaria, M. A. Akbar, and E. Budiman, “Statistical Analysis of Performance Goals Effect to Lecturer Work Achievement in Higher Education,” 2018, doi: 10.1109/EIConCIT.2018.8878571. 12. W. K. Brauers and E. K. Zavadskas, “Robustness of the multi-objective moora method with a test for the facilities sector,” Technological and Economic Development of Economy, 2009, doi: 10.3846/1392-8619.2009.15.352-375. 13. W. K. M. Brauers and E. K. Zavadskas, “Robustness of MULTIMOORA: A method for multi-objective optimization,” Informatica, 2012, doi: 10.15388/informatica.2012.346. 14. W. K. M. Brauers and E. K. Zavadskas, “The MOORA method and its application to privatization in a transition economy,” Control and Cybernetics, 2006. 15. M. Wati, N. Novirasari, E. Budiman, and Haeruddin, “Multi-criteria decision-making for evaluation of student academic performance based on objective weights,” 2018, doi: 10.1109/IAC.2018.8780421. 16. E. Budiman, Haviluddin, N. Dengan, A. H. Kridalaksana, M. Wati, and Purnawansyah, “Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation,” in Lecture Notes in Electrical Engineering, 2018, pp. 380–389, doi: 10.1007/978-981-10-8276-4_36. 17. Peraturan Pemerintah RI, Pembatasan Sosial Berskala Besar dalam Rangka Percepatan Penanganan Corona Virus Disease 2019 (COVID-19). Republik Indonesia, 2020, p. PP Nomor 21 Tahun 2020. Authors: Vatsal Singh, Sanskar Joshi, Sahil Shaikh, Siddheshwar Wakude, Dilip Panchal
Paper Title: Designand Optimisation of a Slat Conveyor for Airport Application Abstract: This manuscript deals with the design, analysis and optimization of a Slat Conveyor for bag handling at the Airports. The requirement here is to transport the bags from loading station to the unloading station which covers the distance of 28 metres. The specification provided are the approximate weight of each bag, the total 21. weight to be transported between the stations and the height upto which it is transported. The Input parameters are reference to the design calculations.Proper material selection is done using appropriate standards like the ASME , CEMA and Ashby standard. With the proposed conveyor system the weight of the base frame will be 116-124 reduced and the fatigue strength/cycle of drive shaft will be increased using the appropriate materials.
Keywords: Factor of Safey, Load, Shaft, Slat Conveyor.
References: 1. Design and Analysis of a Conveyor. (ICIIIME 2017) ISSN : 2321-8169 2. Ketten Handbuch, Iwis- High performance chains. Sprocket and pinions for precision roller. 3. Chain simplex, recommended by NU-TECH, Page-11. 4. V.B. Bhandari “Design of Machine Elements” book. Authors: Fauziah Sulaiman, Salmia Santa, Elnetthra Folly Eldy The Optimum Distance of Lift-Off Height on Different Test Material’s Thickness by using Eddy Paper Title: Current Testing Technique Abstract: The Eddy current testing (ECT) technique is one of the non-destructive testing (NDT) techniques which is sensitive to the unintended signal such as lift-off (LO) height effect. The output voltage of signal defects with different thicknesses of test materials (i.e., Copper, Brass and Magnesium Alloy) can be determined from the optimum distance of LO height of the ECT technique. Previously, an established frequency was determined for these particular materials (i.e., Copper = (5.00-5.25) MHz, Brass = (4.75-5.25)MHz and Magnesium Alloy= (4.75-5.00)MHz). The frequency then generated the established voltage signal of the ECT technique. The acquired optimum distance of LO height for these materials is approximately 2mm. The findings from this established technique indicated that the determined optimum distance of LO height can find the output voltage signal of the defects as well as to detect the thicknesses.
Keywords: ECT Technique, Lift-off Optimum Distances, Non Destructive Test (NDT).
References: 1. Ricci, M., Silipigni, G., Ferrigno, L., Laracca, M. & Adewale, I.D. 2017. Evaluation of the lift-off robustness of eddy current imaging 22. techniques. Journal of NDT&E International 85:43-52. H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4. 2. Klein, G., Morelli, J., & Krause, T. W. (2018). Analytical model of the eddy current response of a drive-receive coil system inside two 125-128 concentric tubes. NDT and E International, 96(June 2017), 18–25. https://doi.org/10.1016/j.ndteint.2018.03.003. 3. Egorov, A., Polyakov, V., Salita, S., Kolubaev, A., Psakhie, G., Chernyavskii, G., & Vorobei, V. (2015). Inspection of aluminium alloys by a multi-frequency eddy current method. Defence Technology, 11(2), 99-103. https://doi.org/10.1016/j.dt.2014.12.002 4. Yuan, X., Li, W., Chen, G., Yin, X., Ge, J., Yang, W., ... Ma, W. (2018). Inner circumferential current field testing system with TMR sensor arrays for inner- wall cracks inspection in aluminum tubes. Measurement: Journal of the International Measurement Confederation, 122(March), 232–239. https://doi.org/10.1016/j.measurement.2018.03.035. 5. Huang, S., Zhao, W., Zhang, Y. & Wang, S. 2009. Study on the lift-off effect of EMAT. Journal of Sensors and Actuators A 153:218- 221. 6. Soni, A. K., Thirunavukkarasu, S., Sasi, B., Rao, B. P. C., & Jayakumar, T. (2015). Development of a high-sensitivity eddy current instrument for the detection of sub-surface defects in stainless steel plates. Insight: Non-Destructive Testing and Condition Monitoring, 57(9), 508–512. https://doi.org/10.1784/insi.2015.57.9.508. 7. Shull, P.J. (2002). Nondestructive evaluation: theory, techniques, and applications. CRC press. 8. Angani, C. S., Ramos, H. G., Ribeiro, A. L., Rocha, T. J., & Prashanth, B. (2015). Transient eddy current oscillations method for the inspection of thickness change in stainless steel. Sensors and Actuators, A: Physical, 233, 217–223. https://doi.org/10.1016/j.sna.2015.07.003. 9. Li, X., Yin, W., Liu, Z., Withers, P.J., & Peyton, A. J. (2008). Characterization of carbon fibre reinforced composite by means of non- destructive Eddy Current Testing and FEM modeling. 17 Th World Conference on Nondestructive Testing, (January), 3-9. 10. Ghanei, S., Kashefi, M., & Mazinani, M. (2013). Eddy current nondestructive evaluation of dual phase steel. Materials and Design, 50, 491–496. https://doi.org/10.1016/j.matdes.2013.03.040 Authors: A.K. Arun Raja, K. Arun Vasantha Geethan, G. Tamilarasan, S.J. Shanoffer, S. Rathish Development and Characterisation of Banana and E-Glass Fiber Reinforced With Isophthalic Resin Paper Title: Based Composites Abstract: Natural fibers can have different advantages over synthetic reinforcing fibers as they are renewable .Thus the natural fibers have been used to reinforce materials in many composites structures Among the various fibers banana fibers are used because of Its light weight properties and it is locally available in all over India and Tamilnadu. Banana fibers obtained from the stem of the plant and it is a lingo cellulosic under exploited bast fibers, where E-glass being a synthetic fiber so the properties of Banana fabric reinforced composite has been compared with E-glass fabric based composites. Here the banana fabric matte is being separately treated with the caustic soda (NAOH)solution in water by the process of mercerization , both the fabric matte reinforced in isophthalic resin and filler chalk powder by 2% weight added , in order to compare their properties under various experiments such as TENSILE , HARDNESS , IMPACT , SEM and the FIRE RETENTION TEST.
23. Keywords: Banana fiber, E-glass fiber, Isophthalic, Mercerization
References: 129-133 1. M. R. Sanjay, G. R. Arpitha, L. Laxmana Naik, K. Gopalakrishna, B. Yogesha, “Studies on Mechanical Properties of Banana/E-Glass Fabrics Reinforced Polyester Hybrid Composites”. 2. P. Divya Vani, P. Prasanna, “Experimental Investigation of Mechanical Properties of Banana and Glass Fiber Reinforced Epoxy Based Hybrid Composites with Filler Materials”. 3. H. Ku, H. Wang, N. Pattarachaiyakoop, M. Trada, “A review on the tensile properties of natural fiber reinforced polymer composites”. 4. Mohanty AK, Drazl LT, Misra M, “Engineered natural fiber reinforced polypropylene composites: influence of surface modifications and novel powder impregnation processing". J Adhes Sci Technol 2002; 16(8):999 – 1015. 5. Yegireddy Haribabu., , “Study of Mechanical Properties of Banana and E-Glass Fiber Composite”. 6. V.P. Arthanarieswaran, A. Kumaravel, M. Kathirselvam., “Evaluation of mechanical properties of banana and sisal fiber reinforced epoxy composites: Influence of glass fiber hybridization”. 7. Saurab Dhakala, Keerthi Gowda B Sb, “An Experimental Study on Mechanical properties of BananaPolyester Composite”. 8. SK Khasim Sharif , Shankarlinga B. Shikkeri , K. Rajanikanth , “Mechanical characterization of Jute/Banana/Epoxy reinforced laminateComposite”. 9. R.D. HEMANTH, M. SENTHIL KUMAR, AJITH GOPINATH & L. NATRAYAN, “Evaluation of mechanical properties of e-glass and coconut fiber reinforced with polyester and epoxy resin matrices”.. 10. Samrat Mukhopadhyay S, Raul Fangueiro R, Yusuf A,Senturk Ulku, “Banana fibers- variability and fracture behavior”. J Eng Fiber Fabric 2008; 3;1-7. 11. Sureshkumar Perumal Singaraj, Kavati Phene Aaron, Krishnaraj Kaliappa, Karthikeyan Kattaiya & Mohan Ranganathan, “ Investigations on Structural, Mechanical and Thermal Properties of Banana Fabrics for Use in Leather Goods Applications. A.K. Arun Raja, D. Santhosh, K. Arun Vasantha Geethan, M. Suidarshanan, M. Suriya Authors: Subramanian Advancement, Characterization and Analogy of Jute Fabric and E-Glass Fibre Reinforced with Paper Title: Isophthalic Resin Based Composite Abstract: In recent years, there has been a growing need for natural fibre reinforced composite materials especially materials with good mechanical properties in order to substitute glass fibre based composite products to showcase a better engineering class for structural applications. Here the properties of jute fabric reinforced composite has been compared and analysed with e-glass fibre matte reinforced composite. The mercerized jute and e- glass fibre mats are treated with isophthalic resin. Charcoal powder has been used as a filler material constituting two percentage of the whole weight of entire composite. Isophthalic polyester resins offer substantially higher strength, better flexibility and chemical resistance. The properties of the jute composite and e-glass composite are determined by a series of tests such as Tensile, Flexural, Impact, Scanning Electron Microscopy (SEM) and Rate of Burning tests. The newly obtained composites provide a better usage for applications that require a much better physical strength and mechanical properties.
Keywords: Jute, E-glass, Isophthalic, Reinforcement, composite, Mercerization. 24. References: 1. Soma Dalbehra, S.K. Acharya, "Study on mechanical properties of natural fiber reinforced woven jute-glass hybrid epoxy composites”. 134-139 2. B. Vijayaramnath, S. Junaid Kokan, R. Niranjan Raja, R. Sathyanarayanan, C. Elanchezhian, A. Rajendra Prasad, V.M. Manickavasagam, “Evaluation of mechanical properties of abaca–jute–glass fibre reinforced epoxy composite”. 3. Harpreet Singh, Jai Inder Preet Singh,Sehijpal Singh, Vikas Dhawan, Sunil Ku ar Tiwari, “A Brief Review of Jute Fibre and Its Composites”. 4. M. R. Sanjay, B. Yogesha, “Studies on Mechanical Properties of Jute/E-Glass Fiber Reinforced Epoxy Hybrid Composites”. 5. Satyendra Pratap Singh Yadav, Dr. A.S. Verma, “Fabrication of composite material using Jute fiber/Glass fiber”. 6. Harpreet Singh, Jai Inder Preet Singh, Sehijpal Singh, Vikas Dhawan, Sunil Kumar Tiwari, “A Brief Review of Jute Fibre and Its Composites”. 7. Sekhar Das, Amiya Kumar Singha, Atin Chaudhuri & Prasanta Kumar Ganguly, “Lengthwise Jute Fibre Properties Variation And Its Effect On Jute-Polyester Composite”. 8. Kanishka Jha, Bibhuti Bhusan Samantaray, Paresh Tamrakar, “A Study on Erosion and Mechanical Behavior of Jute/E-Glass Hybrid Composite”. 9. Elsayed A. Elbadry, Mohamed S. Aly-Hassan, Hiroyuki Hamada, “Mechanical Properties of Natural Jute Fabric/Jute Mat Fiber Reinforced Polymer Matrix Hybrid Composites”. 10. Gowda T.M, Naidu A.C.B. and Chhaya R, “Some Mechanical Properties of Untreated Jute Fabric Reinforced Polyester Composites”. 11. Hong C.K, Hwang I, Kim N, Park D.H, Hwang B.S and Nah C, “Mechanical Properties of Silanized Jute-Polypropylene Composites”. 12. Bledzki A.K and Gassan J, “Composites Reinforced With Cellulose Based Fibres”. Authors: Kehdinga George Fomunyam
Paper Title: Redefining Engineering Education in Africa through Delivering Total Engineering Abstract: Engineering education in sub-Saharan Africa has the potential to contribute to economic and social development of any country. But it has not been leveraged on appropriately to culminate in economic and social development in the countries in Africa. This implies that for Africa as a region to leverage fully on the potentials of engineering education to ensure economic and social development, it must be redefined through delivering total engineering. This study was a theoretical discourse on redefining engineering education in Africa through delivering total engineering and evidences from established literature were used in giving more credence to the work. Delivering Total engineering is a composite of three words which are delivering, total and engineering. This study conceptualized what delivering total engineering and it was defined as an educational perspective which showcases the relationship between learning and teaching which is crucial to innovation in the delivery of capable, competent and confident graduate which are the outcomes. Findings revealed that the three dimensions (delivering, total, engineering) are crucial in redefining engineering education in Africa and they were analyzed in support of this study. The study therefore recommends intensification of effort on research on delivering total 25. engineering as it has no theoretical basis. Pragmatism is also important to verify the veracity of the concept. 140-145 Keywords: redefine, engineering, engineering education, Africa, delivering total engineering, delivering, total, engineering.
References: 1. Center for Strategic and International Studies, The Seven Revolutions (see www.7revs.org) 2. Constable, G., Somerville, B. (2003) A Century of Innovation: Twenty Engineering Achievements That Transformed Our Lives, National Academy of Engineering. 3. Continental. (2006). In search of global engineering excellence: Educating the next generation of engineers for the global workplace. Hanover, Germany: Continental, AG 4. Danko, A. I. (2006). Entrepreneurship Education for Vocational and Technical Education students, second edition pp. 2-3. 5. GE, The Future of Work in Africa: Building Strong Workforces to Power Africa’s Growth, 2015. 6. Kehdinga George Fomunyam. (2020). Exploring Research and Contextual Relevance in Engineering Education: The Case of a University in South Africa. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7 7. Matthews, P., Ryan-Collins, L., Wells, J., Sillem, H. and Wright, H. (2012). Engineers for Africa: Identifyingengineering capacity needs in sub-Saharan Africa. Royal Academy of Engineering, Africa-UK Engineering for Development Partnership. 8. Maynard, A. D. (2015). Navigating the fourth industrial revolution. Nature Nanotechnology, 10(12), 1005-1006. PMid:26632281. http:// dx.doi.org/10.1038/nnano.2015.286 9. Merriam Webster dictionary. (2020). https://www.merriam-webster.com/dictionary/redefine 10. Mkele, Y. (2013). Engineering skills are in short supply. Times LIVE, www.timeslive.co.za. 11. National Academy of Science and Engineering – ACATECH. (2013). Recommendations for implementing the strategic initiative industrie 4.0. final report of the industrie 4.0 working group. Frankfurt: ACATECH. Report 12. R.I Whitefield, A.H.H.B Duffy,, H. Grierson. (2019). Delivering total engineering education. International conference on engineering and product design education. 12-13 September 2019. Department of design, manufacturing and engineering management, University of Strathclyde, United Kingdom. http://www.researchgate.net/publication/335797177_delivering _a_total_engineering_education 13. Royal Academy of Engineering. 2016. http://www.raeng.org.uk/publications/reports/assessing-the-economic-returns-ofengineering- rese 14. The World Bank: News. (2014, 30 June). Partnering to Build Engineering, Scientific and Technical Skills for Africa’s Socioeconomic Transformation. Retrieved on September 15, 2014, from http://www.worldbank.org/en/news/feature/2014/06/30/partnering-to-build- engineering-scientific-and-technical-skills-for-africassocioeconomic-transformation 15. Trencher G (2014) Beyond the Third Mission: Exploring the Emerging University Function of Co-creation for Sustainability. Science and Public Policy 41(2): 151-179. Authors: Kehdinga George Fomunyam Chaos Engineering (Principles of Chaos Engineering) As the Pathway to Excellence and Relevance Paper Title: in Engineering Education in Africa Abstract: A study on engineering in sub-Saharan Africa revealed that engineering is pivotal for economic and social development of any country. This is profound as it underscores the potentials embedded in engineering education for excellence and relevance in Africa. This has not been the case in Africa, as the region has not developed evenly with other countries from the Global South. Hence, the impetus for chaos engineering as a panacea to excellence and relevance in engineering education in Africa. Chaos engineering has been defined by various authors and one of the profound definitions is that chaos engineering is the discipline of experimenting on a distributed system with the intent to build confidence in the system`s capability to withstand turbulent conditions during production. This study therefore looked at chaos engineering, its history and applicability and conceptualize it as a pathway for excellence and relevance in engineering education in Africa. Findings from the that engineering is pivotal for economic and social development of any country but it has not resulted to such in Africa which necessitates chaos principles. It was found out that experimentation is a basic principle of chaos engineering while the advanced principles are hypothesizing about steady state, vary real-world events, run experiments in production, automate experiments to run continuously, minimize blast radius. These all were conceptualized as the pathway to excellence and relevance in engineering education in Africa. The study recommended that there is a need to intensify effort on researching more into chaos engineering in Africa.
Keywords: engineering, engineering education, principle, chaos principle, excellence, relevance, chaos engineering.
References: 1. Medina. 2018. Getting Started with Chaos Engineering. (cit. on pp. 26, 32). 2. Azar, A. T. & Vaidyanathan, S. 2014. Computational intelligence applications in modeling and control,Springer. 26. 3. Azar, A. T. & Vaidyanathan, S. 2015. Chaos modeling and control systems design, Springer. 4. Moore 1991. Generalized shifts: unpredictability and undecidability in dynamical systems.Nonlinearity, 4, 199–230 5. Gaponov-Grekhov, A. V. &Rabinovich, M. I. 2011. Nonlinearities in Action: Oscillations Chaos OrderFractals, Springer Publishing 146-151 Company, Incorporated. Garfinkel, A. 1992. Controlling cardiac chaos. Science. 6. H. Kanz and T. Schreiber, Nonlinear Time Series Analysis. Cambridge, U.K.: Cambridge Univ. Press, 1997. 7. H. Nozawa 1992. A neural network model on a globally coupled map and applications based on chaos. J. of Nonlinear Science, 2, 377– 386. 8. Tsuda, T. Tahara and H. Iwanaga 1992. Chaotic pulsation in human capillary vessels and 9. its dependence on mental and physical condition. Intl. J. Bifurcation and Chaos, 2, 313–326. 10. J. M. T. Thompson and H. B. Stewart 1986. Nonlinear Dynamics and Chaos—GeometricalMethods for Engineers and Scientists. xxxxx: John Wiley & Sons. 11. K. Aihara and R. Tokunaga, ed. 1993. Application Strategy of Chaos. Ohmsya. 12. K. Aihara, “Time series analysis and prediction on complex dynamical behavior observed in a blast furnace,” Physica D, vol. 135, pp.305–330, 2000. 13. K. Aihara, ed. 1992. Application of chaos. Mathematical Sciences, 348, Science Inc. 14. K. Aihara, T. Takabe and M. Toyoda 1990. Chaotic neural networks. Phys. Lett. A, 144,333–340 15. Matthews, P., Ryan-Collins, L., Wells, J., Sillem, H. and Wright, H. (2012). Engineers for Africa: Identifyingengineering capacity needs in sub-Saharan Africa. Royal Academy of Engineering, Africa-UK Engineering for Development Partnership 16. National Academy of Engineering, Center for the Advancement of Scholarship on Engineering Education. (1999) .http://www.nae.edu/NAENonlinearity, 4, 199–230 17. R. L. Devaney 1989. An Introduction to Chaotic Dynamical Systems, Second Edition. Addison-Wesley Publishing Company. 18. S. Murashige and K. Aihara, “Experimental study on chaotic motion of a flooded ship in waves,” in Proc. R. Soc. Lond. A, vol. 454, 1998,pp. 2537–2553 19. Special Issue on Engineering Chaos, Trans. Inst. Electron., Inform.VCommun. Eng., vol. E73, pp. 757–863, 1990. 20. T. S. Parker and L. O. Chua, Practical Numerical Algorithms for Chaotic Systems. Berlin, Germany: Springer-Verlag, 1989. 21. UNESCO Report (2010) Engineering: Issues Challenges and Opportunities for Development (accessed on 11/25/2016 8:22 AM). 22. Vaidyanathan, S. 2013. Analysis and adaptive synchronization of two novel chaotic systems with hyperbolic sinusoidal and cosinusoidal nonlinearity and unknown parameters. Journal of EngineeringScience and Technology Review, 6, 53-65. 23. William R. Shadish, Thomas D. Cook, Donald T. Campbell, Experimental and QuasiExperimental Designs for Generalized Causal Inference, Wadsworth Publishing, 2ndedition, January 2001 24. Y. Mizukami, T. Nishimori, J. Okamoto, and K. Aihara, “Forecasting daily peak load by a deterministic prediction method with the Gram- Schmidt orthonormalization” (in Japanese), Trans. Inst. Electr. Eng.Jpn., vol. 115-C, pp. 792–797, 1995. Authors: A.K.Arun Raja, B.Suresh, Shobhan kumar, G.Priyadharshan, K.Arun Vasantha Geethan 27. Establishment, Description and Equivalence of Flax Fabric Reinforced and E-Glass Fabric Paper Title: Reinforced Polyester Based Composite Abstract: Every fabric can be categorized as either synthetic or natural fibre. Both natural and synthetic fibre have both advantages and disadvantages. Natural fibres are extracted from various plants and animals’ sources, while synthetic fibres are made from chemical compounds which requires enormous amount of non- renewable energy sources. Comparing with the flax fabric, glass fibre mats are made from silica (SiO2) sand, which melts at 1720°C/3128°F. Glass fibre mat requires burning enormous of fossil fuel for producing heat, whereas flax fabric is extracted from the bast or the skin of linseed plant that grow inside stalks of the plants. Flax fabric is hydrophilic in nature, which by the mercerization process is converted to hydrophobic in nature. In Mercerization process fabric is treated with a caustic soda (NaOH) solution in water to improve properties such as fibre strength, shrink- age resistance, lustre, and dye affinity. The composites manufacturing process known as Hand layup involves laying down individual reinforced fabric of glass and flax separately and then wet with isophthalic resin (mixed with 2% of charcoal) by measuring the quantity by weighing. Scanning Electron Microscopy (SEM) analysis, provides evaluating of glass and flax reinforced composites for surface fractures, flaws, contaminants or corrosion. In order to check the flame resistance fire retardant test is done. Furthermore, mechanical test result showed the comparative values of tensile, impact and flexural strength of both the composites.
Keywords: Glass fabric, Flax fabric, Mercerization, Hand layup, SEM. 152-158
References: 1. M. Janarthanan “Mechanical properties of flax fibers and their composites”. 2. Layth Mohammed, M.N.M Ansari, Grace Pua, Mohammed Jawaid, M.Saiful Islam “A Review on Natural Fiber Reinforced Polymer Composite and Its Applications”. 3. S.R. Benin,S. Kannan, Renjin J. Bright, A. Jacob Moses ”A Review on mechanical characterization of polymer matrix composites and its effects reinforced with various natural fibres”. 4. Miroslav Frydrych , Štˇepán Hýsek , Ludmila Fridrichová , Su Le Van 2, Miroslav Herclík,Miroslava Pechoˇciaková, Hiep Le Chiand Petr Louda ”Impact of Flax and Basalt Fibre Reinforcement onSelected Properties of Geopolymer Composites”. 5. T. Srinivasan , G. Suresh , P. Ramu , V. Gokul Ram, M. Giresh , K. Arjun “Effect of water absorption of the mechanical behaviour of banana fiber reinforced IPN natural composites”. 6. Manoj Kumar Singh and Sunny Zafar “Development and mechanical characterization of microwave-cured thermoplastic based natural fibre reinforced composites”. 7. J.P. Torres, L.-J. Vandi, M. Veidt, M.T. Heitzmann “The mechanical properties of natural fibre composite laminates: a statistical study”. 8. K.L. Pickering, M.G. Aruan Efendy, T.M. Le “A review of recent developments in natural fibre composites and their mechanical performance”. 9. Kang Yang1, Sujun Wu1, Juan Guan1, Zhengzhong Shao2 & Robert O. Ritchie “Enhancing the Mechanical Toughness of Epoxy- Resin Composites Using Natural Silk Reinforcements”. 10. M. Fan , A. Naughton , J. Bregulla “Fire performance of natural fibre composites in construction”. Authors: Omosebi Taiwo O, Noor Faisal Abas Feasibility and Durability of Interlocks (Paving stones) from Polyethylene Terephthalate (PET) Paper Title: Wastes Abstract: Managing plastics waste is a global challenge that challenges the health of our ecosystem due to their high rate of production and non-biodegradability. However, it is important to handle PWs properly to curtail the environmental emissions associated with their incineration and dumping into landfills. The world's building industry is influenced by looking at the expense of construction materials and the required raw materials to manufacture them with the supporting climate that is rising at an unprecedented pace. The recycling of plastic waste into new useful building construction products will be a great advantage In this analysis, the shredded PET waste gathered from the recycling center was heated to 230 0C and used as a binder for the complete substitution of cement with a river sand aggregate for the manufacture of polymer interlocking / paving stones. The physical characteristics and mechanical performance of the aggregate materials and PET polymer concrete (including their distribution of particle size, silt , clay and dust content, relative stiffness, water absorption, porosity, flexural and compressive strength) were tested on various PET waste: 100%, 90%, 70%, 50% and 30% sand mixing percentages. The results showed that the produced interlocks from 30% PET and 70% river sand (3:7) achieved higher density, flexural, and compressive strength than the other combination percentages. The least 28. strength and porosity were exhibited by the polymer concrete produced with 100 % PET. The compressive strength of the PET polymer concrete produced with 30 % PET waste composition was higher than that of cement concrete at 28 days curing. Based on the test results, PET polymer concrete at 30 % PET replacement 159-165 can be used for interlocking tiles / paving stones due to its strength, low water absorption, and eco-friendliness, especially in water-logged areas. This prospect of interlocking tile production using polyethylene terephthalate (PET) waste and sand would not only minimise the cost of building production, but will only act as a waste diversion to mitigate environmental emissions caused by plastic waste disposal.
Keywords: Paving stones; Plastic wastes; Pollution; Interlocks; Aggregates; Recycling; Polymer concrete.
References: 1. Semiha Akçaözoğlu, (2015) ‘Evaluation of waste plastics as recycled plastic composite materials’, Journal of Waste Management, Vol. 1, pp. 16–19. Edorium. 2. Abeer, S.A.R., El Nashar, D.E., Abd-El-Messieh, S.L., and K.N. Abd-El Nour K.N., (2009) “Master. Des”., 30, 3760 3. Siti Aishah Wahid, Sullyfaizura Mohd Rawi, Noelia Md Desa, (2015) ‘Utilization of Plastic Bottle Waste in Sand Bricks’, Journal of Basic and Applied Scientific Research, ISSN 2090-4304, Vol. 5(1), pp. 35-44. 4. Sadiq, M.M., and Khattak, M.R. (1999) “An Overview of Plastic Waste Management’’, Journal of Emerging Technologies and Innovative Research (JETIR), 2(6), Plastic Waste Management Institutes, Central Pollution Control Board, Delhi. 5. Anslem E. O., Eneh, (2015) ‘Application of Recycled Plastics and Its Components in the Built Environment’, BEST: International Journal of Management, Information Technology and Engineering ISSN 2348-0513, Vol. 3, Issue 3, pp. 9-16, Delhi. 6. Rajesh C., Manoj, K.C., Unnikrishnan, G., and Purushothaman E. (2011) Adv. Polym. Technol., 32, S1 7. Dr. Pawan Sikka, ‘Plastic Waste Management In India’, Department of Science & Technology, Government of India New Delhi, India, pp. 1 - 4. 8. EPA 430-R-11-005. (2011) “Inventory of U.S. Greenhouse Gas Emissions and Sinks: (1990–2009)”, U.S. Environmental Protection Agency homepage. Available at: http://www. epa.gov. U.S. 9. Melik Bekhiti, Habib Trouzine, Aissa Asroun, (2014) ‘Properties of Waste Tire Rubber Powder’, Engineering, Technology & Applied Science Research, Vol. 4, No. 4, pp. 669-672. 10. Noel Deepak Shiri, P. Varun Kajava, Ranjan H. V., Nikhil Lloyd Pais, Vikhyat M. Naik, (2015) “Processing of Waste Plastics into Building Materials Using a Plastic Extruder and Compression Testing of Plastic Bricks”, in Journal mechanical Engineering and Automation, Vol.5(3B), pp. 39 - 42. 11. Patil, P.S Mali, J.R Tapkire, G.V., and Kumavat, H.R. (2015) ‘’Innovative techniques of waste plastic used in concrete” in Journal mechanical Engineering and Automation, vol.5 pp 1800-1803. 12. Konin, A. (2011). “Use of plastic wastes as a binding material in the manufacture of tiles: the case of wastes with a basis of polypropylene”. In jourmal of Materials and structures RILEM, 1381-1387. 13. Otuoze H. S., Amartey Y. D., Sada B. H., Ahmed H. A., Sanni M. I., & Suleiman M. A. (2012) “Characterization of sugar cane bagasse ash and Ordinary Portland Cement Blends in Concrete”, in 14. 4th West African Built Environment Research (WABER) Conference (pp. 1231-1237). Abuja, Nigeria. 15. Ramaraj, A. P., & Nagammal, A. N. (2014). “Exploring the current practices of post-consumer PET bottles and innovative applications as a sustainable building material” in 30th International Plea Conference (pp. 16-18). Ahmedabad: Cept University Press. 16. Velumani P., & Karthik S. G., (2017). “Development of ecofriendly pressed roof tiles: A prologue study” in International journal of scientific and engineering s research, 8 (12), 20302033 xvii. 17. British Standard (BS EN ISO 62). (1999) “Plastics-determination of water absorption” in British Standard, United Kingdom. 18. American Society for Testing and Materials ASTM C33 (2003) “Standard Specification for Concrete Aggregate” in West Conshohocken, PA, USA. 19. CHOUGULE R.S., Magdum J.J., Jaysingpur Sayali Yanmar, Jaysingpur Sonam, Salunkhe, Jaysingpur Poonam PatilJaysingpur Akshay Saitawadekar, Jaysingpur Mandar Japanese, (2017)“USE OF PLASTIC WASTE IN CIVIL CONSTRUCTION” in International Journal of Engineering Technology, Management, and Applied Sciences www.ijetmas.com Volume 5 Issue 4, ISSN 2349-4476, Japan. Authors: Taruna Sharma, Parikshit Vasisht, Munish Vashishath, R. S. Yaduvanshi
Paper Title: Dual Band Notch, Compact, Low profile, Hybrid Ultra Wideband RDRA Abstract: This paper presents a novel, compact Ultra Wide Band , Asymmetric Ring Rectangular Dielectric Resonator Antenna (ARRDRA), which is a unique combination of Thin Dielectric Resonator (DR), Fork shape patch and defective ground structure. The base of the proposed antenna is its Hybrid structure, which generates fundamental TM, TE and higher order modes that yields an impedance bandwidth of 119%. Proposed antenna provides a frequency range from 4.2 to 16.6 GHz with a stable radiation pattern and low cross polarization levels. Peak gain of 5.5 dB and average efficiency of 90% is obtained by the design. Antenna is elongated on a FR4 substrate of dimension 20 x 24x 2.168 mm3 and is particularly suitable for C band INSAT, Radio Altimeter, WLAN, Wi-Fi for high frequencies. Ease in fabrication due to simplicity, compactness, stable radiation pattern throughout the entire bandwidth are the key features of the presented design. Inclusion of Defective ground structure and asymmetric ring not only increases the bandwidth but also stabilize the gain and efficiency due to less surface current. Presented design launch an Ultra Wide Band antenna with sufficient band rejection at 4.48-5.34 and 5.64-8.33 GHz with stable radiation pattern and high gain.
Keywords: Asymmetric Ring Rectangular Dielectric Resonator Antenna (ARRDRA), Microstrip Patch Antenna (MPA), Ultra Wideband, Multimode Resonance, Microstrip feed.
References: 1. Luk, K.M. and K.W. Leung, 2003. Dielectric resonator antennas, Research Studies Press Baldock , England. 2. Petosa ,A. ,2007. Dielectric Resonator Antennas Handbook. Artech House, Norwood, USA. 29. 3. Tayeb A. Denidni, Qinjiang Rao, Abdel R. Sebak “Broadband L-Shaped Dielectric Resonator Antenna”. IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 4 2005. 4. Yang Gao, Zhenghe Feng, and Li Zhang,”Compact Asymmetrical T-Shaped Dielectric Resonator Antenna for Broadband 166-169 Applications” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 60, NO. 3, MARCH 2012 5. Ahmed A. Kishk, Ricky Chair, Kai Fong Lee,” Broadband Dielectric Resonator Antennas Excited by L-Shaped Probe” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 54, NO. 8, AUGUST 2006. 6. Xian-Ling Liang, Tayeb A. Denidni, Li-Na Zhang,” Wideband L-Shaped Dielectric Resonator Antenna With a Conformal Inverted- Trapezoidal Patch Feed” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 57, NO. 1, JANUARY 2009. 7. Kenny Seungwoo Ryu, Ahmed A. Kishk,” Ultrawideband Dielectric Resonator Antenna With Broadside Patterns Mounted on a Vertical Ground Plane Edge” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 58, NO. 4, APRIL 2010. 8. Kenny Seungwoo Ryu, Ahmed A. Kishk,” UWB Dielectric Resonator Antenna Having Consistent Omnidirectional Pattern and Low Cross-Polarization Characteristics” IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 59, NO. 4, APRIL 2011. 9. M. Abedian, S. K. A. Rahim, and M. Khalily, “Two-Segments Compact Dielectric Resonator Antenna for UWB Application” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 11, 2012. 10. Raghvendra Kumar Chaudhary, Rajnish Kumar, Kumar Vaibhav Srivastava,” Wideband Ring Dielectric Resonator Antenna with Annular-Shaped Microstrip Feed” 2013 IEEE. 11. A. H. Majeed, A. S. Abdullah, F. Elmegri, K. H. Sayidmarie, R. A. Abd-Alhameed, and J. M. Noras,” Aperture-Coupled Asymmetric Dielectric Resonators Antenna for Wideband Applications” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 13, 2014. 12. C.-E. Zebiri, M. Lashab, D. Sayad, I.T.E. Elfergani, K.H.Sayidmarie, F. Benabdelaziz, R.A. Abd-Alhameed, J.Rodriguez, J.M. Noras,” Offset Aperture-Coupled Double-Cylinder Dielectric Resonator Antenna with Extended Wideband” 2017 IEEE. 13. Poonam Kshirsagar, Shubha Gupta & Biswajeet Mukherjee,” A two-segment rectangular dielectric resonator antenna for ultra- wideband application” 2017 Taylor & Francis. 14. Mohammad Abedian , Homayoon Oraizi, Sharul Kamal Abdul Rahim, Shadi Danesh, Muhammad Ridduan Ramli, Mohamad Haizal Jamaluddin, “ Wideband rectangular dielectric resonator antenna for low-profile applications” . IET Microwave. Antennas Propagation., 2018, Vol. 12 Iss. 1, pp. 115-119. 15. Yangzhou Shao, Yuehe Ge*, Yinyan Chen, and Hai Zhang, “Compact Band-Notched UWB Dielectric Resonator Antennas” Progress In Electromagnetics Research Letters, Vol. 52, 87–92, 2015. 16. M. Abedian, S. K. A. Rahim, Sh. Danesh, S. Hakimi, L. Y. Cheong, and M. H. Jamaluddin, “Novel Design of Compact UWB Dielectric Resonator Antenna With Dual-Band-Rejection Characteristics for WiMAX/WLAN Bands” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 14, 2015 Authors: Amarendar Rao Thangeda, Alfred Coleman
Paper Title: Information Security Risk Analysis Methods for Healthcare Systems Abstract: Information and risk analysis in healthcare system is an important issue in the modern technological growth. There are many systems implemented for information security and risk management for information protection. Proper guidance is needed to select the system as all the systems concentrate on information security of healthcare system. The information threats and risk are increasing, and all the issues are integrated to the vulnerabilities producing risk for the healthcare security. The healthcare system process structure and variation are advocated, in which operating performance indication is based on risk scaling factor so that dynamic information security risk analysis is needed. This paper is proposed for information security risk analysis in which the resources, risk threats, vulnerabilities that control the healthcare system. The paper compares the various inputs and outputs are needed by different systems of information security risk assessment and analysis that accurately presents the information security risk. At present, large number of information security risk analysis methodologies are present in the worldwide. Important and efficient methodologies are considered for comparison and quantitative purpose to choose most suitable methodology for healthcare system.
Keywords: Information security risk analysis, healthcare security system, risk assessment, risk threats, risk vulnerabilities
References: 30. 1. Armaghan Behnia, Rafhana Abd Rashid, Junaid Ahsenali Chaudhry, “A Survey of Information Security Risk Analysis Methods”, Smart Computing Review, vol. 2, no. 1, February 2012. 2. Richard A. Caralli, James F. Stevens, Lisa R. Young, William R. Wilson, “Introducing OCTAVE Allegro: Improving the Information 170-177 Security Risk Assessment Process”, Software Engineering Institute, May 2007 3. W.G. Bornman L, Labuschagne, “A COMPARATIVE FRAMEWORK FOR EVALUATING INFORMATION SECURITY RISK MANAGEMENT METHODS”, Standard Bank Academy for Information Technology, Rand Afrikaans University. 4. Ajit Appari and M. Eric Johnson, “Information security and privacy in healthcare: current state of research”, Int. J. Internet and Enterprise Management, Vol. 6, No. 4, 2010. 5. ANITA VORSTER AND LES LABUSCHAGNE, “A Framework for Comparing Different Information Security Risk Analysis Methodologies”, Proceedings of SAICSIT 2005. 6. John Shortreed, John Hicks, Lorraine Craig, “Basic Frameworks for Risk Management”, The Ontario Ministry of the Environment,2003. 7. Wayne L. Brannan, CPHRM, CBCP, ARM, Director, University Risk Management, “A Model for Enterprise Risk Management Within a Healthcare Organization”, The Medical University of South Carolina Charleston, South Carolina,2006. 8. Christopher J. Alberts Sandra G. Behrens William R. Wilson, “Managing Information Privacy & Security in Healthcare”, Health Information Risk Assessment and Management, 2007. 9. Johan Karlsson, “Information Structures and Workflows in Health Care Informatics”, Department of Computing Science Umea University,2010. 10. Bryan Cline, “A Security and Compliance Risk Management Framework for Health Care”,2009. 11. “HOW TO MANAGE WORK HEALTH AND SAFETY RISKS”, Government of South Australia code of practice, 2019. 12. Stefan Fenz, Andreas Ekelhart, “Information Security Risk Management: In Which Security Solutions Is It Worth Investing”, Communications of the Association for Information Systems,2011. 13. Arben Mullai, “Risk Management System – Risk Assessment Frameworks and Techniques”, Project partly financed by the European Union (European Regional Development Fund) within the BSR INTERREG III B programme,2006. 14. “A structured approach to Enterprise Risk management (ERM) and the requirement of ISO 31000”, AIRMIC, Alarm, IRM: 2010 15. Anand Singh, “Improving Information Security Risk Management”, THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA, December 2009. Authors: Ashutosh Pandya, Chinmay Raut, Mihir Patel, Siddharth Das, Amol Deshpande
Paper Title: Bluetooth Based Electronic Notice Board. Abstract: Notice boards are of primary importance in any organization and in places such as bus and railway stations,when a need of for circulating notices arises it becomes tedious job. Thus an electronic notice board is an extremely efficient method of providing messages. It is difficult to update the messages at once. Thus this project focuses on development of a wireless board. This apparatus has the capability of displaying the latest messages using an Android application from a smart phone. This helpsusintransmittinganymessagewithinafractionofasecond eliminating any delay by simply sending a command which is much efficient compared to any other traditional method of transmitting the message. Thus the 31. proposed technology can beof great utility in many public places such as malls or commercial buildings to enhance the security system and also increase the awareness regarding emergency situations and avoid any possible dangers. 178-181
Keywords: Wireless, Microcontroller, GSM module, Bluetooth, Liquid crystal display, Rectifier, Regulator.
References: 1. A.Pramanik,Rishikesh,V.Nagar,S.DwivediandB.Choudhury,"GSM based Smart home and digital notice c board," International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, 2016, pp.41- 46.2016. 2. K. V. S. S. S. S. Sairam, N. Gunasekaran and S. R. Redd, "Bluetooth in wireless communication," in IEEE Communications Magazine, vol. 40, no. 6, pp. 90-96, June2002. 3. J. S. Lee, Y. W. Su, and C. C. Shen, ”A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi”, Proceedings of the 33rd AnnualConferenceoftheIEEEIndustrialElectronicsSociety(IECON),pp. 46-51, November2007. 4. Mr.Praveenraj, Dr.I.Gerald Christopher Mr.S.Selvakumaram, Mr.P.Soundar Rajan ``Lab view based wireless noticeboard'',\emph{International Journal of Engineering and Applied Sciences IJEAS}, vol. 3, no. 11,.2016. 5. T. Kim, S. Cho, S. Choi, S. Park and S. Lee, "Emotional Voice ConversionUsingMultitaskLearningwithText-To- Speech,"ICASSP2020 -IEEEInternationalConferenceonAcoustics,SpeechandSignalProcessing (ICASSP), Barcelona, Spain, 2020, pp. 7774-7778,2020 6. S. S. Mohammedsheet and M. S. Aziz, "Design and implementation of digital heart rate counter by using the 8051 microcontroller," International Conference on Engineering Technology and their Applications (IICETA), Al-Najaf, 2018, pp.107-111,2018. 7. V. Kulkarni, P. P. Kulkarni and R. D. Kulkarni, "Design and Development of Software based Waveform Generation using Microcontroller 8051," International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, 2019, pp.1- 7,2019. 8. S.Sanjeev,J.AjayanandS.Gowtham,"MicrocontrollerBasedBorewell Vehicle Status Informer Using GSM," 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, , pp.73-77,2019. Authors: K.Balashowry, T.Saikrishna, K.Sri Hari Charan Reddy, Mandhula Kalyan
Paper Title: Effect of heat Transfer Through Arbitrary Shaped fins using Computational Fluid Dynamics Abstract: An automobile engine produces a lot of heat and is subjected to thermal stresses. These high temperatures and thermal stresses generated might causedistortions in the engine components and also reduces the volumetric efficiency of the engine. It is important to remove this heat generated to ensure the ideal functionality of the engine. In order to dissipate this heat out of the engine through convection, extended surfaces(fins) are used as a medium, projected to the engine walls. In the present analysis, arbitrary shaped fins of same surface area are designed and heat transfer analysis is performed. Using the ANSYS Fluent software the analysis is done. The main intent of our study is to increase the heat transfer rate using the arbitrary shaped fins. The results obtained are compared with the regular shaped solid fins. Results show there's a significant increase in heat transfer through the arbitrary shaped fins. The fin with elliptical hole has greater heat transfer rate than other models of fins used in the analysis.
Keywords: CFD, Convection, Fins, Notches.
References: 1. “AkshendraSoni”, Study of Thermal Performance between Plate-fin, Pin-fin and Elliptical Fin Heat Sinks in Closed Enclosure under 32. Natural Convection, International Advanced Research Journal in Science, Engineering and Technology, Vol. 3, Issue 11, November 2016. 2. “K. Sathishkumar, K.Vignesh, N.Ugesh, P.B.Sanjeevaprasath, S.Balamurugan”, Computational Analysis of Heat Transfer through 182-187 Fins with Different Types of Notches, Vol-4, Issue-2, Feb- 2017. 3. “N.Nagarani”, Experimental Heat Transfer analysis On Annular Circular and Elliptical Fins, International Journal of Engineering Science and Technology, Vol. 2(7), 2010, 2839-2845. 4. “Mayank Jain, MahendraSankhala, Kanhaiya 5. Patidar, Lokesh Aurangabadkar”, Heat Transfer Analysis And Optimization Of Fins By Variation In Geometry,Volume- 5, Issue-7, Jul.-2017. 6. “Pravin Kamble , S.N Doijode , Geeta Lathkar”, A Review on Experimental Study of Heat Transfer from Plate Fin in Mixed Convection Mode,Volume 4 Issue 7, July 2015. 7. “N.A.Nawale, A.S.Pawar”, Experiment On Heat Transfer Through Fins Having Different Notches, IOSR Journal of Mechanical and Civil Engineering 2278-1684,p-ISSN: 2320-334X, 8. PP 46-49 9. “A.-R. A. Khaled”, Investigation of Heat Transfer Enhancement Through Permeable Fins,Journal of Heat Transfer 132(3),Volume 132, Issue 3,March 2010. 10. “VKarthikeyan, R. Suresh Babu, G. Vignesh Kumar”, Design and Analysis of Natural Convective Heat Transfer Coefficient Comparison between Rectangular Fin Arrays with Perforated and Fin Arrays with Extension, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 2, February 2015. 11. “Sunil S, Gowreesh S S, Veeresh B R”, Heat Transfer Enhancement and Thermal Performance of Extended Fins,International Journal of Engineering and Advanced Technology (IJEAT)ISSN: 2249 – 8958, Volume-5, Issue-5, June 2016. 12. “Sanjay Kumar Sharma1 and Vikas Sharma”,Maximising The Heat Transfer Through Fins Using Cfd As A Tool, International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.2, No.3, Aug2013. Authors: Saikat Chowdhury, Baibaswata Das, Abhishek Hazra Behavioural Study of R/C Natural Draught Cooling Towerunder Gravity Load using Different Paper Title: Support Orientations Abstract: In the field of both atomic and thermal power-plant the Natural Draught Cooling Tower plays a very important role. The vulnerability of NDCT lies in its hyperbolic shell structure whereas the supporting columns play a pivotal role in load distribution and heat transfer mechanism. Throughout our study effort has been given to find out the behavior of Reinforced Concrete NDCT under static loading using two different types of support orientation. For enhanced accuracy finite element analysis method has been adopted. The geometry of NDCT has been created by strictly following IS code provisions. A detail analysis has been presented in terms of 33. deflection, stress propagation and strain behavior under gravity load. In the final stage a comparison of response behavior of the hyperbolic shell and supporting columns has been prepared with various key components of 188-193 stress strain behavior.
Keywords: NDCT, Support Orientation, Finite Element Method, Shell Structure,
References: 1. Ansary, A M El, A A El Damatty, and A O Nassef. “Optimum Shape and Design of Cooling Towers.” World academy of Science, Engineering anf Technology 9 (2011): 4–13.Print. 2. Journal, International et al. “Finite Element Analysis for Structural Response of Rcc Cooling Tower Shell Considering Alternative Supporting.” September 2016(2012):n.pag. Print. 3. Karisiddappa, M. “Finite Element Analysis of Column Supported Hyperbolic Cooling Towers Using Semi-Loof Shell and Beam Elements.” Engineering Structures 20.1–2 (1998): 75–85.Web. 4. Ke, S. T. et al. “A New Methodology for Analysis of Equivalent Static Wind Loads on Super-Large Cooling Towers.” Journal of Wind Engineering and Industrial Aerodynamics 111 (2012): 30–39.Web. 5. Grant, S, and T Y Yangs. “REFINED ANALYSIS OF THE SEISMIC RESPONSE OF COLUMN-SUPPORTED COOLING TOWER.” II.3 (1980): n. pag.Print. 6. Gould, P.L., S.K. Sen, and H. Suryoutomo. “Dynamic Analysis of Column‐ supported Hyperboloidal Shells.” Earthquake Engineering & Structural Dynamics 2.3 (1973): 269–279.Web. 7. Makovička, D. “Response Analysis of an RC Cooling Tower Under Seismic and Windstorm Effects.” 46.6 (2006): 17–21.Print. 8. Reed, D. A., and Robert H. Scanlan. “Time Series Analysis of Cooling Tower Wind Loading.” Journal of Structural Engineering 109.2 (1983): 538–554.Web. 9. Yang, By T Y, M Asce, and Rakesh K Kapania. “• Finite Element Random Response Analysis of Cooling Tower.” 110.4 (1984): 589– 609.Print. 10. “Theoretical Study of Earthquake Response of a Cooling Tower.pdf.” : n. pag. Print. Authors: P. Poongodi, C. Padmavathi, R. Vinitha, G. Hema
Paper Title: Orderings on Generalized Regular Interval Valued Fuzzy Matrices Abstract: In this paper, a special type of ordering for k - regular Interval Valued Fuzzy Matrix (IVFM) is introduced as a generalization of the minus partial ordering for regular fuzzy matrices. A set of equivalent conditions for a pair of k – regular IVFM to be under this ordering are obtained. We exhibit that this ordering is preserved under similarity relation.
Keywords: Fuzzy Matrix, k-regular IVFM, minus ordering, k-ordering. 34. References: 1. K.H. Kim and F.W. Roush, “Generalized fuzzy matrices” Fuzzy sets and systems, 4, 1980, 293 – 315. 194-198 2. R. Meenakshi and C.Inbam , “The Minus Partial Order in Fuzzy Matrices” The Journal of Fuzzy Mathematics vol.12, No.3, 2004, 695 – 700. 3. Meenakshi, AR., and Kaliraja,M., “Regular Interval Valued Fuzzy Matrices” Advances in Fuzzy Mathematics, Vol 5, No 1, 2010, 7 -15. 4. A.R.Meenakshi, and P.Poongodi, Generalized Regular Interval-Valued Fuzzy Matrices, International Journal of Fuzzy Mathematics and Systems, 2( 1) (2012), 29-36. 5. Shyamal.A.K., and Pal. M., “ Interval Valued Fuzzy Matrices” Journal of Fuzzy Mathematics, Vol 14, No 3, 2006, 582 – 592. 6. Thomason, M.G., “Convergence of powers of fuzzy matrix” , J.Math Anal. Appl. 57, 1977, 476 – 480. 7. Zadeh L.A., “ Fuzzy sets, Information and Control”, 8: 1965, 338 – 353. Authors: Girma Kebebew Tufa, Bogale G/Mariam
Paper Title: Impact of Land Use Change Study on Reservoir’s Sediment Yield using SWAT Model Platform Abstract: The overall goal of this study is to evaluate the effect of land use on reservoir’s sediment yield by applying Arc SWAT model interface with GIS and identify the vulnerable sub basin in Neshi dam watershed. Different input data were collected from different sources including Ministry of Water, Irrigation and Energy, and Ethiopia National Meteorological Agency. The study was done using historical records of nineteen years for Neshi Watershed. The calibrated flow and sediment for the 1992-2001 years gave R2 0.77, 0.92 and NES 0.64, 0.96, respectively. The validated flow and sediment for the 2002-2008 years gave R2 0.72, 0.93 and NES 0.75, 0.95, respectively.In this study the SWAT model yields average annual sediment load of 634.49, 516.82 and 542.56 ton/ha/yr for land use change of 1990, 2010 and 2017, respectively at Neshi outlet dam site. Therefore, the issue of land use change impact on sediment yield on reservoir as part of the integrated adaptation mitigation measures program in order to achieve sustainable development is very relevant. The output of this study can help planners, decision makers and other stakeholders to plan and implement appropriate soil and water conservation strategies.
35. Keywords: Sediment yield, SWAT Model, Neshi Watershed, LUC, SWAT CUP
References: 199-206 1. Awulachew.S.,McCartney,Steen huis,T and Ahmed .A (2008). Review of hydrology, sediment and water resource use in the Blue Nile Basin. pp87-pp95. 2. Bayissa, C. (2007). Assessment of Malaria as a Public Health Problem in Finchaa Sugar Factory based on Clinical Records and Parasitological Surveys. MSc Thesis. Addis Ababa University,Ethiopia.: unpublished. 3. Bezuayehu, T. (2006). people and Dams: Environmental and soci-economic impacts of Fincha'a hydropower dam, western Ethiopia. Tropical Res. Manag.Paper, 75. 4. Easton.K.,Fuka,D.,White,D.,Collick,A.and Biruk Ashagre (2010). A multi basin SWAT model analysis of runoff and sedimentation in the Blue Nile, Ethiopia. Hydrology and Earth System Sciences, 14(10), 1827-1841. 5. Friedrich ,K.,Kochi Ann Van Griensven,K.,Sirak Tekleab and Teferi,E. (2012). The Effects of Land use Change on Hydrological Responses in the Choke Mountain Range (Ethiopia). Helmholtz Centre for Environmental Research. 6. Gassman, R.,Reyes,M.,Green,CH.and Arnold,G. (2007). The Soil and Water Assessment Tool: Historical Development. Applications, and Future Research Directions. Transactions of the ASABE. 7. Gizaw , L.,Haddis,A., Deboch,D.and Birke,W. (2004). Assessment of factors contributing to eutrophication of Abasamuel water reservoir in Addis Ababa Ethiopia. Journal of Health Science, 112–113. 8. Habtamu, B. (2017). Simulating Hydrological and ClimateVariability-Impacts on the Watershed of Legedadi Reservoir using ArcSWAT. Addis Ababa: Unpublished. 9. ICOLD. (2009). Sedimentation and sustainable use of reservoirs and river systems. paris. 10. Mekonnen ,A.,Andres,W,,Bijan.D.and Admasu ,G. (2009). Hydrological modelling of Ethiopian catchments using limited data. Hydrol. Process, 23, 3401–3408. 11. Neitsch, J.,Arnold,J., Kirniy,J. and William, J. (2005). Soil and Water Assessment Tool, Theoretical documentation. Texas A & M Black land Research Centre. 12. Setegn,S., Srinivasan,R., Daegahi,B. and Meiesse ,A.(2008). Spatial delineation of soil erosion vulnerability in the lake Tana Basin, Ethiopia. Hydrological Processes. 13. Taye, H. (2016). The Dynamics of Land use Land cover change on the Stream Flow in Fincha Amerti Neshe Sub-basin: Abay basin, Ethiopia. Addis Ababa,Ethiopia: Unpublished. Authors: Hanady A.El-Rahman El-Dehemy
Paper Title: Behavior of Steel Plate Girders with Deep Section under Dynamic Effect Abstract: Having a minimum mass, equal-sized flanges and no web stiffeners is the most economical plate girder to fabricate. As with rolled I-sections, for a given section modulus a section with a greater depth will have a lower mass than one with a smaller depth, except in some instances where a thicker web is required in the deeper section. A wider flange plate to resist the buckling tendency may be necessary to use, when the compression flange is laterally unrestrained, but this will add to the cost because of the more difficult assembly procedure. In order to arrive at a minimum-mass cross section as much as possible of the material should be located in the flanges and as little as possible in the web, consistent with shear requirements. There is usually an advantage, however, in using a somewhat thicker web in order to reduce welding distortion, or to avoid the use of or number of stiffeners. It can be shown that for a given web depth to thickness ratio the minimum-mass cross section is that in which the area of the two flanges combined equals that of the web, i.e. 2Af = Aw.An important consideration in cost reduction is the use of preferred plate widths and thicknesses for the flange and web 36. elements 207-209 Keywords: Steel Girders, Finite Element, ABAQUS software.
References: 1. Earls, C. J, “On the inelastic failure of high strength steel i-shaped beams”. Journal of Constructional Steel Research, 1999, 49(98), 1– 24. 2. Kemp AR, "Factors affecting the rotation capacity of plastically designed members". Journal of Structural Engineering, 1986, 64B (2): 2835. 3. Kemp, A. R., "Inelastic local and lateral buckling in design codes". Journal of Structural Engineering, 2014, 122(4), 374-382. 4. Shokouhian, M., & Shi, Y, “Classification of i-section flexural members based on member ductility". Journal of Constructional Steel Research.2014,95(3), 198-210. 5. Cheng, X., Chen, Y., & Nethercot, D. A. “Experimental study on H-shaped steel beam-columns with large width-thickness ratios under cyclic bending about weak-axis". Engineering Structures, 2013, 49, 264-274. 6. Cheng, X., Chen, Y., & Pan, L., “Experimental study on steel beam–columns composed of slender H-sections under cyclic bending". Journal of Constructional Steel Research”. 2013, 88, 279-288. 7. Basler K . "Strength of plate girders under combined bending and shear". 1961, Journal Structure Div ASCE, 87. Authors: Mohammed Aslam Sohail, K.Venkateswarlu, Abdul Hafeez
Paper Title: Inspection of Centrifugal Blower by Varying Different Blade Configuration using CFD Abstract: This examination points towards the advancement of an enhanced plan of a radiating blower comprising of different fan ribs, in light of execution appraisals looking like its inside parts. Different segments, for example, the outside cases and the turning fan ribs set in an assortment of working conditions, for example, fluctuating impeller speed and number of edges, are assessed mathematically and tentatively. Assessment depends on execution boundaries, including the delta and outlet pressures, stream rate, force, and intensity of the radial fan. The mathematical examination recommends that the blend of the different pivoting outline strategy and the standard k-ε disturbance model was suitable for recreation of the inside stream qualities and for power forecast. The mathematical outcomes were contrasted and tests under deliberately planned trial conditions. Plan and displaying of Impeller and packaging has been completed in CATIA V5R21 and then the Calculation was imported to Ansys 16.0. By differing the quantity of fan ribs, the exhibition fluctuates practically nothing. Nonetheless, the FC fan ribs display the best exhibition in regards to their stream rate reaction and are related with most reduced force. The weight at the exit is diminished as the stream rate is expanded. Among all the fans, 37. the FC fan would yield the most noteworthy stream rate. 210-214 Keywords: CFD, Impeller, k-ε, Torque, Pressure.
References: 1. Huang Chen-Kang and Hsiech Mu-En, “Performance analysis and optimized design of Backward curved airfoil centrifugal blowers”, American society of Heating, Refrigeration and Air Conditioning Engineers, May1,2009 2. J.B. Moreland “Housing Effect of a centrifugal blower” Journal of sound and vibration, Volume 36, isuue 2, September 1974 3. Renjing Cao and Jun HU, “A cluster design approach to noise reduction in centrifugal Blower”, International Journal of Ventilation, Volume 3, Number 4,pp.345-352,2005 4. PrezeljJurij and Carudina Mirko, “Identification of noise sources in centrifugal blower with acoustic camera”, The JOURNAL OF Acoustical Society of America, Volume 123,Number 5,p,3824,May 2008 5. G.H.Koopmann and W.Neise ,” The use of Resonators to silence centrifugal blower”, Journal of sound and Vibration, Volume 82,Number 1,pp.17-27,8May 1982 6. Q.Datong et, “Experimental study on the noise reduction of an industrial forward-curved blades centrifugal fan’, Applied Acoustics, Volume 70,Number 8,pp.1041-10520,August 2009 7. Christopher L. Banks and Sean F. Wu,” Prediction and reduction of Centrifugal blower noises” Journal of Acoustic Society of America, Volume 103, Number 5, pp.3045-3045, May 1998 8. Jianfeng Ma et a1 “Noise reduction for centrifugal fan with non-isometric forward –swept blade impeller”, Energy power Engineers, Volume 2,Number 4,pp.433-437,2008 9. Young-Tae Lee,Hee-Chang Lim,”Performance assessment of various fan ribs inside a centrifugal blower”School of Mechanical Engineering, Pusan National University, North Korea, November 2015 10. BaymoiNN,HafizAA,OsmanAA,”Energy conserve Manag 2006”:47(18):3307-18 11. Chunxi L, Ling WS, Yakui J. The performance of a centrifugal fan with enlarged impeller. Energy Convers Manag 2011;52(8):2902-10 12. Engin T, Gur M, Scholz R. Effects of tip clearance and impeller geometry on the performance of semi-open ceramic centrifugal fan impellers at elevated temperatures. Exp Therm Fluid Sci 2006;30(6):565-77. 13. Cui G, Mandas N, Manfrida N, Nurzia F. Measurements of primary and secondary flows in an industrial forward-curved centrifugal fan. J Fluids Eng 1990;109(4):353-8. Authors: M. Surendar, P. Pradeepa
Paper Title: Future Challenges in State of Charge Estimation for Lithium-Ion Batteries Abstract: Energy storage system is an Emerging technology in past few decades. The Energy storage system is an important technology for Electric Vehicles, Hybrid Electric Vehicles (EV) and (HVE) and Micro grid system. The Battery Management System (BMS) is need to be control and monitor the various parameter of the battery such as SOC , SOH, C-Rate, E-Rate ,Temperature , RVL , EOL and so on. However, the (SOC) State of Charge is an important estimation for the online control and BMS monitoring. The SOC is the challenging task when online control and BMS monitoring. This various technique or methods available to estimate the SOC and alsoits represents the Elaboration for various methods of SOC estimation and its drawback. Past five years, where the tendency of the Estimation technique has been oriented towards a mixture of probabilistic techniques and some Artificial Intelligence.
Keywords: Battery Management System BMS, Battery Model, Energy Storage, Lithium-ion Battery
References: 1. Antón, J.C.Á.; Nieto, P.J.G.; Gonzalo, E.G.; Pérez, J.C.V.; Vega, M.G.; Viejo, C.B. A new predictive model for the state-of-charge of a high-power lithium-ion cell based on a pso-optimized multivariate adaptive regression spline approach. IEEE Trans. Veh. Technol. 2016, vol.65, pp.4197–4208. 2. Cacciato, M.; Nobile, G.; Scarcella, G.; Scelba, G. Real-time model-based estimation of soc and soh for energy storage systems. IEEE Trans. Power Electron. 2017, vol.32, pp.794–803. 3. Cai, Y.;Wang, Q.; Qi,W. D-ukf based state of health estimation for 18650 type lithium battery. In Proceedings 4. Chaoui, H.; Golbon, N.; Hmouz, I.; Souissi, R.; Tahar, S. Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries. IEEE Trans. Ind. Electron. 2014, vol.62, pp.1610–1618. 5. Chaoui, H.; Golbon, N.; Hmouz, I.; Souissi, R.; Tahar, S. Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries. IEEE Trans. Ind. Electron. 2014, vol.62, pp.1610–1618. 6. Chaoui, H.; Gualous, H. Adaptive state of charge estimation of lithium-ion batteries with parameter and thermal uncertainties. IEEE Trans. Control Syst. Technol. 2017, vol.25, pp.752–759. 7. Chaoui, H.; Ibe-Ekeocha, C.C.; Gualous, H. Aging prediction and state of charge estimation of a lifepo4 battery using input time- delayed neural networks. Electr. Power Syst. Res. 2017, vol.146, pp.189–197. 8. Chen, J.; Ouyang, Q.; Xu, C.; Su, H. Neural network-based state of charge observer design for lithium-ion batteries. IEEE Trans. Control Syst. Technol. 2017, PP, 1–9. 38. 9. Chen, Z.; Fu, Y.; Mi, C.C. State of charge estimation of lithium-ion batteries in electric drive vehicles using extended kalman filtering. IEEE Trans. Veh. Technol. 2013, vol.62, pp.1020–1030. 10. Cuma, M.U.; Koroglu, T. A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renew. 215-223 Sustain. Energy Rev. 2015, vol.42, pp.517–531. 11. 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In Proceedings of the 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan, 14–17 March 2016; pp. 1436–1441. 18. Khayat, N.; Karami, N. Adaptive techniques used for lifetime estimation of lithium-ion batteries. In Proceedings of the 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), Beirut, Lebanon, 21–23 April 2016; pp. 98–103. 19. Klass,V.; Behm, M.; Lindbergh, G. Capturing lithium-ion battery dynamics with support vector machine-based battery model. J. Power Sources 2015, 298, pp.92–101. 20. Lillehei, C.W.; Cruz, A.B.; Johnsrude, I.; Sellers, R.D. A new method of assessing the state of charge of implanted cardiac pacemaker batteries. Am. J. Cardiol. 1965, vol.16, pp.717–721. 21. Liu, C.; Liu, W.; Wang, L.; Hu, G.; Ma, L.; Ren, B. A new method of modeling and state of charge estimation of the battery. J. Power Sources 2016, vol. 320, pp.1–12. 22. Lu, L.; Han, X.; Li, J.; Hua, J.; Ouyang, M. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 2013, vol.226,pp. 272–288. 23. Ng, K.S.; Moo, C.-S.; Chen, Y.-P.; Hsieh, Y.-C. Enhanced coulomb counting method for estimating state-of-charge and state-of- health of lithium-ion batteries. Appl. Energy 2009, vol.86, pp.1506–1511. 24. of the 2016 IEEE International Conference on Mechatronics and Automation, Harbin, China, 7–10 August 2016;pp. 754–758. 25. Peishan, Y.; Yin, B. Analysis of SOC Estimation Algorithm for Electric Vehicle Power Battery. Automot. Pract. Technol. 2019, vol.15, pp.15–17. 26. Peishan, Y.; Yin, B. Analysis of SOC Estimation Algorithm for Electric Vehicle Power Battery. Automot. Pract. Technol. 2019, vol.15, pp.15–17. 27. Ping, S.; Lu, L.; Gao, M.; Dong, R.; Xuning, S.F. Combined Estimation Method for Lithium Ion Battery State of Charge, State of Health and State of Function. CN105301509A, 3 February 2016. 28. Rahimi-Eichi, H.; Ojha, U.; Baronti, F.; Chow, M.Y. Battery management system: An overview of its application in the smart grid and electric vehicles. IEEE Ind. Electron. Mag. 2013, vol.7, pp.4–16. 29. Tang, X.; Liu, B.; Gao, F. State of charge estimation of lifepo4 battery based on a gain-classifier observer. Energy Procedia 2017, vol.105, pp.2071–2076. 30. Tingting, Y.; Jie, Z.; Linkai, Z.; Yuhua, Z. SOC Estimation and Simulation of Lithium Battery Based on Improved Ampere-hour Integral Method. Energy Sav. New Energy 2018, vol.6, pp.58–60. 31. Waag, W.; Fleischer, C.; Sauer, D.U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sources 2014, vol.258, pp.321–339. 32. Weng, C.; Sun, J.; Peng, H. A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of- health monitoring. J. Power Sources 2014, vol.258, pp.228–237. 33. Wu, H.-D.; Xiao-ming, R.;Wei, N.; Chao, H. Estimating SOC of Li-ion battery by improved AH combined with neural network. Battery Bimon. 2016, vol.46, pp.16–19. 34. Wu, Z.; Shang, M.; Shen, D.; Qi, S. SOC estimation for batteries using MS-AUKF and neural network. J. Renew. Sustain. Energy 2019, vol.11, pp.1–10. 35. Xiong, R.; Tian, J.; Mu, H.; Wang, C. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries. Appl. Energy 201, vol.207,pp.372-383. 36. Yang, Y.; Cui, N.;Wang, C.; Liu, M.; Gao, R. SOC estimation of lithium-ion battery based on new adaptive fading extended Kalman filter. In Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China, 20–22 October 2017. 37. Yu, H.; Duan, J.; Du, W.; Xue, S.; Sun, J. China’s energy storage industry: Develop status, existing problems and countermeasures. Renew. Sustain. Energy Rev. 2017, vol.71, pp.767–784. 38. Zhang, J.; Pan, G. Comparison and application of multiple regression and BP neural network prediction model. J. Kunming Univ. Sci. Technol. 2013, vol.38, pp.61–67. 39. Zhang, Z.L.; Cheng, X.; Lu, Z.Y.; Gu, D.J. Soc estimation of lithium-ion batteries with aekf and wavelet transform matrix. IEEE Trans. Power Electron. 2017, vol.32, pp.7626–7634. 40. Zou, Y.; Hu, X.; Ma, H.; Li, S.E. Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. J. Power Sources 2015, vol.273, pp.793–803. Authors: Pradeep Kumar, Pankaj Sharma
Paper Title: Multi- Response Optimization of Wire EDM Process Parameter on Aluminium Alloy (5086) Abstract: In the present work, the effect of process parameters on material removal rate during the machining of aluminium alloy (5086) with WEDM is studied. The four control parameter were selected i.e pulse on time (TON), pulse off time (TOFF), peak current (IP), and spark gap voltage (SV) to investigate their effects on material removal rate (MRR). Each control parameter had three levels. Total 27 experiments were done with a zinc coated brass wire of diameter 0.25 mm. Taguchi L9 orthogonal array technique was used for the experiment. ANOVA was used to find out the significance of control parameters and their contribution on MRR. It was found that maximum material removal rate was 41.52 mm3/min which was due to high pulse on time and low pulse off time.
Keywords: MRR, Process parameters, Taguchi technique, WEDM.
References: 1. . Kumar, K. Vivekananda, and K. Abhishek. (2019). Experimental investigation and optimization of process parameter for Inconel 718 using wire electrical discharge machining (WEDM). Journal of Advanced Manufacturing Systems.doi:10.1142/s0219686719500185. 39. 2. R. Magabe, N. Sharma,K. Gupta, and J. P. Davim. (2019). Modeling and optimization of Wire-EDM parameters for machining of Ni55.8Ti shape memory alloy using hybrid approach of Taguchi and NSGA-II. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-019-03287-z. 224-229 3. D. Pramanik, A. S. Kuar, and D. Bose. (2018). Effects of wire EDM machining variables on material removal rate and surface roughness of Al 6061 alloy. Renewable Energy and its Innovative Technologies. Doi:10.1007/978-981-13-2116-0_19. 4. G. R. Joshi and A. N. Chapgaon. (2017). Multi-response optimization of AISI M42 HSS material in wire-cut EDM using grey relational analysis. International Journal of Advanced Research in Engineering and Applied Sciences. 6(8), 1 – 14. 5. A. M. Takale,N. K. Chougule,R. L. Patil, and A. S. Awate. (2017). Analysis and optimization of wire electro discharge machining parameters of TiNi shape memory alloy using Taguchi technique. International Conference on Advances in Thermal Systems, Materials and Design Engineering, 2017. 6. A. Goswami and J. Kumar. (2017). Trim cut machining and surface integrity analysis of Nimonic 80A alloy using wire cut EDM. Engineering Science and Technology: An International Journal. 20(1), 175–186. 7. S. Garg,S. Kumar and G. Chawla. (2016) Experimental investigation of effect of process parameters on material removal rate during WEDM. International Journal of Current Engineering and Technology, 6(1) 40-45. 8. A. Kumar,V. Gulati and A. Goswami. (2015). Optimization of process parameter in WEDM for Monel K-500 using Taguchi method and grey relational analysis. International Journal of Research in Aeronautical and Mechanical Engineering. 3(4), 53-68. 9. V. Aggarwal, R. K. Garg and S. S. Khangura. (2015). Parametric modeling and optimization for wire electrical discharge machining of Inconel 718 using response surface. International Journal of Advanced Manufacturing Technology. 79(1-4), 31–47. 10. B. Sivaraman,C. Eswaramoorthy and E. Shanmugham. (2015). Optimal control parameters of machining in CNC Wire-Cut EDM for Titanium. International Journal of Applied Sciences and Engineering Researches. 4(1), 102-121. 11. P. Kubade, S. Jamadade, R. Bhedasgaonkar, R. Attar, N. Solapure, U. Vanarse and S. Patil. (2015). Parametric study and optimization of WEDM parameters for Titanium diboride TiB2. International Journal of Engineering and Technology. 2(4) 1657-1661. Authors: Apurva D. Dhawale, Sonali B. Kulkarni, Vaishali M. Kumbhakarna
Paper Title: Automatic Pre-Processing of Marathi Text for Summarization Abstract: The text summarization is a technique where the original large text is condensed into smaller version without changing its abstract meaning. The text summarization is done on the common foreign and regional 40. languages typically, but infrequent work has been observed for the Marathi language. As the amount of e- contents on web is increasing drastically, the users are facing difficulty to read the newspaper articles with extraction of its different perspectives with sorting. We are focussing on educational, Political and sports news 230-234 for summarization, which will be helpful for students who are appearing for competitive exams. This paper explores the pre-processing techniques for Marathi e-news articles.
Keywords: Text summarization, POS tagging, Pre-processing, LDA(Latent Dirichlet Allocation), LNS (Label Induction Grouping), SVM (Support Vector Machine)
References: 1. Mr. Shubham Bhosale, Ms. Diksha Joshi, Ms. VrushaliBhise, Prof.Rushali A. Deshmukh, “Marathi e-Newspaper Text Summarization Using Automatic Keyword Extraction Technique”, International Journal of Advance Engineering and Research Development Volume 5, Issue 03, March -2018. 2. Pooja Bolaj, SharvariGovilkar, “Text Classification for Marathi Documents using Supervised Learning Methods”, International Journal of Computer Applications (0975 – 8887), Volume 155 – No 8, December 2016. 3. Virat V. Giri, Dr.M.M. Math and Dr.U.P. Kulkarni, “A Survey of Automatic Text Summarization System for Different Regional Language in India”, Bonfring International Journal of Software Engineering and Soft Computing, Vol. 6, Special Issue, October 2016. 4. Prof. Satish Kamble, ShivlilaMandage,ShubhangiTopale, DipaliVagare, PreranaBabbar, “Survey on Summarization Techniques and Existing Work”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 1 (2017). 5. Anishka Chaudhari1, Akash Dole2, Deepali Kadam3, “Marathi text summarization using neural networks”, International Journal of Advance Research and Development, Volume 4, Issue 11, 2019. 6. Deepali K. Gaikwad, Deepali Sawane and C. Namrata Mahender, “Rule Based Question Generation for Marathi Text Summarization using Rule Based Stemmer”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, PP 51-54, 2018. 7. Yogeshwari V. Rathod,“Extractive Text Summarization of Marathi News Articles”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 07,July 2018. 8. Shraddha A. Narhari, RajashreeShedge, “Text Categorization of Marathi Documents using Modified LINGO”, IEEE, 2017 9. Jaydeep Jalindar Patil, Prof. NagarajuBogiri, “Automatic Text Categorization-Marathi documents”, International Conference on Energy Systems and Applications (ICESA 2015), IEEE, 2015. 10. Prakhar Sethi, Sameer Sonawane, SaumitraKhanwalker, R. B. Keskar, “Automatic Text Summarization of News Articles”, International Conference on Big Data, IoT and Data Science (BID) Vishwakarma Institute of Technology, Pune, Dec 20-22, IEEE, 2017 11. N. Dangre, A. Bodke, A. Date, S. Rungta, S.S. Pathak, “System for Marathi news clustering”, 2nd International conference on Intelligent computing,communication & convergence, bhubaneshwar, ELSEVIER, 2016. 12. Apurva D. Dhawale, Sonali B. Kulkarni, Vaishali Kumbhakarna, “Survey of Progressive Era of Text Summarization for Indian and Foreign Languages Using Natural Language Processing”, ICIDCA 2019, LNDECT 46, pp. 654–662, Springer Nature Switzerland, AG, 2020. 13. E. Lloret and M. Palomar, “Text summarization in progress: a literature review,” in Springer, no. April 2011, pp. 1–41, Springer, 2012. 14. Tarun B. Mirani and SreelaSasi, “Two-level Text Summarization from Online News Sources with Sentiment Analysis”, International Conference on Networks & Advances in Computational Technologies (NetACT) ,20-22 July 2017, Trivandrum, IEEE, 2017. 15. Vaishali Kalra, Dr. Rashmi Aggarwal, “Importance of Text Data Preprocessing& Implementation in RapidMiner”, Proceedings of the First International Conference on Information Technology and Knowledge Management pp. 71–75.ICITKM, ISSN 2300-5963 ACSIS, Vol. 14, New Delhi, 2017. 16. Sheetal Shimpikar, Sharvari Govilkar, “Abstractive Text Summarization using Rich Semantic Graph for Marathi Sentence”, JASC: Journal of Applied Science and Computations Volume V, Issue XII, ISSN NO: 1076-5131, December/2018. 17. Jovi D’silva, Dr.Uzzal Sharma, “Automatic Text Summarization Of Indian Languages: A Multilingual Problem”, Journal of Theoretical and Applied Information Technology Vol.97. No 11, 15th June 2019. 18. Poonam Kolhe, Prof. Ashish Kumbhare, “Optimizing Accuracy of Document Summarization Using Rule Mining”, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.6, pg. 207-216, June- 2017. 19. Umakant Dakulge, S. C. Dharmadhikari, “Automated Text Summarization: A Case Study for Marathi Language”, Data Mining and Knowledge Engineering, CIIT, Vol 6, No 3 (2014). 20. Mamatha Balipa, Dr. Balasubramani R, Harolin Vaz, Christina Shilpa Jathanna, “Text Summarization For Psoriasis Of Text Extracted From Online Health Forums Using Textrank Algorithm”, International Journal Of Engineering & Technology, 7 (3.34) (2018) 872-873, 18 September 2018. 21. Chirantana Mallick, Ajit Kumar Das, Madhurima Dutta, Asit Kumar Das And Apurba Sarkar, “Graph-Based Text Summarization Using Modified Textrank”, J. Nayak Et Al. (Eds.), Soft Computing In Data Analytics, Advances In Intelligent Systems And Computing 758, Springer Nature Singapore Pte Ltd. 2019. 22. 10] Reda Elbarougy, Gamal Behery, Akram El Khatib, “Extractive Arabic Text Summarization Using Modified Pagerank Algorithm”, Egyptian Informatics Journal 21, 73–81, Science Direct, Elsevier, (2020). 23. Ahmed Elrefaiy, Ahmed Rafat Abas, Ibrahim Elhenawy, “Review Of Recent Techniques For Extractive Text Summarization”, Journal Of Theoretical And Applied Information Technology 15th December 2018. Vol.96. No 23, Issn: 1992-8645, Jatit & Lls, 2005. 24. Rasim Alguliev, Ramiz Aliguliyev, “Evolutionary Algorithm for Extractive Text Summarization”, Intelligent Information Management, 1, 128-138, Scientific Research, SciRes, 2009. 25. Kalliath Abdul Rasheed Issam, Shivam Patel, Subalalitha C. N., “Topic Modeling Based Extractive Text Summarization”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020. 26. Siddhant Upasani, Noorul Amin, Sahil Damania, Ayush Jadhav, A. M. Jagtap, “Automatic Summary Generation using TextRank based Extractive Text Summarization Technique”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395- 0056, Volume: 07 Issue: 05 May 2020. 27. Yash Asawa, Vignesh Balaji, Ishan Isaac Dey, “Modern Multi-Document Text Summarization Techniques”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-9 Issue-1, May 2020. Authors: Amr A. Wael, Ahmed Elyamany, Ahmed Elhakeem
Paper Title: Classification of Evaluation Metrics for Project Baseline Schedules Abstract: The Evaluation of construction baseline schedules aims to provide a high-quality project schedule. However, obtaining a project schedule free from technical defects remains a challenge to construction planners and schedulers.The result of having a high-quality schedule is achieving the desired project completion dates and avoid any disputes between the construction project parties which may be present due to the technical defects of the schedule itself. Many organizations and consultants had developed some evaluations techniques for assessing 41. the baseline schedules from different prospects. The aim of this study is to classify and filter out the evaluation metrics and parameters of the project baseline schedule, in order to facilitate the schedule reviewing and 235-239 evaluating process. This is achieved by introducing a clear, arranged and classified evaluation metrics to let the schedule reviewerto list down all the schedule technical defects and convert them into clear comments in order to rectify them and having a schedule free of defects
Keywords: Baseline Evaluation, Baseline assessment, Schedule evaluation metrics.
References: 1. J. M. Framinan and R. Leisten, Overview of Scheduling Systems, no. May 2016. 2014. 2. T. Seymour and S. Hussein, “The History Of Project Management,” Int. J. Manag. Inf. Syst., vol. 18, no. 4, p. 233, 2014. 3. TMOGPI, “Critical path method, Turnaround Management for the Oil, Gas, and Process Industries.,” Turnaround Manag. Oil, Gas, Process Ind., pp. 299–332, 2019. 4. J. W. Chinneck, “Chapter 11 ; PERT for Project Planning and Scheduling,” Syst. Comput. Eng. Carlet. Univ., pp. 1–11, 2016. 5. A. Tomar and V. K. Bansal, “Scheduling of repetitive construction projects using geographic information systems: an integration of critical path method and line of balance,” Asian J. Civ. Eng., vol. 20, no. 4, pp. 549–562, 2019. 6. FAO, “Overview of methods for baseline assessments ; M&E Technical Advisory Notes Series,” M&E Tech. Advis. Notes Ser., 2017. 7. N. Braimah, “Construction Delay Analysis Techniques—A Review of Application Issues and Improvement Needs,” Buildings, vol. 3, no. 3, pp. 506–531, 2013. 8. K. P. L. Hoshino, “Forensic Schedule Analysis; AACE® International Recommended Practice No. 29R-03 FORENSIC,” AACE® Int. Recomm. Pract. No. 29R-03 FORENSIC, no. 29, pp. 1–135, 2011. 9. [9] R. Winter, “Reviewing a Baseline Schedule,” 2014. 10. GAO, “Schedule Assessment Guide,” no. December, 2015. 11. DCMA, “Earned Value Management System ( EVMS ) Program Analysis Pamphlet ( PAP ),” no. October, 2012. 12. NDIA, “Planning & Scheduling Excellence Guide ( PASEG ),” 2012. 13. C. W. Foster and A. Avalon, “Schedule Quality Assurance Procedures,” AACE, no. 303, 2010. 14. PMI, “Practice Standard for Scheduling,” Proj. Manag. Inst., p. 113, 2007. 15. NAVAIR, “Integrated Master Schedule (IMS) Guidebook,” Nav. Air Syst. Command, no. February, 2010. Authors: Samridha M, Akshar Chawla, Shagnik Roy, Neharidha M
Paper Title: Semantic Network Abstract: Knowledge representation is an emerging field of research in Artificial Intelligence, Big data analytics, Semantic web, and Data Mining. Knowledge represented in an effective way helps in easy traversal, searching, reasoning, prediction, and inference. There are number of approaches, algorithms, techniques, and models that have been proposed for the same. Every approach has their own pros and cons. Hence our aim is to propose a simple and extremely effective way to represent knowledge, which has a greater expressiveness compared to logic and reverberates within the methods of people process data. In this paper we address the implementation of semantics network with simple yet powerful method, which despite the vague nature of English language, proves to give accurate results. The patterns discovered in sentences are listed in a defined order throughout the paper. The usage of Natural Language Toolkit and Posing tagging simplifies the most crucial task of tagging a word with its part of speech.
Keywords: Clauses, Natural Language Toolkit, NetworkX, Ontology, Pos tagging. 42. References: 1. Matthew Huntbach, Artificial Intelligence I, Queen Mary and Westfield 240-243 College,London.Available:https://www.coursehero.com/file/10092029/AINotes4/ 2. Atta ur Rahman, Knowledge Representation: A Semantic Network Approach, Handbook of Research on Computational Intelligence Applications in Bioinformatics, June 2016. 3. Nils J Nilsson, Artificial Intelligence: A New Synthesis, 1998. 4. Steve Bird, NLTK: the Natural Language Toolkit, TMTNLP '02: Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1. 5. Nyein PyaePyaeKhin, Analyzing Tagging Accuracy of Part-of-speech Taggers, International Conference on Genetic and Evolutionary Computing, September 2015. 6. Yuan Tian and David Lo, A Comparative Study on the Effectiveness of Part-Of-Speech Tagging Techniques on Bug Report, School of Information systems, Singapore Management University, Singapore. 7. Aric A. Hagberg, Daniel A. Schult and Pieter J. swart, “Exploring network structure, dynamics, and function using NetworkX”, in Proceedings of the 7th Python in Science Conference (SciPy2008), GäelVaroquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15, Aug 2008. 8. Cambridge Dictionary, Cambridge university press, 2020. 9. Steve Bird, Ewan Klein and Edward Loper, “Natural Language Processing with Python”, Chapter 5. 10. Dr. S.Vijayarani and Ms. R.Janani, “TextMining: Open Source Tokenization Tools-An Analysis”, Advanced Computational Intelligence: An International Journal (ACII), Vol3, No.1, January 2016. Authors: Shanmuga Skandh Vinayak E, Shahina A, Nayeemulla Khan A
Paper Title: Dementia Prediction on OASIS Dataset using Supervised and Ensemble Learning Techniques Abstract: The Magnetic Resonance Imaging (MRI) data, which are a prevalent source of insight in understanding the inner functioning of the human body is one of the most preliminarymechanisms in the analysis of the human brain, including and not limited to detecting the presence of dementia. In this article, 7 machine learning models are proposed in the analysis and detection of dementiain the subjects ofOpen Access Series of Imaging Studies(OASIS) Brains 1, using OASIS 2 MRI and demographic data. The article also compares the performances of the machine learning models in terms of accuracy and prediction duration. The proposed model, 43. eXtreme Gradient Boosting (XGB) algorithm performs with the highest accuracy of 97.87% and the fastest prediction durationof 0.031s/sample. 244-254 Keywords: Dementia, detection, Machine Learning, Algorithms, OASIS, feature selection, dimension reduction.
References: 1. John Elflein, Statista, Sep 24, 2019. Accessed on: November 24, 2019. [Online]. Available: www.statista.com/statistics/264951/number- of-people-with-dementia-from-2010-to-2050 2. C. Greenblat, World Health Organization, Sep 19, 2019. [Online]. Available: https://www.who.int/news-room/fact- sheets/detail/dementia 3. M. Clarke, M.D.&J. W. Swanson, M.D., Mayo Foundation for Medical Education and Research, April 19, 2019. [Online]. Available: https://www.mayoclinic.org/diseases-conditions/dementia/symptoms-causes/syc-20352013 4. OASIS: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498 5. OASIS: Cross-Sectional: Principal Investigators: R. D. Marcus, J. Buckner& C. J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. 6. OASIS: Longitudinal: https://doi.org/10.1162/jocn.2009.21407 7. OASIS: Longitudinal: Principal Investigators: R. D. Marcus, J. Buckner& C. J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. 8. Stef van Buuren . Multivariate Imputation by Chained Equations. Version 3.8.0. February 21, 2020. [Online]. Available: https://github.com/stefvanbuuren/mice 9. D.Bansal, R. Chhikara, K. Khanna &P. Gupta. Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia. Procedia Computer Science, 132, 1497–1502, 2018. doi:10.1016/j.procs.2018.05.102 10. Y. Zhang, S. Wang &Z. Dong.Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree. Progress In Electromagnetics Research, Vol. 144, 171-184, 2014. doi:10.2528/PIER13121310 11. C. Naidu, D. Kumar, N. Maheswari, M. Sivagami& G. Li.Prediction of Alzheimer’s Disease using Oasis Dataset.International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7 Issue-6S3, April 2019. 12. P. Garrard, V. Rentoumi, B. Gesierich, B. Miller & M.L. Gorno-Tempini. Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex, 55, 122–129,2014.doi:10.1016/j.cortex.2013.05.008 13. T. Chen, A. Rangarajan &B. C. Vemuri. Caviar: Classification via aggregated regression and its application in classifying oasis brain database.IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010.doi:10.1109/isbi.2010.5490244 14. T. R. SivapriyaA. R. N. B. Kamal&V. Thavavel. Automated Classification of Dementia Using PSO based Least Square Support Vector Machine. International Journal of Machine Learning and Computing, Vol. 3, No. 2, April 2013. 15. G. Battineni, N. Chintalapudi &F. Amenta. Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Informatics in Medicine Unlocked, 100200, 2019. doi:10.1016/j.imu.2019.100200 16. B. A.Ardekani, E. Bermudez,A. M.Mubeen &A. H. Bachman. Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment. Journal of Alzheimer’s Disease, 55(1), 269–281, 2016. doi:10.3233/jad-160594 Authors: Vivek Soni, M. P. Verma
Paper Title: Design and Evaluation of Residential Building Alongwith Floating Column Abstract: In the epoch of Construction multi-storied building with floating column plays a serious role in urban areas of India. These floating columns are mainly used for justifying the space availability within the construction and to urge good architectural view of the building. A residential high -rise building consisting of G+7 has been chosen for polishing off project work. The work was disbursed considering different cases of removal of columns in several positions and in various floors of the building. The building models are designed by using the software E-TABs 2018 and models of buildings are analyze and refined followed by IS 456-2000 guidelines.
Keywords: Floating columns, G+7, E-TABs, RCC frames, Building Evaluation, Building design, Shear force. 44. References: 1. Bureau of Indian Standards: IS-875, Part II (1987), Live Loads on Buildings and Structures, New Delhi, India. 255-258 2. Bureau of Indian Standards: IS-875, Part III (1987), 3. Wind Loads on Buildings and Structures, New Delhi, India. 4. IS 456-2000 Plain and Reinforced Concrete code. 5. BadgireUdhav S and Shaikh A.N, “Analysis ofMultistorey Building with Floating Column”, Volume no.4, Issue No. 9, 01 Sept. 2015, pp: 475-478. 6. Sasidhar T and P. Sai Avinash, “Analysis of MultistoriedBuilding with and without Floating Column Using ETABs”,Volume 8, Issue 6, June 2017, pp:91-98. Shivam Tyagi and B.S. Tyagi, “Seismic Analysis ofMultistorey Building with Floating Column”, Volume 5, Issue No. 5, May 2018. 7. MD Najeeb Ur Rahman and B Rajkumar Singh, “Analysisof Multi-storey Building with Floating Column”, Volume6, Issue No. 01, January-June 2018. 8. P. Pavan Kumar and D. Thrimurthi Naik, “Design andAnalysis of Residential Building with Floating ColumnsBy Considering Footing Design”, Volume 2, Issue No. 6, June 2017. 9. Bureau of Indian Standards: IS-875, Part I (1987), DeadLoads on Buildings and Structures, New Delhi, India Authors: Juhi K. Patgiri, Arindam Mondal, Gitanjali Kaman, Pinki Deori, Vinayak Majhi, Sudip Paul
Paper Title: Significant Contribution in Healthcare by using IoT Abstract: Nowadays, the heart-related disease is rapidly rising. Most patients, in some cases, may not identify their health condition. Even in rural areas, doctors are not available 24x7. Due to the advancement of modern technology, these diseases can be detected early and can be treated on time. So, the developed device can continuously track the patient's heartbeat and temperature, and the recorded data can be sent to the concerned doctor so that treatment can be provided to the patient. Here the IoT (Internet of Things) is being used for monitoring the patient's health status and send wirelessly to the IoT server. This device can also be monitored 45. from remote places. This device is mainly helpful for aged and disabled people who find it difficult to go to doctors daily or for patients who need continuous monitoring of their health status. The designed hardware will 259-264 capture the real-time heartrate and temperature value and send the data to the concern IoT server using any mobile or Wi-Fi network having with internet facility. Before sending the data the preprocessing is being done by the attached microcontroller in the respective sensors module. The value sent in the IoT server will be used for generating the online graph through the Application Program Interface (API) that are used in developing the web and mobile application. In this system the authenticated user can view the output trough developed mobile application web application.
Keywords: Internet of Things (IoT), Human Heart Rate, Body Temperature, ThingSpeak, Mobile Application.
References: 1. Booth, F.W., C.K. Roberts, and M.J. Laye, Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology, 2012. 2(2): p. 1143-1211. 2. Abdelgawad, A., K. Yelamarthi, and A. Khattab. IoT-based health monitoring system for active and assisted living. in International Conference on Smart Objects and Technologies for Social Good. 2016. Springer. 3. Usak, M., et al., Health care service delivery based on the Internet of things: A systematic and comprehensive study. International Journal of Communication Systems, 2020. 33(2): p. e4179. 4. Patil, S. and S. Pardeshi, Health monitoring system using IoT. Int. Res. J. Eng. Technol.(IRJET), 2018. 5(04). 5. Julius, A. and Z. Jian-Min, IoT Based Patient Health Monitoring System Using Lab VIEW and Wireless Sensor Network. International Journal of Science and Research (IJSR), 2017. 6(3). 6. Baudenbacher, F., et al., Smart mobile health monitoring system and related methods. 2015, Google Patents. 7. Hariman, R.J., et al., Method for recording electrical activity of the sinoatrial node and automatic atrial foci during cardiac catheterization in human subjects. The American journal of cardiology, 1980. 45(4): p. 775-781. 8. Wagner, G.S., Marriott's practical electrocardiography. 2001: Lippincott Williams & Wilkins. 9. Grant, R.P., Clinical electrocardiography. Academic Medicine, 1958. 33(3): p. 242. 10. Madl, T. Network analysis of heart beat intervals using horizontal visibility graphs. in 2016 Computing in Cardiology Conference (CinC). 2016. IEEE. 11. Liu, C., et al. The application of soil temperature measurement by LM35 temperature sensors. in Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. 2011. IEEE. 12. Pasha, S., ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis. International Journal of New Technology and Research, 2016. 2(6). 13. Briginets, S., et al. Development of a mobile heart monitor based on the ECG module AD8232. in AIP Conference Proceedings. 2018. AIP Publishing LLC. 14. Kakiuchi, T., T. Yoshimatsu, and N. Nishi, New class of Ag/AgCl electrodes based on hydrophobic ionic liquid saturated with AgCl. Analytical chemistry, 2007. 79(18): p. 7187-7191. 15. Kumar, R. and M.P. Rajasekaran. An IoT based patient monitoring system using raspberry Pi. in 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16). 2016. IEEE. 16. Patton, E.W., M. Tissenbaum, and F. Harunani, MIT App Inventor: Objectives, Design, and Development, in Computational Thinking Education, S.-C. Kong and H. Abelson, Editors. 2019, Springer Singapore: Singapore. p. 31-49. 17. Ye, C., M.T. Coimbra, and B.V. Kumar. Investigation of human identification using two-lead electrocardiogram (ECG) signals. in 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS). 2010. IEEE. 18. Badamasi, Y.A. The working principle of an Arduino. in 2014 11th international conference on electronics, computer and computation (ICECCO). 2014. IEEE. 19. Coorporation, A., Atmel atmega328p datasheet. Accessed: Apr, 2011. 26: p. 2018. Authors: Poonam Sawant, Abhijeet Kaiwade
Paper Title: Automatic Ratings Generation System for Behavior Analysis Abstract: User Behavior Analysis plays a pivotal role in finding the behavior of an individual regarding any certain business or social issue. It is helping a business to take an appropriate decision for improvementby understanding customers’ opinion about their products or services in a positive or negative way. Although there are many systems have been developed and implemented till date for performing behavior analysis in different ways; still better advancement is needed due to the nature of the data. In this paper, we have developed a system to generate automatic ratings based on the commentsgiven by the banking customers on social networking sites. These ratings then further analyzed to find out positive and negative behavior. PIG ETL tool on the top of MapReduce is used to develop and implement a proposed system and performing the analysis. AFINNsentiment lexicon is used to generate automatic ratings whereasvisualization is done using D3.js. Performance evaluation of the proposed system is done by comparing it with the existing system.
Keywords: Behavior Analysis, AFINN, Pig, Hadoop, MapReduce, D3.js, Positive and Negative Polarity.
References: 46. 1. Joseph O. Chan (2013), An Architecture for Big Data Analytics, communications of the IIMA, Volume 13 Issue 2 2. Vignesh Prajapati (2013), Big Data Analytics with R and Hadoop, PACKT,ISBN-978-1-78216-328-2 3. Tom White (2014), Hadoop The Definitive Guide, O’REILY, ISBN 13:978-93-5023-756-4 265-272 4. Nada Elgendy, Ahmed Elragal (2014),Big Data Analytics: A Literature Review Paper, ICDM, LNAI 8557, 214-227 5. Utkarsh Shrivastava, Santosh Gopalkrishnan (2015) Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks, Procedia Computer Science, Elsevier ScienceDirect, 643-652 6. J. Ramsingh, Dr. V. Bhuvaneswari (2015), An Insight on Big Data Analytics Using Pig Script, IJETTCS, Volume 4, Issue 6, ISSN 2278-6856 7. Vikhyat Gupta, Tarik Taeib (2015), Analyzing User Behavior Using MapReduce, JMEST, Volume 2, Issue 3, ISSN 3159-0040,384- 387 8. MapReduce vs. Pig vs. Hive - Comparison between the key tools of Hadoop, 01,sept 2015, https://www.dezyre.com/article/mapreduce-vs-pig-vs-hive/163 9. Subramaniya swamy V, Vijayakumar V, LogeshRc and Indragandhi V (2015), Unstructured Data Analysis on Big Data using Map Reduce, Procedia Computer Science, 456 – 465 10. MohdRehan Ghazi, DurgaprasadGangodkar(2015), Hadoop, MapReduce and HDFS: A Developers Perspective, Elsevier ScienceDirect, 45 – 50 11. Rajendra Akerkar, Sajja(2016), Intelligent Techniques for Data Science, Springer, ISBN 978-3-319-29205-2 12. Anindita A Khade (2016), Performing Customer Behavior Analysis using Big Data, Procedia Computer Science, ElsevierScienceDirect, 986-992 13. A. Shrivastva, Chandan Kumar, Neha Mangla (2016), Analysis of Diabetic Dataset and Developing Prediction Model by using Hive and R, Indian Journal of science and Technology, Vol 9(47), ISSN 0974-6846 14. Poonam Sawant, Dr. Abhijeet Kaiwade, Dr. S.D. Mundhe(2018), Pig: Novel Approach to Analyze Customer Behavior in Banking Authors: P.ParabrahmaSai, K.Lakshmi Prasad, P.Ravindra Kumar, K. Srinivasa Rao 47. Experimental Study of Heat Transfer Rate in Single and Series Cross Flow Heat Exchanger using Paper Title: Matlab Coding Abstract: The present study investigates the heat transfer rate and effectiveness of cross flow heat exchanger by varying velocity and mass flow rate of the cold and hot fluids.The velocity of the cold fluid i.e. air varying from 15m/s to 30m/s(with an intermediate step of 20m/s and 25m/s) whereas the mass flow rate of hot fluid i.e. water is taken as 35lit/hr and 84lit/hr. The logarithmic mean temperature difference (LMTD) method is used to find the heat transfer rate. In thepresent workthe effectiveness and heat transfer rate werecompared between the single and series cross flow heat exchangers. The result shows an average increase of 47.14% and 59.59% of heat transfer rate analogous to mass flow rate of 84lit/hrand 35lit/hr
Keywords: Cross flow Heat exchanger, Effectiveness, Heat transfer rate, Logarithmic mean temperature difference.
References: 1. Mahoumad Khaled, Mohamad Ramadan, Hicham EI Hage“Innovative approach of determining the overall heat transfer coefficient of heat exchangers” ,Applied Thermal Engineering99(2016) 1086-1092 2. KhitamFuaadAefan, Zainab Hakim Malik, RaadAwad Abdul Hussain, “Design, Fabrication and Testing of Cross Flow Heat Exchnager”,Department Of Mechanical Engineering, University of Al-Qadisiyah (2018) 3. Karthik Silaipillayarputhur, Tawfiq AIMughanam,“Performance Charts for Multipass Parallel Cross Flow Heat Exchangers”, International Journal of Mechanical Engineering and Robotics Research Vol. 7 (2018) 4. S.D.Chavhan, N.S.Gohel, R.S. Jha, ”Thermal Hydraulic Performance Of Elliptical Shape Staggered Tube Cross Flow Heat Exchanger At 45° Angle of Attack”,Inetrnational journal of current engineering and technology(2016) 2347 – 5161. 5. ChadRandall Harris, “Design, Fabrication And Testing of Cross Flow Micro Heat Exchangers”, Louisiana State University and Agricultural & Mechanical College (2001). 6. 6.Shung When Kang, Shin Chau Tseng,”Analysis of Effectiveness and Pressure Drop In Micro Cross Flow Heat Exchanger”, Applied Thermal Engineering 27 (2007) 877-885. 7. H. ingimundardottir, S. lalot, ”Detection of Fouling In A Cross Flow Heat Exchanger Using Wavelets”, Proceding of International 273-280 Conference on Heat Exchanger Fouling and Cleaning Vlll-2009(peer reviewed). 8. A.s Krishnan and P.Gowtham, ’’Computational Study of Staggered and Double Cross Flow Heat Exchanger”, Defence Science Journal, vol 67 (2017). 9. Luben Cabezas- Gomez, HelioAparecido Navarro, Jose Maria Saiz-Jabardo, ”Thermal Performance of MultipassParallel and Counter Cross Flow Heat Exchanger’’, Journal of Heat Transfer vol 129 (2007). 10. 10.Jiangfeng Guo, XiulanHuai, “Coordination Analysis of Cross flow Heat Exchnager under High Variations in Thermodynamic Properties”, International Journal of Heat and Mass Transfer 113 (2017) 935-942. 11. X.jLuo, ”Parametric Study of Heat Transfer Enhancement on Cross Flow Heat Exchangers”, Chemical Engineering and Processing (2017). 12. Dr. Sadiq Elias Abdullah, ”Investigation the Performance of Cross Flow Heat Exchanger”, International Journal of Science and Research, volume 5 Issue 4, (2016). 13. Abhishek Bhandegaonkar, N.S.Gohel, ”Experimental Investigation of Cross Flow Heat Exchanger with Staggered Tube Arrangement”, International Engineering Research Journal, Special Edition Pgcon Mech-2017 14. Tisha Dixit, Indranil Ghosh, “Two Stream Cross Flow Heat Exchangers in Thermal Communication with The A Surroundings Generalized analysis, International Journal of Heat and Mass Transfer 66 (2013) 1-9. 15. Tianyi Gao, Bahgat G Sammakia, James F. Geer, ”Dynamic Analysis of Cross Flow Heat Exchangers in Data Centers Using Transient Effectiveness Method”, IEEE Transactions on Components, and Manufacturing Technology, vol 4 No 12 (2014). 16. CheenSu An, Man-Hoe Kim, ”Thermo Hydraulic Analysis of Cross Flow Heat Exchangers”, International Journal of Heat and Mass Transfer, 120 (2018) 534-539. 17. S. Toolthaisong, N.Kasayapand, ”Effect of Attack Angles on Air Side Thermal and Pressure Drop of the Cross Flow Heat Exchangers With Staggered tube arrangement”, Sciverse ScienceDirect Energy Procedia 34 (2013) 417-429 18. Anwar Sadath, Harish N.Dixit, C.P. Vyasaraynai, ”Dynamics of Cross Flow Heat Exchanger Tubes with Loose Supports” Journal of Pressure of Vessel Technology, vol 138/051303-1 (2016). 19. W.A Khan, ”Optimal design of Tube Banks In Cross Flow Using Entropy Generation Minimization Method”, Journal of Thermodynamics and Heat Transfer, vol 21 (2007). 20. Mansour NasiriKhalaji, Isak Kotcioglu, Sinan Caliskan, Ahmet Cansiz, ”The Second Law Analysis of Thermodynamics for the Plate Fin Surface Performance in a Cross Flow Heat Exchanger”, Journal of Heat Transfer, (2018). Authors: Rajashree V Biradar, Anita Patil
Paper Title: Priority Arbiter for TinyOS: The need of renown OS for WSN and IOT. Abstract: In the present technical era, we are extremely dependent on technological applications such as internet, multimedia, social media, home automation, industrial automation, medical instrumentation, web technology so on and so forth. Moreover, as a backbone such applications are supporting the research related to science and technology in turn. There are certain technologies for example Wireless Sensor Network, IOTs, Artificial Intelligence, and Cloud Computing etc., working behind these applications as unseen hands. Nowadays in all these facilities, there is much more advancement and high demand for real-time applications to serve interactive services. Such necessities enforce the technologies to upgrade themselves to their next level. As such, in WSN, the existing Operating Systems also should upgrade in-terms of different concerns such as 48. memory management, scheduling techniques, power supply scarcity issues and overall efficient utilization of available limited resources. In this regard, here is an attempt to improvise TinyOS, which is the popular OS for 281-286 WSN and IOT. The survey on WSN applications reveals, what sort of improvisations are necessary to fulfill the requirements of varieties of applications. In that direction, for more efficient scheduling of tasks based on the situation, new technique is required. Being the best OS for low power devices of WSN and IOT, TinyOS hinders to support many application those need different type of scheduling than FCFS, which is the only scheduling technique for TinyOS. Hence, Integration of new Priority arbiter as a first step of main scheduler improvisation is the essence of this paper
Keywords: Operating Systems for WSN, Applications of WSN, Scheduling techniques, Scheduling in TinyOS, Operating systems for IOT.
References: 1. Roberto Rodriguez-Zurrunero , Ramiro Utrilla , Alba Rozas and Alvaro Araujo, “Process Management in IoT Operating Systems:Cross-Influence between Processing and Communication Tasks in End-Devices”. Sensors 2019, 19, 805; doi:10.3390/s19040805 www.mdpi.com/journal/sensors Published: 16 February 2019 2. Rebin B Khoshnaw¹, Dana Farhad Doghramachi, Mazin S. Al-Hakeem, “A Review on Internet of Things’ Operating Systems, Platforms and Applications”, Conference Paper • February 2017 DOI:10.23918/iec2017.06 3. Hicham Aberbach, Sabri Abdelouahed, Adil Jeghal, H. Tairi, “A Comparative Study between Operating Systems (Os) for the Internet of Things (IoT)”, DOI: 10.14738/tmlai.54.3192 Publication Date: 15th August 2017 URL: http://dx.doi.org/10.14738/tmlai.54.3192 4. Arslan Musaddiq1, Yousaf Bin Zikria1, (Senior Member, Ieee), Oliver Hahm2, Heejung Yu1, Ali Kashif Bashir 3, (Senior Member, Ieee), And Sung Won Kim , “ A Survey on Resource Management in IoT Operating Systems”, IEEEAccess, publication February 21, 2018, date of current version March 12, 2018. DOI 10.1109/ACCESS.2018.2808324 5. Salahuddin M. ElKazak, Cairo University, Masters in Computer Engineering, “GEN600 Final Technical Report:Research in Internet of Things' Operating Systems (IoT OS's)”, Research in IoT OS's, GEN600: Final Technical Report 6. Anita Patil (1), Dr: Rajashree.V.Biradar(2), “Comparative study of Operating Systems for Wireless Sensor Networks”,NCRTCSE- 12, pages 232-242 7. Muhammad Amjad, Muhammad Sharif, Muhammad Khalil Afzal, and Sung Won Kim, “TinyOS-New Trends, ComparativeViews, and Supported Sensing Applications: A Review”, IEEE SENSORS JOURNAL, VOL. 16, NO. 9, MAY 1, 2016 8. Waltenegus Dargie, Christian Poellabauer, Book, “Fundamentals of wireless sensor networks theory and practice”,2010 John Wiley & Sons Ltd. 9. Adi Mallikarjuna Reddy V AVU Phani Kumar, D Janakiram, and G Ashok Kumar, “Operating Systems for Wireless Sensor Networks: A Survey Technical Report”, May 3, 2007 pages 1-30 10. K. Dwivedi, M. K. Tiwari, O. P. Vyas, “Operating Systems for Tiny Networked Sensors: A Survey”, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009, pages 153-15 11. W. Dong, C. Chen, X. Liu, and J. Bu, “Providing OS support for wireless sensor networks: Challenges and approaches”, IEEE Commun.Surveys Tuts., vol. 12, no. 4, pp.519–530, Nov. 2010. 12. Muhammad Omer Farooq and Thomas Kunz,Article, “ Operating Systems for Wireless Sensor Networks: A Survey”, Sensors 2011, 11, 5900-5930; doi:10.3390/s110605900. 13. Yousaf Bin Zikria , Sung Won Kim , Oliver Hahm , Muhammad Khalil Afzal and Mohammed Y. Aalsalem , “Internet of Things (IoT) Operating Systems Management: Opportunities, Challenges, and Solution”, Sensors 2019, 19, 1793; doi:10.3390/s19081793 www.mdpi.com/journal/sensors 14. Farhana Javed, Muhamamd Khalil Afzal, Muhammad Sharif and Byung-Seo Kim, “Internet of Things (IoTs) Operating Systems Support, Networking Technologies, Applications and Challenges: A Comparative Review”, 1553-877X (c) 2018 IEEE 15. Roberto Rodriguez-Zurrunero, Ramiro Utrilla, Elena Romero, and Alvaro Araujo, Research Article-“An Adaptive Scheduler for Real- Time Operating Systems to Extend WSN Nodes Lifetime”, Hindawi, Wireless Communications and Mobile Computing. Volume 2018, Article ID 4185650, 10 pages. https://doi.org/10.1155/2018/4185650 16. Anita Patil, Rajashree Biradar, “Scheduling Techniques for TinyOS: A Review”, DOI: 10.1109/CSITSS.2016.7779420 Publisher: IEEE, Date of Conference: 6-8 Oct. 2016 17. https://en.wikipedia.org/wiki/TinyOS 18. https://en.wikipedia.org/wiki/List_of_wireless_sensor_nodes 19. http://tinyos.stanford.edu/tinyos-wiki/index.php/MSPSim 20. https://en.wikipedia.org/wiki/NesC 21. http://tinyos.stanford.edu/tinyos-wiki/index.php/TEPs 22. https://github.com 23. https://stackoverflow.com 24. http://www.tinyos.net Authors: M.Subramanian, B.Aravinth
Paper Title: Effect on the Mechanical Properties of Al 7075 Reinforced with SiC and TiC Particles Abstract: In the present industrial scenario, Aluminium and its alloy based composites have more importance in the growing fields of engineering. Aluminium reinforced metal matrix composites are broadly speaking desired because it has the excessive strenth along with less weight, hardness, corrosion resistance, fatigue and creep resistance. The Al composites are focused to use in aerospace, automobile and also in structural domain because it gives good strength with less weight. This paper discussed about the mechanical residences of Aluminium 7075 alloy strengthened with SiC and TiC. Stir casting process was utilized for fabrication of composites and composite specimens are subjected to tensile test by using Universal Testing Machine. The composite hardness was tested by using Brinell hardness tester and the Charpy impact tester used for findings the impact strength. An experimental results are compared with unreinforced alloy of Al 7075. Micro structural characterization confirms the particles of reinforcement are distributed to the entire structure of matrix. The experimental result shows the mechanical properties slightly increased by varying wt% of reinforcements in the matrix material. The better tensile strength (252MPa), hardness (83HB) and impact strength (4.6 Joules) is 49. obtained by the composition of 60% wt of Al 7075, 20% wt of TiC and 20 %wt of SiC. 287-290 Keywords: Al 7075, Hardness, SiC and Stir Casting.
References: 1. A. R. K. Swamy, A. Ramesh, G.B. Veeresh Kumar, J. N. Prakash “Effect of Particulate Reinforcements on the Mechanical Properties of Al6061-WC and Al6061-Gr MMCs”, Journal of Minerals & Materials Characterization & Engineering, Vol. 10, No.12,(2011), pp.1141-1152. 2. A. Ramesh, J. N. Prakash, A. S. Shiva ShankareGowda and SonnappaAppaiah. “Comparison of the Mechanical Properties of Al6061/Albite and Al6061/Graphite Metal Matrix Composites”, Journal of Minerals & Materials Characterization & Engineering, Vol. 8, No.2,(2009), pp 93-106. 3. Ankesh Kumar, Study of Physical, Mechanical and Machinability Properties of Aluminium Metal Matrix Composite Reinforced with Coconut Shell Ash particulates, Imperial Journal of Interdisciplinary Research, Vol-2, Issue-5, 2016 ISSN: 2454-1362. 4. Baradeswaran, A., ElayaPerumal, A., (2014). Study on mechanical and wear properties of Al 7075/Al2O3/graphite hybrid composites. Composites Part B: Engineering 56(0): 464-471. 5. Deepak Singla, Evaluation of Mechanical Properties of Al 7075-Fly Ash Composite Material, International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 4, April 2013. 6. Efzan MNE, Syazwani NS, Abdullah MMAB, Microstructure and mechanical properties of fly ash particulate reinforced in LM6 for energy enhancement in automotive applications, IOP Conference Series, Materials Science Engineering, 133, 2016, 1-12. 7. Feng, A.H., Xiao, B.L. and Ma, Z.Y. “Effect of microstructure evolution on mechanical properties of friction stir welded AA2009S/SiCp composite”, Compos. Sci. Technol., 68(9), 2008, 2141-2148 8. I.Mobasherpour, A.A. Tofigh and M. Ebrahimi , ‘Effect of nano-sizeAl2O3 reinforcement on the mechanical behavior of synthesis 7075aluminum alloy composites by mechanical alloying’, Mat. Che. and Phy., Vol. 138, pp.535-541, 2013. 9. Kumar GBV, Rao CSP, Selvaraj N, Bhagyashekar MS (2010) Studies on Al6061-SiC and Al7075-Al2O3 Metal Matrix Composites. Journal of Minerals and Materials Characterization and Engineering, pp 43-55. 10. L.Ceschini, “Tensile and fatigue properties of the AA6061/20% volume Al2O3p and AA7005/10% volume Al2O3p composites”, Composites Science and Technology 66 (2006) 333–342. 11. Lal Krishna, Development of Silicon Carbide Reinforced Aluminium Metal Matrix composite For Hydraulic Actuator In Space Applications, International Journal of Research in Engineering & Technology, ISSN(E): 2321-8843; ISSN(P): 2347-4599,Vol. 3, Issue 8, Aug 2015, 41-50. 12. P. Muruganandhan, M. Eswaramoorthi, and K. Kannakumar, “Aluminium fly ash composite – an experimental study with mechanical properties perspective”, Int. J. Eng. Res., vol. 3, pp.78-83, 2015. 13. Prashant Kumar Suragimath and Dr. G.K. Purohit , ‘A Study on Mechanical Properties of Aluminium Alloy (LM6) Reinforced with SiC and Fly Ash’, IOSR J. of Mech. and Civil Eng. , Vol. 8, No.5, pp.13-18, 2013. 14. Ramesh CS, Pramod S, Keshavamurthy R. A study on microstructure and mechanical properties of Al 6061-TiB2 in-situ composites. Materials Science and Engineering A. 528, 2011, 4125–32. 15. Ramesh, C.S., Keshavamurthy, R., Chennabasappa, B.H., and Abrar Ahmad. “Microstructure and mechanical properties of Ni-P coated Si3N4 reinforced Al6061 composites”, Mater. Sci. Eng., A 502(2), 2009, 99-106. 16. S.K.Ravesh, and T.K. Garg, “Preparation & analysis for some mechanical property of aluminium based metal matrix composite reinforced with SiC& fly ash”, Int. J.Eng. Res. Appl., vol. 2, pp.727– 731, 2012. 17. S.V. Prasad, “Aluminum metal–matrix composites for automotive applications: tribological considerations”, Tribology Letters, Vol. 17, No. 3, October 2004. 18. Selvam JDR, Smart DSR, Dinaharan I, Microstructure and some mechanical properties of fly ash particulate reinforced AA6061 aluminum alloy composites prepared by compocasting, Materials and Design, 49, 2013, 28-34. 19. T.V.Christy, N.Murugan and S.Kumar, “A Comparative Study on the Microstructures and Mechanical Properties of Al 6061 Alloy and the MMC Al 6061/TiB2/12P”, Journal of Minerals & Materials Characterization & Engineering, 2010 Vol. 9, No.1, pp.57-65. 20. V. Balaji, N. Sateesh and M. ‘Manufacture of Aluminium Metal Matrix Composite (Al7075-SiC) by Stir Casting Technique’, Mat. Today Pro. Vol. 2, pp.3403 – 3408, 2015. 21. V. Balaji, N. Sateesh and M. Manzoor Hussain , ‘Manufacture of Aluminium Metal Matrix Composite (Al7075-SiC) by Stir Casting Technique’, Mat. Today Pro. Vol. 2, pp.3403 – 3408, 2015. 22. Viney Kumar ,Rahul Dev Gupta and N.K. Batrab , ‘Comparison of Mechanical Properties and effect of sliding velocity on wear properties of Al 6061, Mg 4%, Fly ash and Al 6061, Mg 4%, Graphite 4%, Fly ash Hybrid Metal matrix composite’, Pro. Mat. Sci., Vol. 6, pp.1365 – 1375, 2014. 23. Zhang, H., He, Y., Li, L., (2008). Tensile deformation and fracture behavior of spray-deposition 7075/15SiCp aluminum matrix composite sheet at elevated temperatures. Materials Characterization 59(8): 1078-1082. 24. M. Nataraj and P. Ramesh, (Dec 2016) “Experimental Study on the Mechanical Properties of Aluminum based Hybrid Metal Matrix Composite”, International Journal of Printing, Packaging & Allied Sciences Research, Vol. 4 No. 5 pp. 3431-3438, ISSN: 2320-4387 25. M. Nataraj and P. Ramesh, (June 2016) “Investigation on Machining Characteristics of Al 6061 Hybrid Metal Matrix Composite Using Electrical Discharge Machine”, Middle-East Journal of Scientific Research, Vol. 24 No. 6 pp. 1932-1940, ISSN: 1990-9233. 26. Ramesh P and Nataraj M (August 2018) “Automotive industry application of Aluminium based hybrid metal matrix composite”, International Journal of Heavy Vehicle Systems, Accepted, ISSN: 1741-5152 Authors: Heru Ismanto, Abner Doloksaribu, Diana Sri Susanti The Design of a Land Suitability Model on Rice Production Estimation using Remote Sensing Paper Title: Method in Merauke District Papua Abstract: The area of agricultural land in Merauke Regency according to data from Bappeda (Agency for Regional Development) of Merauke Regency is for about 4.6 million hectares in 2015 and within the next 5 years will be cultivated as much as 1.2 hectares, especially for rice field and secondary crops plants [1].This study has purpose to estimate the value of rice production by using dry-milled rice with a land suitability approach using decision tree analysis and remote sensing methods in Merauke Regency. Remote sensing that has been corrected geometrically and radio-metrically is analyzed by using decision tree analysis to derive information on paddy/rice and non-rice field land use and which is reclassified by using field unit information based on field observations which will result in accuracy of use and producer. The rice field data is then processed to derive information on the rotation pattern of rice by using a decision tree analysis that is using input data on land characteristics which will produce total accuracy. Information on rice field area and rotation patterns are complemented by land productivity (tons / ha) sourced from BPS (Central Statistics Agency) data and interviews with land cultivators and local residents (farmers) were used to calculate the total rice production value on dry-milled rice The results of calculations by using this method are expected to have a fairly large 50. surplus of calculations, both data from BPS (Central Bureau of Statistics) and data from interviews with land cultivators and local residents (farmers). Thus, these results are expected to show that the land suitability 291-295 approach by using remote sensing methods for estimating rice production can be used to produce information on rice field area and rotation patterns with moderate to high accuracy.
Keywords: Rice production, land suitability, decision tree, remote sensing, Merauke Papua.
References: 1. BPS Kabupaten Merauke (2019). Kabupaten Merauke Dalam Angka 2019. Kabupaten Merauke: Badan Pusat Statistik Kabupaten Merauke 2. Firmansyah, T. (2010). http://www.republika.co.id/berita/ breakingnews/nasional/ 11/01/13/ 158052 3. Octaviano, T. (2011). Nilai Ekonomi Beras Investor. Retrieved http://www.investor.co.id/ home/nilai-ekonomi-beras-capai- rp262triliun/21657 4. Ye, X., Sakai, K., Sasao, A., & Asada, S.-i. (2008). Potential of airborne hyperspectral imagery to estimate fruit yield in citrus. Chemometrics and Intelligent Laboratory Systems, 90, 132-144. doi: 10.1016/j.chemolab.2007.09.002 5. Arifin, B. (2009). Tantangan baru ekonomi pangan. Economic Review, 1-9. 6. Prabowo, E. H., & Suprihadi, M. (2011). Data Produksi Beras Bisa Dikoreksi, Kompas. http://bisniskeuangan.kompas.com/ read/2011/09/16/13055364/Data.Produksi.Beras.Bisa. Dikoreksi 7. Dorigo, W., Richter, R., Baret, F., Bamler, R., & Wagner, W. (2009). Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach. Remote Sensing, 1, 11391170. doi:10.3390/rs1041139 8. Munir, A.Q., Hartati, S. and Musdholifah, A. (2019). Early Identification Model for Dengue Haemorrhagic Fever (DHF) Outbreak Areas Using Rule-Based Stratification Approach. International Journal of Intelligent Engineering and Systems (IJIES), Vol 12, No. 2, pp. 246-260. 9. Ismanto, H., Azhari, Suharto, Arsyad, L. (2018). Ranking Method in Group Decision Support to Determine the Regional Prioritized Areas and Leading Sectors using Garrett Score. International Journal of Advanced Computer Science and Applications (IJACSA), 9(8), pp.94–99. 10. Sugiartawan, P., Hartati, S. and Musdholifah, A., 2020. Modeling of a Tourism Group Decision Support System using Risk Analysis based Knowledge BaseNo Title. International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), pp.354–363. 11. Sugiartawan, P. and Hartati, S., 2018. Group Decision Support System to Selection Tourism Object in Bali Using Analytic Hierarchy Process ( AHP ) and Copeland Score Model. In 2018 Third International Conference on Informatics and Computing (ICIC). Palembang, Indonesia: IEEE, pp. 1–6 12. Brantley, S. T., Zinnert, J. C., & Young, D. R. (2011). Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sensing of Environment, 115, 514-523. doi: 10.1016/j.rse.2010.09.020 13. Xiaoping, W., & Ni, G. (2008). Hyperspectral Reflectance And Their Relationships With Spring Wheat Growth Status Characteristics In Rained Agriculture Areas Of Loess Plateau. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII. 14. Hatfield, J. L., & Prueger, J. H. (2010). Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices. Remote Sensing, 2, 562-578. doi: 10.3390/rs2020562 15. Castro, K. L., & Sanchez-azofeifa, G. A. (2008). Changes in Spectral Properties, Chlorophyll Content and Internal Mesophyll Structure of Senescing Populus balsamifera and Populus tremuloides Leaves. 51-69. 16. Eitel, J. U. H., Keefe, R. F., Long, D. S., Davis, A. S., & Vierling, L. a. (2010). Active ground optical remote sensing for improved monitoring of seedling stress in nurseries. Sensors (Basel, Switzerland), 10, 2843-2850. doi: 10.3390/s100402843 17. Meroni, M., Rossini, M., Picchi, V., Panigada, C., Cogliati, S., Nali, C., & Colombo, R. (2008). Assessing Steady-state Fluorescence and PRI from Hyperspectral Proximal Sensing as Early Indicators of Plant Stress: The Case of Ozone Exposure. Sensors, 8, 1740- 1754. 18. Yang, C.-M., & Chen, R.-K. (2004). Modeling Rice Growth with Hyperspectral Reflectance Data. Crop Science, 44, 1283. doi: 10.2135/cropsci2004.1283 19. Evri, M., Sadly, M., & Kawamura, K. (2010). Diagnosing Ground-based Hyperspectral Red Edge Position Over Rice Canopy to Estimate Biophysical and Biochemical Parameters. Paper presented at the Pertemuan Ilmiah Tahunan MAPIN, Bogor. 20. Abbasi, M., Darvishsefat, A. A., & Schaepman, M. E. (2010). Spectral Reflectance of Rice Canopy and Red Edge Position (REP) as Indicator of High-Yielding Variety. ISPRS TC VII Symposium, XXXVIII, 1-5. 21. Danoedoro, P., Phinn, S., & Mcdonald, G. (2004). Developing A Versatile Land-Use Information System Based on Satellite Imagery for Local Planning in Indonesia Phase I : Establishment of Classification Scheme. Paper presented at the GISDECO 2004: 7th Seventh International on GIS in Developing Countries, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia. 22. Jensen, J. R. (2005). Introductory Digital Image Processing (3rd ed.). Englewood Hills, NJ: Prentice Hall. Authors: Zaydoun Abu Salem, Nabil Al-Hazim
Paper Title: Investigation of the Accumulation of Greenhouse Gases in Terms of Road Traffic Gradients Abstract: This research reflects on the impacts of traffic factors, car acceleration, volume of traffic, road gradient and the resulting sum of air pollutants, with a significant impact on the emissions of the vehicles. The general and detailed urban plans are normally addressed to these factors. Such considerations usually determine the adverse effects of motor vehicles, and environmental hazards, such as air pollution and vibration, which affects highways and bridges. However, the effect of road transport and preparation on the ecosystem is described. The research focuses on climate aspects that can be identified and designed so that all generic proposals can include them. In this study, CO, NO2, TVOC’s and SO2 concentration at multiple sampling sites were screened regularly. The study revealed that air pollutant rates are highly correlated with traffic movement and prevailing gradients. The SO2, NO2, CO and TVOC’s concentrations were very much associated to significant road flow parameters such as traffic elevation, intensity and amount of transport.
Keywords: transport, emission, pollutant, gradient, speed, traffic volume, road gradient
References: 1. AL-Rousan, Ammar A. et al. “Urban Traffic Pollution Reduction for Certain Cars Using Petrol Engines by Hydro-Oxide Gas 51. Induction” Journal of Air & Waste Management Association 65[12]: 1456-1460, [2015]. 2. Berggren, Christian, Thomas Magnusson,” Reducing automotive emissions”, The potentials of combustion engine technologies and the power of policy, Energy Policy, Volume 41, 2012, pages 636-643, 296-301 3. Chih Ming Ma, Gui Bing Hong, Chang Tang Chang, “Influence of Traffic Flow Patterns on Air Quality inside the Longest Tunnel in Asia”, Aerosol and Air Quality Research,11: 44–50, [2011]. 4. Duffy, B.L. and Nelson, P.F., “Non-methane Exhaust Composition in the Sydney Harbour Tunnel: A Focus on Benzene and 1, 3- butadiene”. Atmos. Environ. 30: 2759–2768, [1996]. 5. Joumard, R’Estimation of Pollutant Emissions from Transport’, ISBN 92-928-6785-4, Luxembourg, p. 175. Available on the Web at: [1999]. 6. Kai Zhanga and Stuart Battermanb, “Air pollution and health risks due to vehicle traffic” Sci Total Environ. 2013 Apr 15; 0: 307–316. 7. Katarzyna Bebkiewicz1, Zdzisław Chłopek2, Krystian Szczepański3, Magdalena 8. Zimakowska-Laskowska4 “The Influence of the Properties of Vehicles Traffic on the Total Pollutant Emission” Proceedings of the Institute of Vehicles 1(110), [2017} 9. Lonneman, W.A., Selia, R.L. and Meeks, S.A., “Non methane Organic Composition in the Lincoln Tunnel”. Environ. Sci. Technol. 20:790–799, [1986]. 10. M. Zickus and A. Greig, “Effect of congested vs. freeway urban traffic flow on air pollutant concentrations in a street canyon”. 7th Int. Conf. on Harmonization within Atmospheric Dispersion Modelling for Regulatory Purposes, [2003]. 11. Marsden, G., Bell, M., Shirley R., “Towards a real-time microscopic emissions model”, Transportation Research Part D, Vol. 6, pp. 37- 60, [2001]. 12. Norazian Mohamed Noor, Ahmad Shukri Yahya, Nor Azam Ramli, Mohd Mustafa Albakri Abdullah, “Using the Linear Interpolation Technique to Estimate Missing Values for Air Pollution Data”, Proceeding of Malaysian University Colleges Seminar on Engineering and Technology [MUCET], [2006]. 13. Pierson, W.R., “Real-world Automotive Emissions–Summary of Studies in the Fort McHenry and Tuscarora Mountain Tunnels”. Atmos. Environ. 30: 2233–2256, [1996]. 14. Rogak, S.N., Green, S.I. and Pott, U., “Use of Tracer Gas for Direct Calibration of Emission-factor Measurements in a Traffic Tunnel”. J. Air Waste Manage. Assoc.48: 545–552, [1998]. 15. Saad Abo-Qudais et al. “Performance Evaluation of Vehicles Emissions Prediction Models”. Clean Technology and Environmental Polices, Vol71s.4, pp 279-284. November [2005]. 16. Shohel Reza Amin,1 Umma Tamima,2 and Luis Amador Jimenez “Understanding Air Pollution from Induced Traffic during and after the Construction of a New Highway: Case Study of Highway 25 in Montreal” Journal of Advanced Transportation Volume 2017, Article ID 5161308, 14 pages 17. Talal M. AL-Momani et al. “Emission Rate of Gases Emitted from Private Gasoline Vehicles in Irbid-Jordan” Jordan Journal of Civil Engineering, Vol.5, No. 2, [2011]. 18. Talal M. AL-Momani. “Exhaust Emissions and Environmental Case Study: German Gasoline Vehicles” Dirasat, Pure Sciences, Vol. 33, No. 1, [2006] Authors: Anirudh Joshi, Chaitanya Mishra
Paper Title: Strength Assessment of wedges in the Splice Zone of the Column Abstract: Splice zone is the lower base of cross-section and a part of column which is also known as lower hinge zone. It is the weaker part of the column so additional reinforcement should be required every time in case of regular pad footing. The presented research provides a way of strengthening the reinforced concrete column by applying wedges at the splice zone. The work is focused on the base cross-section of an isolated footing against deflection, stresses, bending moment, etc. By implementing the proposed work, we can avoid critical damage at the base cross-section of the column & it also provides more stability, thus make splice zone stronger than earlier to withstand the resistance. The two sets of footings are considered in which one is regular pad footing & the other is pad footing strengthened by applying wedges in the splice zone. Both of them are tested under constant axial load and moment. The static structural analysis is done by using finite element analysis in ANSYS 2016 software. Further we will observe the deflection, stresses & also the overall effects of applying wedges with multiple height & size at the splice zone of the column.
Keywords: ANSYS, Base cross-section, bending moment, Finite element analysis, isolated footing, splice zone, wedge etc
References: 1. Cem YALÇIN and Osman KAYA.” An experimental study on the behavior of reinforced concrete columns using FRP material”. 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada, August 1-6, 2004 paper no 919. 52. 2. Amlan K. SENGUPTA, CHEMURU Srinivasulu Reddy, Badari Narayanan V T and Asokan “Seismic Analysis and retrofit of existing multistoried building in India – An overview with a case study”. 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada, August 1-6, 2004 paper no. 2571 3. M.Sarafraz and F.Danesh. “Flexural enhancement of RC columns with FRP”. The 14th World Conference on Earthquake Engineering, 302-307 October 12-17, 2008, Beijing, China 4. E.Choi, K.T. Yang, G.H. Tae, T.H. Nam and Y.S. Chung. “Seismic retrofit for RC columns by NiTi and NiTiNb SMA Wires”. ESOMAT 2009, 07005 (2009), DOI:10.1051/esomat/200907005, owned by the authors, published by EDP science, 2009. 5. Bassem Andrawes, Qiwen Chen and Moochul Shin. “New SMA Confinement technology for RC bridge column”. 6. Jianhua Liu, Robert G. Driver, and Adam S. Lubell. “Experimental Study on Short Concrete Columns with External Steel Collars”. ACI Structural Journal/May-June 2011, Title no. 108-S35 7. Dario Rosignoli, Francesca Simonelli, Alberto Meda, and ROberto Rosignoli. “High-Performance Fiber-Reinforced Concrete Jacketing in a Seismic Retrofitting Application”. Concrete Repair Bulletin, September/October 2012 ( www.icri.org ) 8. A.M. Tarabia and H.F. Albakry. “Strengthening of RC columns by steel angle sand strips”. Department of Structural Engineering, Alexandria University, Alexandria, Egypt Received 4 March 2014; revised 2 April 2014; accepted 15 April 2014 available online in 17 may 2014. Alexandria engineering journal (2014) 53, 615, 626 9. Mostafa Fakharifar, Genda Chen, Mahdi Arezoumandi, and Mohamed ElGawady. “Hybrid Jacketing for Rapid Repair of Seismically Damaged Reinforced Concrete Columns”. Article in Transportation research record: Journal of the Transportation Research Board, 2522, Transportation Research Board, Washington D.C., 2015 pp. 70-78. DOI: 10.3141/2522-07 10. Zumrawi M. M. E. and Aldaw H. K. E. “A study on Strengthening of Building foundation for Storey Extension”. J. Build. Mater. Structure. (2018) 5: 218-226 DOI: 10.5281/zenodo.2538661, ISSN 2353-0057, EISSN: 2600-6936 11. Asma Nabila binti Abd Kader, S. A. Osman, and M. Y. M. Yatim. “A state-of-the-art review on retrofitting beam column joint using GRPF with NSM techniques under seismic loading”. International Journal of Engineering and Technology, Vol. 11, No. 1, February 2019 12. Design of RCC structures by SS Bhavikatti, Third Edition,2016, pp 34 13. Indian standard code IS 456:2000 14. Indian standard code IS 13920:1993 15. Indian standard code IS 800:2007 16. https://en.m.wikipedia.org/wiki/Ansys Authors: Shubham Ashok Avhad, Satish M. Waysal
Paper Title: Analysis of Factor’s Causing Delays in Road Project by Severity Index Method Abstract: The construction sector is one of the important sources of growth and development of the national economy. However, entirely projects background delays in construction work and therefore raise its time and money. Problem in structure/construction work is studied one of the important obstacles in the work and it has a 53. very large issue in terms of time, money, and quality. This survey is done to find the time achievement of the road development project to identify the causes of delays and related factors according to the contractor with the help of the Questionnaire Survey which was taken with the help of Google Forms. As per the research, there are 308-312 a total of 32 factors affecting the delay/ failure of the road. As per the survey accidents during construction, an increase in material price, change in cost, and delay in time are the top factors which mostly affect the road failure. These factors were analyzed with the help of the Severity Index Method and mathematically with the help of Excel and Google Forms and then the result was calculated.
Keywords: Delay, Management, Severity Index, Road Failure, Quality.
References: 1. W. F. Lee, A.M.ASCE, H. J. Liao, M.ASCE, M. H. Chang, C. W. Wang, S. Y. Chi, and C. C. Lin, "Failure Analysis of a Highway Dip Slope Slide ", Journal of Performance of Constructed Facilities, ASCE, Vol. 27, pp. 1, 2013. 2. R. P. Chen, Z. C. Li, Y. M. Chen, C. Y. Ou, Q. Hu, and M. Rao, “Failure Investigation at a Collapsed Deep Excavation in Very Sensitive Organic Soft Clay”, Journal of Performance of Constructed Facilities, ASCE, December 12, 2013. 3. Yong K. Cho, Thaddaeus Bode, Jongchul Song, and Jin- HoonJeong, “Thermography -Driven Distress Prediction from Hot Mix Asphalt Road Paving Construction”, Journal of construction engineering and management, ASCE, Vol. 138, pp. 2, 2012. 4. Byungil Kim, Hyounkyu Lee, Hyungbae Park, and Hyoungkwan Kim4, “Estimation of Greenhouse Gas Emissions from Land-Use Changes due to Road Const in the Republic of Korea”, Journal of construction engineering and management, ASCE, Vol. 139, pp. 3, 2013. 5. Steven Vick and IoannisBrilakis, “Road Design Layer Detection in Point Cloud Data for Construction Progress Monitoring”, Journal of Computing in Civil Engineering, ASCE, 2018. 6. Fengwen Lai, Fuquan Chen, and Dayong Li, “Bearing Capacity Characteristics and Failure Modes of Low Geosynthetic-Reinforced Embankments Overlying Voids”, International Journal of Geomechanics, ASCE, 2018. 7. Zahra Kalantari and Lennart Folkeson, “Road Drainage in Sweden: Current Practice and Suggestions for Adaptation to Climate Change”, Journal of Infrastructure Systems, ASCE, Vol. 19, pp. 2, 2013. 8. Ibrahim Mahamid, Amund Bruland and Nabil Dmaidi, “Causes of Delay in Road Construction Projects”, Journal of Management in Engineering, ASCE, Vol. 28, pp. 3, 2012. 9. Z. Ren, G. Q. Shen and X. L. Xue, “Failure Caused by Inappropriate Construction Methods: An Expensive Lesson”, Journal of Management in Engineering, ASCE, Vol. 29, pp. 1, 2013. 10. Djoen San Santoso, Ph.D., and SothySoeng, “Analyzing Delays of Road Construction Projects in Cambodia: Causes and Effects”, Journal of Management in Engineering, ASCE, 2016. 11. Feng Li, Ph.D., A.M.ASCE, Hui Li, Ph.D., P.E., M.ASCE, and Tinggang Li, Ph.D., “Evaluation of Premature Failures of Asphalt Pavement Crack Sealing Bands”, Journal of Materials in Civil Engineering, ASCE, 2015. 12. Earl Marvin B. De Guzman, S.M.ASCE, and Marolo C. Alfaro, Ph.D., P.Eng., “Geotechnical Properties of Fibrous and Amorphous Peats for the Construction of Road Embankments”, Journal of Materials in Civil Engineering, ASCE, 2018. 13. Kristin Svenson, “Estimated Lifetimes of Road Pavements in Sweden Using Time-to-Event Analysis”, Journal of Transportation Engineering, ASCE, 2014. 14. By Elmar K. Tschegg, Georg Kroyer, Dong-Ming Tan, Stefanie E. Stanzl-Tschegg, and Johann Litzka, “Investigation of Bonding Between Asphalt Layers on Road Construction”, Journal of Transportation Engineering, ASCE, Vol. 121, pp.4, 1995. 15. By Fabian C. Hadipriono, M. ASCE and Hana- Kwang Wang, "Analysis of Causes of Falsework Failures in Concrete Structures", Journal of Construction Engineering and Management, ASCE, Vol. 112, pp. 1, 1986. 16. R. Navon, M. ASCE, and Y. Shpatnitsky, “Field Experiments in Automated Monitoring of Road Construction”, Journal of Construction Engineering and Management, ASCE, Vol. 131, No. 4, 2005. 17. V. K. Quagraine, Ph.D., S. G. Brandenburg, Ph.D., and Y. J. Beliveau, Ph.D., “Improving Labor-Based Road Rehabilitation in Ghana”, Journal of Management in Engineering, ASCE, Vol. 25, pp. 2, 2009. 18. By Kenneth L. Carpet, M. ASCE, “Failure Information: Dissemination Strategies”, Journal of Performance of Constructed Facilities, ASCE, Vol. 1, pp. 1, 1987. 19. By Kenneth L. Carper, M. ASCE, “Structural Failures During Construction”, Journal of Performance of Constructed Facilities, ASCE, Vol. 1, pp. 3, 1987. 20. Raymond S. Rolling and Marian Poindexter Rolling, "Pavement Failures: Oversights, Omissions, and Wishful Thinking", Journal of Performance of Constructed Facilities, ASCE, Vol. 5, pp. 4, 1991. 21. Mohammed S. Hashem M. Mehany, Ph.D., A.M., and Angela Acree Guggemos, Ph.D., A.M., “Risk-Managed Lifecycle Costing for Asphalt Road Construction and Maintenance Projects under Performance-Based Contracts”, Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, ASCE, 2001 Authors: Fahad Alturise, Tamim Alkhalifah, Sami Alshmrany Faculty Perception, Attitude, and Readiness Towards e-Learning in Ar Rass Dental College A Paper Title: Comparison of Moodle and Blackboard Learning Management Systems Abstract: With advances in technology, revolutionary changes have been taking place in educational institutions. The traditional classroom method of teaching no longer fulfills all teaching outcomes. A blended teaching methodology, involving the traditional system and the addition of e-learning through the application of Learning Management Systems (LMS), provides newer opportunities to achieving the expected learning outcomes. The functionality of these systems must be studied and analyzed for proper application. This study compared the faculty experience and perception of using two of the most widely used LMS, namely Blackboard and Moodle. The results of the survey revealed that these systems help to enhance the effectiveness of teaching and learning and increase student-staff interaction. The analysis concludes that the Blackboard system of e- learning by LMS is widely preferred.
Keywords: e-learning, Learning Management Systems, Blackboard, Moodle 54. References: 313-320 1. Shdaifat, A., & Obeidallah, R. (2019). Quiz Tool Within Moodle and Blackboard Mobile Applications. 2. Sarkar N, Ford W, Manzo C. (2017). Engaging digital natives through social learning. Systemics, cybernetics and informatics, 15(2), 1- 4. 3. Peter Bradford et al. (2006). The Blackboard learning system: The Be All and End All in Educational Instruction, J.Educational Technology Systems, Vol. 35(3) 301-314. 4. Machado M, Tao E. (2007). Blackboard vs. Moodle: Comparing user experience of learning management systems, Proceedings - Frontiers In Education Conference. 5. Al Mulhem, A. (2020). Exploring the Key Factors in the Use of an E-Learning System Among Students at King Faisal University, Saudi Arabia. 6. Graham C et al. Seven principles of effective teaching: A practical lens for evaluating online courses. http://www.westvalley.edu/trc/seven.html 7. Devraj M, Irene G. (2008). Use of the Blackboard learning management system. EURASIA J Math Sci and Tech Ed, 14(7):3069-3082 8. Priyavahani S et al. (2014). A study of comparison between Moodle and Blackboard based on case studies for better LMS. Journal of Information Systems Research and Innovation: 26-33. ISSN: 2289-1358. 9. Prescott D. (2003). Faculty use of the course management system ilearn at the American University of Sharjah. Learning and Teaching in Higher Education: Gulf Perspectives, 10(1). 10. Suppasetseree S, Dennis N. (2010). The use of Moodle for teaching and learning English at tertiary level in Thailand. The International Journal of Humanities, 8(6). 11. Alokluk J. (2018). The effectiveness of Blackboard system uses and limitations in information management. intelligent information management, 10:133-149. 12. AlAjlan A, Zedan H. (2008). Future trends of distributed computing systems. 12th IEEE International workshop on future trends of distributed computing systems. 13. Morgan G. (2003) Faculty use of course management systems. Educause Center for Applied Research. 14. S. Jayson, (2006) Blackboard Breaks Through, The Motley Fool, www.fool.com/News/mft/2006. 15. M. Pittinsky and T. Bell, (2005) From the Dining Hall to the Campus Bookstore to a Networked Transaction Environment: Overview White Paper, Blackboard, Inc. 16. M. Pittinsky, (2004). The Networked Learning Environment: Overview White Paper, Blackboard, Inc. 17. Sang, Y.-T., Chang, K.-E., & Liu, T.-C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252- 275 18. Venkatesh V. et al. (2003). User acceptance of information technology: Toward a unified view. 27(3): 425-478. 19. Zain, N. M., & Fadil, N. F. M. (2018). Learning Management System: An Experience and Perception Study from Medical Imaging Lecturers and Scholars in a Private University. 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(2012). The impact of computer assisted grammar teaching on EFL pupils’ performance in Jordan. International Journal of Education and Development using ICT, 8(1), 71-90. 31. Al-Ajlan, A., & Zedan, H. (2008). Why moodle. Paper presented at the 2008 12th IEEE International Workshop on Future Trends of Distributed Computing Systems. 32. Alotaibi, S. J. (2017). ICT classroom LMSs: examining the various components affecting the acceptance of college students in the use of blackboard systems. In Advances in human factors, business management, training and education (pp. 523-532): Springer. 33. Avila, V. J. G., Hembra, N. G., Mueco, J. M., & Zamora, F. G. (2015). Moodle and Facebook As a tool for delivering instruction and attainment of learning. Communication Research, 2(1). 34. Blin, F., & Munro, M. (2008). Why hasn’t technology disrupted academics’ teaching practices? Understanding resistance to change through the lens of activity theory. Computers & Education, 50(2), 475-490. 35. 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Paper presented at the 2016 IST-Africa Week Conference. 41. Nedeva, V., Dimova, E., & Dineva, S. (2010). Overcome disadvantages of E-learning for training English as foreign language. University of Bucharest and University of Medicine and Pharmacy Târgu-Mures, 275-281. 42. Piotrowski, M. (2010). What is an e-learning platform? In Learning management system technologies and software solutions for online teaching: Tools and applications (pp. 20-36): IGI Global. 43. Soliman, N. A. (2014). Using e-learning to develop EFL students’ language skills and activate their independent learning. Creative Education, 2014. Authors: Satyasundar Mallick, Monideepa Roy A Comparative Study of Weather Forecasting Simulation Models through Sensors for Accurate Paper Title: Monsoon Predictions in the Indian Ocean Abstract: When Monsoon depressions form over the seas, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides humidity and high-horizontal resolution temperature details about the depressions. These high-resolution satellite data related to temperature and humidity can improve the poor predicting rate of depressions [1]. Using three-dimensional variational data assimilation (3DVAR) and with the help of humidity profiles along with MODIS temperature. We can achieve an advanced prospect of detection and a larger value of (ETS) equitable threat score observed over 48 hours collected precipitation with respect to the control run. The 55. 3DVAR assimilation of Doppler Weather Radar wind data associated with Indian Meteorological Department (IMD) surface data and upper air data helped in the improvements in the simulation of strong gradients 321-326 associated with horizontal wind speed ,higher warm core temperature , high vertical velocity & better precipitation and spatial distribution.[2]. The effect of Spectral sensor microwave imager (SSM/I), humidity profiles, use of Advanced TIROS Vertical Sounder (ATOVS) temperature and total precipitable water (TPW) helped in improving the ‘‘forecast impact’’ parameters of ‘‘bias score’’ and ‘‘equitable threat score’’ with respect to the assimilation of satellite observation[3]. In this paper we have discussed a comparative study of different proposed techniques to analyze its effects in improving the low prediction rates of depressions.
Keywords: MODIS, 3DVAR, ATOVS, TPW, monsoons depressions, prediction rates
References: 1. The Impact of Assimilation of MODIS Observations Using WRF-VAR for the Prediction of a Monsoon Depression During September 2006 M. Govindankutty1, A. Chandrasekar, A.K. Bohra, John P. George and Munmun Das Gupta The Open Atmospheric Science Journal 2(1):68-78 · June 2008. 2. Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone M. Govindankutty, A. Chandrasekar and Devendra Pradhan International Journal of Remote Sensing, Volume 31,July 2010, Issue 24 3. Effect of 3DVAR assimilation of MODIS temperature and humidity profiles on the dynamic and thermodynamic features of three monsoon depressions over the Bay of Bengal 4. M. Govindankutty A. Chandrasekar Meteorology and atmospheric physics (Print). 2010, Vol 107, Num 1-2, pp 65-79, 15 p ; ref : 1/4 p 5. Ide K, Courtier P, Ghil M, Lorenc AC. Unified notation for data assimilation: Operational, sequential and variational. J Meteor Soc Jpn 1997; 75: 181-89. 6. Barker DM, Huang W, Guo YR, Bourgeois AJ, Xiao QN. A Three dimensional Variational (3DVAR) system with MM5: Implementation and Initial results. Mon Weather Rev 2004; 132: 897-914. 7. Gu J, Xiao Q, Kuo YH, Barker DM, Xue J, Ma X. Assimilation and simulation of typhoon Rusa (2002) using the WRF system. Adv Atm Sci 2005; 22: 415-27. 8. Zhang H, Xue J, Zhu G, Zhang S, Wu X, Zhang F. Application of direct assimilation of ATOVS microwave radiances to typhoon track predictions. Adv Atm Sci 2004; 21: 283-90. 9. [8] A Bhilash , S., Das , S., Kalsi , S.R., Dasgupta , M., M Ohankumar , K., G Eorge , J.P., B Anerjee , S.K., T Hambi , S.B. And P Radhan , D., 2007, Impact of Doppler radar wind in simulating the intensity and propagation of rainbands associated with mesoscale convective com-plexes using MM5-3DVAR system. Pure and Applied Geophysics, 164, pp. 1491–1509. 10. B Arker , D.M., H Uang , W., G Uo , Y.R. And A L B Ourgeois , 2003, A Three Dimensional Variational (3DVAR) data assimilation scheme for use with MM5. NCAR technical note, NCAR/TN-453 þ STR, p. 68. 11. D AVIS, C.A. and LOWNAM ,S., 2001, The NCAR-AFWA tropical cyclone bogussing scheme.Air Force Weather Agency Report, NCAR Boulder, CO, 13 pp. Authors: Samer I. Mohamed
Paper Title: Reference Evapotranspiration Prediction for Smart Irrigation Abstract: Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.
Keywords: Evapotranspiration; machine learning; FAO_56; Neural network; predict; linear regression; irrigation.
References: 1. A. Goldstein, L. Fink, A. Meitin, S. Bohadana, O. Lutenberg, and G. Ravid, “Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge,” Precision Agriculture, vol. 19, no. 3, pp. 421–444, 2017. 2. Y. Gandge and Sandhya, “A study on various data mining techniques for crop yield prediction,” 2017 International Conference on 56. Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017. 3. M. Işık, Y. Sönmez, C. Yılmaz, V. Özdemir, and E. Yılmaz, “Precision Irrigation System (PIS) Using Sensor Network Technology Integrated with IOS/Android Application,” Applied Sciences, vol. 7, no. 9, p. 891, Jan. 2017. 327-333 4. J. D., “Using Wireless Sensor Networks for Precision Irrigation Scheduling,” Problems, Perspectives and Challenges of Agricultural Water Management, 2012. 5. 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Paper Title: Automatic Sign Language Gesture Recognition using Prewitt & Morphological Dilation 57. Abstract: Sign languages have their own linguistic structure, grammar and characteristics, and are independent of the rules that govern spoken languages. They are visual languages that rely on hand gestures as well as on 334-339 bodily and facial expressions. Sign languages in different countries are vastly different from one another, so enabling easy communication is important: not just to break the barrier between hearing and deaf individuals, but also between people who do not sign in the same language. In India, sign language plays an important role in the field of communication among dumb and deaf people. There are different signs associated for communication in every country as per their convenient gestures. Automatic sign language gesture recognition is an approach for recognizing gestures and converts it to its actual meaning and convey either through speech or text as per requirements. Here the system is based on Prewitt Edge Detection that possesses the gestures of sign language and helps to recognize and assign their meanings. The Prewitt is second order derivative that has been used in image processing and computer vision, in the form of edge detection or extraction algorithms where it creates gradient of horizontal and vertical magnitude. System also uses certain pre-processing filtration technique such as morphological dilation for better feature extraction
Keywords: Sign Language Recognition, Prewitt Edge Detection, Morphological Operation, Dilation, Gesture Recognition.
References: 1. https://www.pinterest.com.au/pin/491103534343877366/ 2. https://www.semanticscholar.org/paper/An-Automated-System-for-Indian-Sign-Language-Kaur- Gill/ebcceb337be93d44e3d6635ad5964e1e8bfaeb2c 3. B. Gupta, P. Shukla and A. Mittal, "K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion," 2016 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2016, pp. 1-5, doi: 10.1109/ICCCI.2016.7479951. 4. G. A. Rao, K. Syamala, P. V. V. Kishore and A. S. C. S. Sastry, "Deep convolutional neural networks for sign language recognition," 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), Vijayawada, 2018, pp. 194-197, doi: 10.1109/SPACES.2018.8316344. 5. Daware, Snehal & Kowdiki, Manisha. (2018). Morphological Based Dynamic Hand Gesture Recognition for Indian Sign Language. 343-346. 10.1109/ICIRCA.2018.8597417. 6. H. Muthu Mariappan and V. Gomathi, "Real-Time Recognition of Indian Sign Language," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-6, doi: 10.1109/ICCIDS.2019.8862125. 7. K. Revanth and N. S. M. Raja, "Comprehensive SVM based Indian Sign Language Recognition," 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-4, doi: 10.1109/ICSCAN.2019.8878787. 8. S. C.J. and L. A., "Signet: A Deep Learning based Indian Sign Language Recognition System," 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2019, pp. 0596-0600, doi: 10.1109/ICCSP.2019.8698006. 9. S. Hayani, M. Benaddy, O. El Meslouhi and M. Kardouchi, "Arab Sign language Recognition with Convolutional Neural Networks," 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco, 2019, pp. 1-4, doi: 10.1109/ICCSRE.2019.8807586. 10. A. Subhash Chand, A. S. Jalal and R. Kumar Tripathi, “A survey on manual and non-manual sign language recognition for isolated and continuous sign,” Applied Pattern Recognition, 2016. 11. https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm 12. H. Cooper, B. Holt and R. Bowden, “Sign language recognition,” Springer, 2011. 13. S. Ong and S. Ranganath, chez IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. 14. M. Al-Rousan and M. Hussain, “Automatic recognition of Arabic sign language finger spelling,” International Journal of Computers and Their Applications, 2001. 15. K. Assaleh and M. Al-Rousan, “Recognition of Arabic sign language alphabet using polynomial classifiers,” EURASIP Journal on Applied Signal Processing, 2005. 16. O. Al-Jarrah and A. Halawani, “Recognition of gestures in Arabic sign language using neuro-fuzzy systems,” Atificial Intelligence, 2001. 17. T. Shanableh, K. Assaleh and M. Al-Rousan, “Spatio-temporal feature-extraction techniques for isolated gesture recognition in Arabic sign language,” IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics), 2007. 18. M. AL-Rousan, K. Assaleh and A. Tala’a, “Video-based signerindependent Arabic sign language recognition using hidden Markov models,” ELSEVIER, 2009. 19. M. Maraqa, F. Al-Zboun , M. Dhyabat and. R. Abu Zitar, “Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks,” Intelligent Learning Systems and Applications, 2012 20. R. Alzohairi, R.Alghonaim, W.Alshehri, S.Aloqeely, M.Alzaidan and O.Bchir, ”Image based Arabic Sign Language Recognition System,” International Journal of Advanced Computer Science and Applications, 2018. 21. Y. LeCun, L. Bottoux, Y. Bengio and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” IEEE, November 1998. 22. A. Krizhevsky, I. Sutskever and G. Hintton, “Imagenet classification with deep convolutional neural networks,” chez Proceedings of the 25th international conference on neural information processing systems, 2012. 23. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, 2014. 24. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinov, C. Hill and A. Arbor, “Going Deeper with Convolutions,” IEEE Xplore, 2015. 25. K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” 2015. 26. D. Coomans and D. Massart, “Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearestneighbour classification by using alternative voting rules ,” AnalyticaChimicaActa 136, 15-27, 1982. 27. C. Williams and M. Seeger, “Using the Nyström method to speed up kernel machines ,” 2001. 28. Y-W. Chang, C.-J. Hsieh, K-W. Chang, M. Ringgaard and C-J. Lin, “Training and testing low-degree polynomial data mappings via linear SVM ,” Journal of Machine Learning Research, april 2010. 29. J-P. Vert, K. Tsuda and B. Schölkopf, “A primer on kernel methods ,” Kernelmethods in computationalbiology, 47, 35-70, 2004. Volume(issue), paging if given. Available: http://www.(URL) Authors: Sayan Ghar, Kushal Jain The Relative Research on Planning, Modelling, and Analysis of G+6 Residential Buildings with and Paper Title: without Multi-level Car Parking Facility Abstract: In India, normally the proposed buildings are either residential or commercial cum residential. 58. Generally, the parking is provided in the basement of the buildings or open to the sky, based on the availability of space. But there are some cases due to the high cost of land, it is not possible to provide an open space 340-345 parking facility. On the other hand, parking facilities are very critical in metropolitan cities like Mumbai, Kolkata, Chennai, Delhi, etc. Due to the high number of vehicles on the roads. In some places due to the non- availability of the parking, the people had to face issue in their day to day work and some times had to pay fine for illegal parking. In this project, the crisis of the parking facilities is kept in mind and relative research is done to check whether it can be adapted in the cities of India or not to minimize the issues. And also the analysis of structures is done to check the stability of the structures.
Keywords: G+6 Building, Multi-level Car Parking, Multi-storey, Modelling and Analysis, Relative Research, Hydraulic Lift.
References: 1. Sonila Sonker “Planning & Designing of a Multi-Level Vehicle Parking at Aakash Ganga Shopping Complex Bhilai (Chhattisgarh)”, Imperial Journal of Interdisciplinary Research (IJIR) , 2017, Vol-3, Issue-1, Page 1372-1387. 2. Aman, M Nalwadgi, et.al, “Analysis and Design of Multistorey Building by using STAAD Pro,” in IRJET, vol. 3, June 2016, pp. 887–891. 3. Abdul Qayyum, et. al, “Review of Multi-Storey Car Parking Building”, International Research Journal of Engineering and Technology (IRJET), 2017, Vol- 04, Issue: 04, Page 2188 -2191. 4. Ravi Kumar B, Saleem SK “Analysis And Design OF Multi Storyed Building By Using STAAD PRO” Anveshana's International Journal Research in Engineering and Applied Sciences (AIJREAS), 2017, -Vol-2 Issue 01, Page 121-126. 5. T. Sasidhar, T.B. Manideep, I. Siva Kishore, N. Sanjana, “Analysis and Design of a high rise building (G+10) by STAAD Pro,” in IJCIET, vol. 8, April 2017, pp. 654–658. 6. Pramod Kr, Venkatesh K, Pawan R, et.al “Analysis and design of multistoried parking building proposed at Jalahalli cross, Bengaluru,” in IRJET, vol. 5, June 2018, pp. 1019–1024. 7. A L Kheyfets, V N Vasilieva, “ 3D Modeling as Modeling as Method for Construction and Analysis of Graphic Objects”, IOP Conference Series: Material Science and Engineering (MSE), 2017, pp. 1-6. IS SP 7: 2016, Code of National Building Code of India 2016 (Vol- 1), BIS, New Delhi. Authors: SudhaSurwase, Ravi Yadahalli, Shankar Nawale
Paper Title: Design of Nested H slot Passive UHF RFID Tag Abstract: RFID is a short distance communication system which comprises of a RFID tag, a RFID reader and a personal computer with desired software that can maintain the related information. These RFID tags can be of active or passive types. This paper focuses on design, simulation and fabrication of passive ultra-high frequency RFID tag (microchip and an antenna) which resonates at the frequency 866 MHz in the Industrial Scientific Medical Band. The nested H-slot inverted-F microstrip antenna structure is used for the design of passive RFID tag. It examines the specific tag geometry and its characteristics to optimize the PIFA antenna and in turn RFID tag’s performance.
Keywords: Impedance, PIFA, RFID tag, UHF 59. References: 1. K. Finkenzeller, RFID handbook, NewYork, Wiley & Son, 2000 346-349 2. BEST PRACTICES GUIDE, RFID Implementation, Testing & Deployment 3. Davinder Parkash, “RFID Technology and its Applications: A Review” 4. Aurelian Moraru ; Elena Helerea ; Corneliu Ursachi ; Marius Daniel Călin“RFID system with passive RFID tags for textiles”, 10thInternational Symposium on Advanced Topics in Electrical Engineering (ATEE), April 2017. 5. Scientific Research and Essays Vol. 5(10), pp. 1033-1051, 18 May 2010 Available online at http://www.academicjournals.org/SRE ISSN 1992-2248 © 2010 Academic Journals 6. Gaetano Marrocco, “The Art of UHF RFID Antenna Design:Impedance-Matching and Size-Reduction Techniques”, IEEE Antennas and Propagation Magazine, Vol. 50, No. 1, February 2008. 7. K. V. Seshagiri Rao, Pavel V. Nikitin, Sander F. Lam, “Antenna Design for UHF RFID Tags: A Review and a Practical Application”, IEEE transactions on antennas and propagation, vol. 53, no. 12, december 2005 8. C. A. Balanis, Antenna Theory Analysis and Design, John Wiley and Sons, Inc.,Toronto, Canada, 3rd edition, 2005. 9. Sudha Surwase, Dr. Ravi Yadahalli, Dr. Shankar Nawale “PIFA RFID Tag Antenna Design and Simulationusing CST Microwave Studio”, International Journal of Science and Research (IJSR), Volume 7 Issue 10, October 2018. Authors: CPS Pasricha, Rajeev Gupta, Rahul Walawalkar Stand Alone 1-MW Microgrid for Remote locations of Armed Forces with PV-Battery-Diesel Paper Title: Generator Abstract: Many times, Armed Forces are deployed in bases in remote areas on the borders or Islands, which are far flung areas away from mainland. In many such cases, these areas do not have their power requirements through the main grid supply and entire power requirement of the deployment is supplied by diesel generators. These diesel generators have high environmental impact due to emission of greenhouse gases and are highly uneconomical as logistic sustenance of remote bases for supply of fuel is very challenging, Fossil fuel has to be supplied by vehicles, helicopters, boats or manually carried to hill tops. This increases the overall cost of deploying armed forces in remote areas. In recent years with the advancements in power electronic components 60. and renewable energy, development in Microgrids (MGs) have shown a way to reduce dependency on main power grids. Hence, with the help of MGs, renewable energy can be used to fulfill power requirements of the 350-358 armed forces deployed in remote places. In this work, a MG with capacity of 1MW has been designed keeping the special needs of armed forces as a major consideration. Solar power has been used as a primary renewable energy source in the proposed design. In order to mitigate the adverse effects of meteorological and extreme conditions on the solar power generation capacity, energy storage system in the form of batteries has also been provided. Batteries store power when excess power is generated from the photo voltaic (PV) system and discharge the power when power demand is higher than the PV generated power. Diesel generator sets have also been used to run critical loads, provide reliability and as backup to critical operations catering for outages, night time needs and un-expected meteorological conditions. MATLAB has been used to design and simulate the proposed MG. Working of the MG has also been demonstrated for varying meteorological and varying load conditions as well. The proposed design works satisfactory in all cases.
Keywords: Solar energy, battery, MG, diesel generator, voltage source inverter, PWM.
References: 1. Dimeas, A., & Hatziargyriou, N. (2004, May). A multi-agent system for MGs. In Hellenic Conference on Artificial Intelligence (pp. 447-455). Springer, Berlin, Heidelberg. 2. Oyarzabal, J. R. A. E. J., Jimeno, J., Ruela, J., Engler, A., & Hardt, C. (2005, November). Agent based micro grid management system. In 2005 International Conference on Future Power Systems (pp. 6-pp). IEEE. 3. Kanellos, F. D., Tsouchnikas, A. I., & Hatziargyriou, N. D. (2005, June). Micro-grid simulation during grid-connected and islanded modes of operation. In International Conference on Power Systems Transients (Vol. 6). 4. Moreira, C. L., & Lopes, J. P. (2007, May). MGs dynamic security assessment. In 2007 International Conference on Clean Electrical Power (pp. 26-32). Ieee. 5. Popov, M., Karimi, H., Nikkhajoei, H., & Terzija, V. (2009, June). Dynamic model and control of a MG with passive loads. In IPST Conference Proceedings. 6. Pipattanasomporn, M., Feroze, H., & Rahman, S. (2009, March). Multi-agent systems in a distributed smart grid: Design and implementation. In 2009 IEEE/PES Power Systems Conference and Exposition (pp. 1-8). IEEE. 7. Kim, H. M., & Kinoshita, T. (2010). A multiagent system for MG operation in the grid-interconnected mode. Journal of Electrical Engineering and Technology, 5(2), 246-254. 8. Oudalov, A., & Fidigatti, A. (2009). Adaptive network protection in MGs. International Journal of Distributed Energy Resources, 5(3), 201-226. 9. Balijepalli, V. M., Khaparde, S. A., & Dobariya, C. V. (2010). Deployment of MGs in India. IEEE PES General Meeting (pp. 1-7). 10. Khamphanchai, W., Pisanupoj, S., Ongsakul, W., & Pipattanasomporn, M. (2011, September). A multi-agent based power system restoration approach in distributed smart grid. In 2011 International Conference & Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE) (pp. 1-7). IEEE. 11. Guerrero, J. M., Vasquez, J. C., Matas, J., De Vicuña, L. G., & Castilla, M. (2010). Hierarchical control of droop-controlled AC and DC MGs—A general approach toward standardization. IEEE Transactions on industrial electronics, 58(1), 158-172. 12. Kouluri, M. K., & Pandey, R. K. (2011, September). Intelligent agent based micro grid control. In 2011 2nd International Conference on Intelligent Agent & Multi-Agent Systems (pp. 62-66). IEEE. 13. Zhang, Z., Dou, C., Yue, D., Zhang, B., & Luo, W. (2018). A decentralized control method for frequency restoration and accurate reactive power sharing in islanded MGs. Journal of the Franklin Institute, 355(17), 8874-8890. 14. Shayeghi, H., Shahryari, E., Moradzadeh, M., & Siano, P. (2019). A survey on MG energy management considering flexible energy sources. Energies, 12(11), 2156. 15. Tavassoli, B., Fereidunian, A., & Mehdi, S. (2020). Communication system effects on the secondary control performance in MGs. IET Renewable Power Generation. 16. Salgueiro, Y., Rivera, M., & Nápoles, G. (2020). Multi-agent-Based Decision Support Systems in Smart MGs. In Intelligent Decision Technologies 2019 (pp. 123-132). Springer, Singapor. 17. Siva Ganesh Malla and Priyanka Malla, “TSFuzzy Controller based Grid Connected Hybrid Renewable Energy Sources”, International Journal of New Technologies in Science and Engineering (IJNTSE), Vol. 7, Issue. 7, pp. 1-13, July 2020 18. Siva Ganesh alla, Priyanka Malla and Rajesh Koilada, “Solar Energy based Hybrid Electric Car: Part 1”, International Journal of New Technologies in Science and Engineering (IJNTSE), Vol. 6, Issue 6, pp. 11- 25, Dec. 2019 Authors: Somalina Chowdhury, Santanu Kumar Sen
Paper Title: Smart Meter using Big Data in IoT Abstract: Nowadays Green energy or energy efficiency has become one of the key concerns of the people. In this era Smart Grid with Internet of Things has took a vital role. Here distributed system with Smart Grid principle is being discussed. Unlike traditional Grid, Smart Grid are bidirectional in nature. One of the important component of Smart Grid is Smart Meter. In this paper we will focus on the vast data handled by Smart Meter using Big Data. The paper will focus on efficient energy management and how tactical decision making is done by Big Data to improve the overall Smart Grid performance. Data is collected through sensors especially wireless sensors are used. A vast amount of data is collected, analyzed, and processed to retrieve information. This will increase the business prospects and will be cost effective in future. Issues like instability, blackouts, etc will be under controlled. In traditional process of meter reading collecting usage and generating bill is the vital issue done by manually visiting the individual location which is now automated. Smart Meter works with real time data. It will be shown how Big Data will improve customer relation as well as improves social welfare. Thus proper techniques of data mining is used to retrieve data but with high data security. Mesmerising of various current technology is done here to get ultimate information about energy consumption and also to 61. maintain a balance among customers and utilities. 359-362 Keywords: Smart Grid, Smart Meter, Internet of Things (IoT), Big Data, Green Energy, Energy consumption, Wireless Sensor Network (WSN), Wireless Sensor, Business Intelligence
References: 1. Yang Zhang, Tao Huang and Ettore Francesco Bompard," Big data analytics in smart grids: a review ", in Zhang et al. Energy Informatics (2018) https://doi.org/10.1186/s42162-018-0007-5 2. Prachi Kulkarni, D.K. Chitre," Energy Consumption Using IoT and Big Data Analytics Approach in Smart Home ". In IJIRSET 3. Driss Benhaddou, Mohamed Riduan Abid, Ouidad Achahbar , Nacer Khalil, Tajjeeddine Rachidi and Maen Al Assaf," BIG DATA PROCESSING FOR SMART GRIDS ", in IADIS International Journal on Computer Science and Information Systems Vol. 10, No. 1, pp. 32-46 ISSN: 1646-3692 4. Laura L. Pullum and IEEE Smart Grid Big Data Analytics, Machine Learning and 10 Artificial Intelligence in the Smart Grid Working Group, "Big Data Analytics in the Smart Grid"' by IEEE team of Smart grid 5. A.Berouine, F. Lachhab, Y. Nait. Malek, M. Bakhouya, R. Ouladsine, "A Smart Metering Platform Using Big Data and IoT technologies", by 2007 IEEE 6. P.C. Chen, T. Dokic, and M. Kezunovic, "The use of big data for outage management in distribution systems," in Int. Conf. Electricity Dist. (CIRED) Workshop, 2014 7. Shibily Joseph, and E. A. Jasmin,"Stream computing framework for outage detection in smart grid.", International Conference on Power, Instrumentation, Control and Computing, 2015. 8. Suhail Sami Owais, Nada Sael Hussein, “Extract Five Categories CPIVW from the 9V’s Characteristics of the Big Data ", in International Journal of Advanced Computer Science and Applications, Vol. 7, No. 3, 2016 9. SomalinaChowdhury, Santanu Kumar Sen, “Security in Smart Meter using Iot", in IJEAT, ISSN: 2249 – 8958, Volume-9 Issue-4, April 2020 10. SomalinaChowdhury, Santanu Kumar Sen,, “Designing of Smart meter in Smart grid using IoT ",Processing in Manufacturing and Quality Engineering - Industrial Perspective Authors: Somalina Chowdhury, Santanu Kumar Sen
Paper Title: Cloud Computing in IoT based Smart Meter Abstract: Modern tends forces a massive change in power industry and utility value chain. Therefore demands of resources and storage is also increases which can be supported by cloud computing. Consumer role becomes one of the vital role here. These are all implemented by Smart Grid. So we need to develop advanced power distribution grids with modern infrastructure. In this paper we will discuss about one of the most important component of smart grid that is smart meter that will IoT based and use cloud platform for data processing and storing. Cloud infrastructure will help in building a large number of application on it and also helps in storing large volume of data. Cloud technology enhances the storage on demand using virtual memory concept. Thus provides an economic, portable, scalable infrastructure.
Keywords: Smart grid, Cloud infrastructure, smart metering infrastructure, IoT, GSM, Wi-Fi, webpage 62. References: 1. Nikhil Mishra, Vinay Kumar, Garima Bhardwaj, "Role of Cloud Computing in Smart Grid", publication at: 363-364 https://www.researchgate.net/publication/334765912 2. P. Siano, C. Cecati, C. Citro, and P. Siano, “Smart operation of wind turbines and diesel generators according to economic criteria,” IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4514–4525, Oct. 2011 3. R. H. Katz, “Tech Titans Building Boom,” IEEE Spectrum, pp. 40–54, Feb. 2009. 4. Nikhil Mishra, Vinay Kumar, Garima Bhardwaj , “Role of Cloud Computing in Smart Grid,” publication at: https://www.researchgate.net/publication/334765912 5. SomalinaChowdhury, Santanu Kumar Sen, “Security in Smart Meter using Iot", in IJEAT, ISSN: 2249 – 8958, Volume-9 Issue-4, April 2020 6. Marco Paua, Edoardo Pattib, Luca Barbieratob, Abouzar Estebsaric, Enrico Ponsc, Ferdinanda Poncia, Antonello Montia, “A Cloud- based Smart Metering Infrastructure for Distribution Grid Services and Automation” submitted to Sustainable Energy, Grids and Networks August 9, 2017 7. Birendrakumar Sahani, Tejashree Ravi, Akibjaved Tamboli , Ranjeet Pisal, " IoT Based Smart Energy Meter", in IRJET Volume: 04 Issue: 04 | Apr -2017 8. Yang Zhang, Tao Huang and Ettore Francesco Bompard," Big data analytics in smart grids: a review ", in Zhang et al. Energy Informatics (2018) https://doi.org/10.1186/s42162-018-0007-5 Authors: R.Kasthuri, P.Vasanthi, S.Ranganayaki
Paper Title: Optimization of Inventory Model-Cost Parameters, Inventory and Lot Size as Fuzzy Numbers Abstract: In general, the demand rate and the unit cost of the items remains constant inspite of lot size in inventory models. But in reality, the demand rate and the unit cost of the items are connected together. In this research, demand dependent unit cost inventory model is considered where different cost parameters, maximum inventory and the lot size of the model are taken under fuzzy environment. First an analytic solution of the crisp model is obtained by the method of calculus where the inventory parameters are exact and deterministic. Later, the problem is developed with fuzzy parameters where inaccuracy has been introduced through triangular membership function.Then the defuzzification of the model is done by using the method of Graded mean integration. An optimal solution is obtained using Karush Kuhn-Tucker conditions approach. An illustrative model is done and an analysis of total cost for different measures of possibility are performed and tabulated
Keywords: Demand dependent on unit cost, Graded mean integration, Karush Kuhn-Tucker conditions technique, Triangular fuzzy number. 63. References: 1. Abou-El-Tal, M.O &Kotb, K.A.M, 1997 ‘Multi-item EOQ inventory model with varying holding cost under two restrictions: a 365-368 geometric programming approach’, Production Planning and Control, vol.8, issue.6, pp.608-611. 2. Cheng, TCE 1989, ‘An Economic production quantity model with demand dependent unit cost’, European Journal of Operations Research, vol. 40,pp.252-256. 3. Harris, F, 1915 ‘Operations and cost’, Factory Management Service, Chicago, A.W. Shaw C0., Jung, H & Klein, CM 2001, ‘Optimal inventory policies under decreasing functions via geometric programming’, European Journal of Operations Research, vol. 132, pp.628-642. 4. Lee, HM & Yao, JS 1999, ‘Economic order quantity in fuzzy sense for 5. inventory without backorder model’, Fuzzy Sets and Systems, vol. 105, no. 1, pp. 13-31. 6. Mandal, B, Bhunia, AK&Maiti, M 2007, ‘A Model on two storage and Inventory system under stock dependent selling rate incorporating marketing decisions andtransportation cost with optimum release rule’, Tamsui Oxford journal of Mathematical Sciences, vol.23. no.3, pp. 243-267. 7. Manna, SK &Chaudhuri, KS 2006, ‘An EOQ model with ramp type demand rate, time dependent deterioration rate, unit production cost and shortages’, European Journal of Operations Research, vol. 171, pp. 557-566. 8. Min, J & Zhou, Y.W, 2009 ‘A perishable inventory model under stock-dependent selling rate and shortage-dependent partial backlogging with capacity constraint’, International Journal of Systems Science, vol.40, pp. 33-44. 9. Ranganayaki, S, Kasthuri, R &Vasanthi, P, 2019 ‘Inventory model with demand dependent on Unit price under fuzzy parameters and Decision variables’, International Journal of Recent Technology and Engineering, vol.8, issue.3, pp.784-788. 10. 10.Teng, J.T & Yang, H.L, 2007 ‘Deterministic inventory lot-size models with time varying demand and cost under generalized holding costs’, Information and Management Sciences, vol.18, issue.2, pp.113-125. 11. Vasanthi, P, Ranganayaki, S &Kasthuri, R, 2019 ‘Fuzzy EoQ model with Shortages using Kuhn-Tucker conditions’, International Journal of Engineering and Advanced Technology, vol.8, issue.6, pp.822-827. Authors: Nitin Krishna V, Ragunath B, Kowshika Priya B, Sivaranjani M, Vasanthamani K
Paper Title: Smart Farm Assist Robot Abstract: Autonomy in agriculture is the need of the hour in today’s world. Advancements in technology have made it possible to design autonomous systems that carry out their intended operations efficiently, without any human intervention. However, most farmers still carry out agricultural operations manually using simple and conventional tools like a wooden plough, sickle, etc. Large-scale mechanization and autonomous systems are affordable only by medium and large class farmers who possess more than 2.00 hectares of agricultural land. Marginal and small-class farmers find it difficult in managing the workforce at an affordable cost. A user- friendly cost-effective approach will be a valuable support system for this sector. This paper proposes a novel design of a seed sowing robot with two operating modes; manual control by the operator and remote operation through GPS. The proposed seed sowing bot extracts the features of the agricultural field under consideration and adopts the optimal speed for seed sowing. Parameters like temperature, humidity, and soil moisture, which are pivotal in carrying out agricultural operations are measured by the use of different sensors embedded in the robot. Arduino ATMEGA2560 controls the locomotion of the bot and Raspberry Pi is used for image classification and obstacle detection. Sunset and the presence of rain are detected and the corresponding feasible actions are programmed to be followed by the robot automatically. A user-friendly mobile application has been developed to issue commands to the robot. The robot intends to reduce human efforts and provide intelligent aid to marginal and small class farmers while being affordable
Keywords: Agribot, Autonomous agriculture, Smart farming, Seed sowing robot. 64. References: 1. Varun B Krishnan (2018), What the agriculture census shows about land holdings in India. The Hindu, October 3, 2018. 369-376 2. Michell Zappa (2014), Emerging Agriculture Technologies That Will Change The World. Policy Horizons Canada, May 6, 2014. 3. Shubam Khandelwal, Neha Kaushik and Manoj K. R. Pandey (2017). “AgRo-Bot: An Autonomous Robot”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 5, May-June 2017. 4. ShivaprasadB. S., RavishankaraM. N., and Shoba B. N., (2014), “Design and Implementation of Seeding and Fertilizing Agriculture Robot”, International Journal of Application or Innovation in Engineering & Management, Vol. 3, Issue 6, June 2014. 5. Shavon McGlynn and Drew Walters (2019),“Agricultural Robots: Future Trends for Autonomous Farming”, International Journal of Emerging Technologies and Innovative Research,Vol.6, Issue 4, April 2019. 6. Shraddha Muley (2017), “Robotic Vehicle for Seed Planting & Weeding Applications”, International Journal for Innovative Research in Science & Technology, Vol 3, Issue 12, May 2017. 7. Shaik K., Prajwal E., Sujeshkumar B., Bonu M., and BalapanuriVamseedhar Reddy (2018), “GPS Based Autonomous Agricultural Robot”,International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, 2018, pp. 100- 105. 8. Sowjanya K. D., Sindhu R., Parijatham M., Srikanth K., and Bhargav P., (2017), “Multipurpose autonomous agricultural robot”, 2017International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2017, pp. 696-699 9. Sweety Dutta, Udit Shanker, SulekhaKatiyar, Venktesh Singh, Mohd. Nayab Zafar and Mohanta, J. C., (2019),“Development and Fabrication of an Autonomous Seed Sowing Robot”, 2019 IOP Conf. Ser.: Mater. Sci. Eng. 691 012023. 10. AkshayNilawar, Pradnya P., Shilvant and Parmar Harpreet Kaur (2018), “Wireless Agricultural Seed Sowing Robot”,International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Vol VII, Issue IV, April 2018, ISSN 2278- 2540 11. Gowthami K., Greeshma K., and Supraja N., (2019),“Smart Farming Using Agri-Bot”, International Journal of Applied Engineering Research,Vol 14, Issue 6, 2019 ISSN 0973-4562 12. Ranjitha B., Nikhitha M. N., Aruna K., Afreen K., and Venkatesh Murthy B. T., (2019),“Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth/Android App”,International Conference on Electronics Communication and Aerospace Technology [ICECA 2019], IEEE Xplore ISBN: 978-1-7281-0167-5. Authors: S. Sree Hari Raju, E.G. Rajan Estimating Visual Quality in Skin Pictures Obtained by Optical Cameras and Analysis by Paper Title: Morphological Filters Abstract: This paper portrays the utilization of morphological channels for breaking down skin surface pictures. The factual boundaries and visual quality proportion of skin pictures caught by optical cameras are given. Morphological channels are blends of the fundamental activities of expansion and disintegration. For example if the activity of widening is spoken to by the image 1 and that of disintegration by 0, at that point a twofold string 0110 would demonstrate that the morphological tasks of disintegration, two enlargements and one disintegration to be done on a given picture with the equivalent organizing component. 65. Keywords: Morphological filters, Dilation, Erosion, Dermatologist, optical camera. 377-383 References: 1. Maton, Anthea; Jean Hopkins; Charles William McLaughlin; Susan Johnson; MaryannaQuon Warner; David La Hart; Jill D. Wright (1893). Human Biology and Health. Englewood Cliffs, New Jersey, USA: Prentice Hall. ISBN 978-0-13-981176-0. 2. Wilkinson, P.F. Millington, R. (2009). Skin (Digitally printed version ed.). Cambridge: Cambridge University Press. pp. 49–50. ISBN 978-0-521-10681-8. 3. Bennett, Howard (25 May 2014). "Ever wondered about your skin?". The Washington Post. Retrieved 27 October 2014. 4. Stücker, M.; A. Struk; P. Altmeyer; M. Herde; H. Baumgärtl; D. W. Lübbers (2002). "The cutaneous uptake of atmospheric oxygen contributes significantly to the oxygen supply of human dermis and epidermis". The Journal of Physiology. 538 (3): 985–994. doi:10.1113/jphysiol.2001.013067. ISSN 0022-3751. PMC 2290093. PMID 11826181 5. "The human proteome in skin - The Human Protein Atlas". www.proteinatlas.org. 6. Uhlén, Mathias; Fagerberg, Linn; Hallström, Björn M.; Lindskog, Cecilia; Oksvold, Per; Mardinoglu, Adil; Sivertsson, Åsa; Kampf, Caroline; Sjöstedt, Evelina (23 January 2015). "Tissue-based map of the human proteome". Science. 347 (6220): 1260419. doi:10.1126/science.1260419. ISSN 0036-8075. PMID 25613900. 7. Edqvist, Per-Henrik D.; Fagerberg, Linn; Hallström, Björn M.; Danielsson, Angelika; Edlund, Karolina; Uhlén, Mathias; Pontén, Fredrik (19 November 2014). "Expression of Human Skin-Specific Genes Defined by Transcriptomics and Antibody-Based Profiling". Journal of Histochemistry&Cytochemistry. 63 (2): 129–141. doi:10.1369/0022155414562646. PMC 4305515. PMID 25411189. 8. Muehlenbein, Michael (2010). Human Evolutionary Biology. Cambridge University Press. pp. 192–213. ISBN 978-1139789004. 9. Jablonski, N.G. (2006). Skin: a Natural History. Berkeley: University of California Press. ISBN 978-0520954816. 10. Handbook of General Anatomy by B. D. Chaurasia. ISBN 978-81-239-1654-5 11. "Pigmentation of Skin". Mananatomy.com. Retrieved 3 June 2019. 12. Webb, A.R. (2006). "Who, what, where, and when: influences on cutaneous vitamin D synthesis". Progress in Biophysics and Molecular Biology. 92 (1): 17–25. doi:10.1016/j.pbiomolbio.2006.02.004. PMID 16766240. 13. Jablonski, N.G.; Chaplin (2000). "The evolution of human skin coloration". Journal of Human Evolution. 39 (1): 57–106. doi:10.1006/jhev.2000.0403. PMID 10896812. 14. "The Fitzpatrick Skin Type Classification Scale". Skin Inc. (November 2007). Retrieved 7 January 2014. 15. "Fitzpatrick Skin Type"(PDF). Australian Radiation Protection and Nuclear Safety Agency. Archived from the original(PDF) on 31 March 2016. 7 January 2014. 16. Alexiades-Armenakas, M. R., et al. The spectrum of laser skin resurfacing: nonablative, fractional, and ablative laser resurfacing. J Am AcadDermatol. 2008 May;58(5):719-37; quiz 738-40 17. Cutroneo, Kenneth R.; Kenneth M. Sterling (2004). "How do glucocorticoids compare to oligo decoys as inhibitors of collagen synthesis and potential toxicity of these therapeutics?". Journal of Cellular Biochemistry. 92 (1): 6–15. doi:10.1002/jcb.20030. ISSN 0730-2312. PMID 15095399. 18. Oikarinen, A. (2004). "Connective tissue and aging". International Journal of Cosmetic Science. 26 (2): 107. doi:10.1111/j.1467- 2494.2004.213_6.x. ISSN 0142-5463. 19. Gilchrest, BA (1990). "Skin aging and photoaging". Dermatology Nursing / Dermatology Nurses' Association. 2 (2): 79–82. PMID 2141531. 20. WI, Kenneth Todar, Madison. "Immune Defense against Bacterial Pathogens: Innate Immunity". Textbook of bacteriology.net. Retrieved 19 April 2017. Authors: D. Arokiya Pushparaj, N. Karunakaran, N. Alagappan Performance Analysis of 6063 Aluminium Alloy Semi-Circular Two Phase Closed Thermosyphon Paper Title: Tpct using Fe3o4 and Graphene Nano-Fluid Abstract: Nanofluids stability on rest is important to characterize the nanofluids thermophysical properties before being used on different thermal systems. However, this stability can be modified during devices operation because of different thermal loads, fluid movements and phase changes. Particularly, in Two Phase Closed- Thermosyphon (TPCT). Input response are heat input, flow rate and inclination angle. Graphene and fe304 nanofluids used as working fluid in the fill ratio of 50%. An attempt is made to optimise the process parameters with Response Surface Methodology (RSM) using Box - Behnken design for 6063 aluminium alloy (AA) semi- circular two phase closed thermosyphon (TPCT). Experiments are conducted by varying the mass flow rates of water at condenser section and by varying the heat input at evaporator section and varying inclination angle. The effects of variables on the process parameters are studied. The study, predominantly, aims at assessing how the variables affect the process parameters.
Keywords: Particularly, in Two Phase Closed-Thermosyphon (TPCT).
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Paper Title: Managing the Triangular Bond of the EBP for SQU Students Through the Proposed Test Model Abstract: Smart city technologies are becoming dominant. One of the important pillars of a smart city is education. All citizens are learners in the smart city. To fulfill goals of a smart city, supported technology should be boosted. As a result of incorporation with the growing Information and Communication Technologies (ICT) along with software and hardware, learning environments have undergone numerous changes. Students and strategies represent the center point while adopting the online learning environment. This paper highlights important issues to shape electronic education. It considers Moodle-LMS to figure out the triangular relationship between the engagement, behavior and performance (EBP) of Sultan Qaboos Uinversity (SQU) students in any course. Moodle collects students’ information and generates students’ profiles. Researchers could analyze students’ profiles to findout relationships between different attributes (i.e. EBP). This could guide instructors to know how engagement and behavior could be used as an indication to improve students’ overall performance. This paper aims to suggest a test model intended to guide instructors to prepare personalized materials that suit individual students needs and overcome their deficiency towards a better performance. Another objective is to integrate the proposed model within Moodle environment. This paper uses data of 14 students from a fully online course at SQU. This is used to explore whether patterns of student engagement and behavior are correlated with student performance. Findings reveal the existence of a positive relationship between EBP attributes. Authors recommend instructors to use students’ results to recognize students who need additional support in a specific course.
Keywords: Student Engagement, Student Behavior, Student Performance, Learning Object, Moodle, Logfile
References: 1. I. Nurjaman, “The Challenge of Implementing Smart Learning: Learning Behavior Readiness for Indonesian Students”, International Journal of Education, Information Technology, and others, 2018, 1(2), 25-29. 2. M. Epstein, M. Atkins, D. Cullinan, K. Kutash, and K. Weaver, “Reducing behavior problems in the elementary school classroom”, IES Practice Guide, 2008, 20(8), 12-22. 3. L. Lee, and K. Hao, “Designing and evaluating digital game-based learning with the ARCS motivation model, humor, and animation”, International Journal of Technology and Human Interaction (IJTHI), 2015, 11(2), 80-95. 4. C. Lee, Y. Cheng, S. Rai, and A. Depickere, “What affect student cognitive style in the development of hypermedia learning system?”, Computers & Education, 2005, 45(1), 1-19. 5. M. Zorrilla, S. Millan, and E. Menasalvas, “Data web house to support web intelligence in e-learning environments”, In 2005 IEEE International Conference on Granular Computing, Beijing, China, 2005, July, (Vol. 2, pp. 722-727). 67. 6. R. Estacio, and Jr. Raga, “Analyzing students online learning behavior in blended courses using Moodle”, Asian Association of Open Universities Journal, 2017, 12(1), 52-68. 7. I. Al-Kindi, and Z. Al-Khanjari, “The Smart Learning Management System (SLMS)”, Free and Open Source Software Conference 391-400 (FOSSC-2019), Muscat, Sultanate of Oman, 2019, February, pp. 32-35. 8. M. Dixson, “Measuring student engagement in the online course: The Online Student Engagement scale (OSE)”, Online Learning, 2015, 19(4), n4. 9. M. Guilloteaux, “Student engagement during EFL high school lessons in Korea: An experience-sampling study”, Foreign Languages Education, 2016, 23(1), 21-46. 10. K. Manwaring, R. Larsen, C. Graham, C. Henrie, and L. Halverson, “Investigating student engagement in blended learning settings using experience sampling and structural equation modeling”, The Internet and Higher Education, 2017, 35, 21-33. 11. T. Nguyen, M. Cannata, and J. Miller, “Understanding student behavioral engagement: Importance of student interaction with peers and teachers”, The Journal of Educational Research, 2018, 111(2), 163-174. 12. M. Hussain, W. Zhu, W. Zhang, and S. Abidi, “Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores”, Computational intelligence and neuroscience, 2018, https://doi.org/10.1155/2018/6347186. 13. N. Mirriahi, J. Jovanovic, S. Dawson, D. Gašević, and A. Pardo, “Identifying engagement patterns with video annotation activities: A case study in professional development”, Australasian Journal of Educational Technology, 2018, 34(1). 14. F. Martin, and D. Bolliger, “Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment”, Online Learning, 2018, 22(1), 205-222. 15. S. Wasik, J. Barrow, C. Royal, R. Brooks, L. Dames, L. Corry, and C. Bird, “Online Counselor Education: Creative Approaches and Best Practices in Online Learning Environments”, Research on Education and Psychology, 2019, 3(1), 1-1. 16. J. Lee, H. Song, and A. Hong, “Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning”, Sustainability, 2019, 11(4), 985. 17. Z. Al-Khanjari, and I. Al-Kindi,“ Integrating MOOC with Open Source Moodle: The New Direction of Learning at Sultan Qaboos University”,.In 2018 International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, 2018, January , (pp. 47-51). IEEE. 18. S. Dias, and J. Diniz, “Towards an enhanced learning management system for blended learning in higher education incorporating distinct learners' profiles”, Journal of Educational Technology & Society, 2014, 17(1), 307-319. 19. I. Al-Kindi, and Z. Al-Khanjari, “Collaborative learning: A new horizon for E-learning in Sultan Qaboos University using concepts of MOOC and cloud computing”, In 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), Muscat, Sultanate of Oman, 2017, April, (pp. 1-6). IEEE. 20. J. Mostow, and J. Beck, “Some useful tactics to modify, map and mine data from intelligent tutors”, Natural Language Engineering, 2006, 12(2), 195-208. 21. G. Sinatra, B. Heddy, and D. Lombardi, “The Challenges of Defining and Measuring Student Engagement in Science”, Educational Psychologist, 2015, 50:1, 1-13, DOI: 10.1080/00461520.2014.1002924 22. S. Lamborn, F. Newmann, and G. Wehlage, “The significance and sources of student engagement”, Student engagement and achievement in American secondary schools, 1992, 11-39. 23. S. McGeown, D. Putwain, E. Simpson, E. Boffey, J. Markham, and A. Vince, “Predictors of adolescents' academic motivation: Personality, self-efficacy and adolescents' characteristics”, Learning and Individual Differences, 2014, 32, 278-286. 24. K. Bhagat, Y. Wu, and C. Chang, “The impact of personality on students' perceptions towards online learning”, Australasian Journal of Educational Technology, 2019, 35(4). 25. T. Magnussen, “Learning behavior, strategy and performance: a structural equation modeling study”, (Master's thesis), University of Oslo, 2010. 26. S. Johns, and M. Wolking, “The Core Four of Personalized Learning: The Elements You Need to Succeed”, Education Elements, https://www.edelements.com/hubfs/Core_Four/Education_Elements_Core_Four_White_Paper.pdf, [Accessed January 29, (2020)]. 27. Excel 2016 - Groups and Subtotals. (n.d.), https://edu.gcfglobal.org/en/excel2016/groups-and-subtotals/1/, [Accessed August 21, (2020)]. 28. Z. Al-Khanjari, and I. Al-Kindi, “Proposing the EBP Smart Predictive Model Towards Smart Learning Environment”, Journal of Talent Development and Excellence, 2020, 12(2s), pp2422-2438. 29. Z. Al-Khanjari, S. Kutti, and F. Al-Mahri, “RMLOM: Reusable Multipurpose Learning Object Model”, In Proceedings of The 4th International Conference on Information Systems, Bangkok, Thailand, 2010, March, (pp. 11-13). 30. F. Al-Mahri, “Master Thesis: Learning Content Management System in support of Personalized Learning”, Master of Science, Sultan Qaboos University, Muscat, Sultanate of Oman, 2008. 31. A. Chikh, “A general model of learning design objects”, Journal of King Saud University-Computer and Information Sciences, 2014, 26(1), 29-40. https://doi.org/10.1016/j.jksuci.2013.03.001 Authors: Suneetha Eluri, Naga Santosha Lahari Penmatsa
Paper Title: Sarcasm Detection of Sentiments in Telugu Language Abstract: Sarcasm is usually used by people to either tease/irritate others or simply for comic purposes. The presence of sarcasm becomes certain as it is difficult to be identified by basic sentiment analysis method. Sarcasm detection is addressed with various rule-based methods, statistical approaches, and classifiers in machine learning , most of these are introduced to identify sarcasm in text written in English as it is a popular language on the internet. Although the groundwork done on sarcasm detection on various Indian languages like Telugu is limited. Hence, this paper presents a Deep learning model based on neural networks to detect sarcasm in Telugu news headlines taken from various websites . The proposed model comprises of Convolutional Neural Networks(CNN) and next a Long short-term memory(LSTM) Network which is a modified version of Recurrent neural networks (RNN) and lastly a fully connected dense layer is added to classify the sentiments into sarcastic and non-sarcastic. A pre-trained word embeddings GloVe are used in the model
Keywords: Convolutional Neural Networks, Deep learning, Long-short term memory, Sarcasm.
References: 1. B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lectures on Human Language Technologies, May 2012 ,vol. 5, no. 1, pp. 1– 167, 2012. 2. S. K. Bharti, K. S. Babu, and S. K. Jena, “Parsing-based sarcasm sentiment recognition in twitter data,” in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) ACM, 2015, pp. 1373–1380. 3. M. Bouazizi and T. Ohtsuki, “A Pattern-Based Approach for Sarcasm Detection on Twitter,” IEEE Access, vol. 4, pp. 5477–5488, 2016, doi: 10.1109/ACCESS.2016.2594194 4. Diana Maynard and Mark A Green wood, “Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis”, in Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14),2014, pp. 4238– 68. 4243 5. P. Dharwal , T. Choudhury, Rajat Mittal, Praveen Kumar “Automatic sarcasm detection using feature selection”, in proceedings of 3rd International Conference on Applied and Theoretical computing and communication and Technology ,2017 IEEE ,doi 401-406 : 10.1109/ICATCCT.2017.8389102 6. Ashwin Rajadesingan, Reza Zafarani , Huan Liu, “Sarcasm detection on twitter: A behavioral modeling approach”, in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, ACM, 2015, pp. 97–106. 7. soujanya poria ,Erik cambria , Devamanyu Harzarika, prateek vij, “A Deeper look into sarcastic tweets using deep convolution neural networks”, in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1601–1612, Osaka, Japan, December 11-17 2016. 8. Le Hoang Son et al. “Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model with Convolution Network”,vol.7,pp.23319-23328,2019IEEE,doi:10.1109/ACCESS.2019.2899260 9. Yessi Yunitasari , Aina Musdholifah , Anny Kartika Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets”, Indonesian Journal of Computing and Cybernetics Systems, Volume 13, Issue 1, 2019, pp.53-62 10. Shih-Kai Lin, Shu-Kai Hsieh, “Sarcasm Detection in Chinese Using a Crowd sourced Corpus”, The 2016 Conference on Computational Linguistics and Speech Processing ROCLING 2016, pp. 299-310 11. Dana Al-Ghadhban , Eman Alnkhilan , Lamma Tatwany , Muna Alrazgan , “Arabic Sarcasm Detection in Twitter” , International Conference on Engineering & MIS ,2017 IEEE, doi : 10.1109/ICEMIS.2017.8272990 12. Santosh Kumar Bharti , Korra Sathya Babu, Rahul Raman, “Context-based Sarcasm Detection in Hindi Tweets” , 9th International Conference on Advances in Pattern Recognition (ICAPR-2017), doi : 10.1109/ICAPR.2017.8593198 13. J. Pennington, R. Socher, and C. Manning, ‘‘Glove: Global vectors for word representation,’’ in Proc. Conf. Empirical Methods Natural Lang.Process. (EMNLP), 2014, pp. 1532–1543. 14. Y. Kim. (2014). ‘‘Convolutional neural networks for sentence classification.’’[Online]. Available: https://arxiv.org/abs/1408.5882. 15. A. Ghosh and T. Veale, ‘‘Fracking sarcasm using neural network,’’ in Proc.7th Workshop Comput. Approaches Subjectivity, Sentiment Social Media Anal., 2016, pp. 161–169. 16. S. Hochreiter and J. Schmidhuber “Long short-term memory” Neural computation, 9(8):1735–1780, 1997. 17. Suneetha Eluri, Sumalatha Lingamgunta “ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language” published in i-manager Journal on Computer Science, Volume Issue 1, pp. 25-35, ISSN Print: 2347-2227, March-May 2019 18. Suneetha Eluri, Sumalatha Lingamgunta “A Statistical Method for Named Entity Recognition in Telugu, an Indian Language” published in International Journal of Recent Technology and Engineering (IJRTE): ISSN: 2277-3878, Volume -8 Issue-2, pp. 4211- 4216, July 2019. Authors: Subramanya K, M S Nagaraj Optimization of Demand Side Management and DG Placement in the Distribution System with Paper Title: Demand Response Abstract: In the distribution system, Distribution Generation (DG) plays an vital role and by optimizing the DG 69. the performance and efficiency is improved in the distribution system network. Demand Side Management (DSM) deals with this process of optimizing the DGs in a network. In this paper, a new algorithm is proposed 407-414 for optimal DSM including the demand response and DG units. The optimal capacity and location of the DGs to be connected in the network are selected using real and reactive power loss and voltage profile. The environmental conditions and economic operation of the system is ensured by optimizing the daily performance of the multiple DG units and grid parameters with and without inclusion of demand response. A non dominated sorting firefly algorithm is used to obtain optimization of the functions and decision making fuzzy system is used to decide the best possible scenario from the list of optimized solutions. It is tested with IEEE 33 bus system. The validity of the proposed DSM methodology is verified with the simulation results.
Keywords: Demand side management, Distributed Generation, Demand Response, Non dominated Sorting Firefly Algorithm, fuzzy decision making.
References: 1. L. Gelazanskas, A. Gamage, “Demand side management in smart grid: a review and proposals for future direction”, Sustainable Cities Soc., vol. 11, pp. 22-30,2014. 2. M. Behrangrad, “A review of demand side management business models in the electricity market”, Renewable Sustainable Energy Rev., vol. 47, pp. 270-283, 2015. 3. H. Shayeghi, M. Alilou, “Application of multi objective hfapso algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network”, J. Oper. Autom. Power Eng., vol. 3, p.131-146, 2015. 4. E. Heydarian, H. A. Aalami, “Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power”, J. Oper. Autom. Power Eng., vol. 4, pp. 104-116, 2016. 5. Sanjeeva Kumar R A,Kavya Prayaga, “A Weighted Sum of Multi-Objective Function based Reliability Analysis with the Integration of Distributed Generation”,International Journal of Engineering and Advanced Technology(IJEAT),Volume-9 Issue-4,April 2020. 6. M. Wang, Y. Ting, Y. Mu, H. Jia, L Shiguang, “A unified management and control model of demand-side resources”, Energy Procedia, vol. 105, pp. 2935-2940, 2017. 7. F. Verrilli, G. Gambino, S. Srinivasan, G. Palmieri, C. Vecchio, L. Glielmo, “Demand side management for heating controls in microgrids”, Int. Fed. Autom. Control, pp. 611- 616, 2016. 8. D. Müller, A. Monti, S. Stinner, T. Schlosser, Th. Schütz, P. Matthes, H. Wolisz, Ch. Molitor, H. Harb, R. Streblow, “Demand side management for city districts”, Build. Environ., vol. 91, pp. 283-293, 2015. 9. Sanjeeva Kumar R A, Sudarshana Reddy H R and Ananthapadmanabha T, “Enhancement of Power Quality in Distribution System by Optimal Integration of Distributed Generators Using Hybrid Flower Pollination Algorithm”, in International Journal of Electrical Engineering and Technology(IJEET), Volume 9, Issue 3, May-June 2018, Pp. 146-153. 10. H. Li, Q. An, B. Yu, J. zhao, L. Cheng, Y. Wang, “Strategy analysis of demand side management on distributed heating driven by wind power”, Energy Procedia, vol. 105, pp. 2207-2213, 2017. 11. Z. Wu, H. Tazvinga, X. Xia, “Demand side management of photovoltaic-battery hybrid system”, Appl. Energy,vol. 148, pp. 294-304, 2015. 12. Sanjeeva Kumar R A, Sudarshana Reddy H R and Ananthapadmanabha T, “Analytical Approach for Optimal Placement of Distributed Generators in Power System”, in International Journal of advent in research Technologies (IJRAT), July 2018, Volume 6, Issue 7. 13. K. Ma, C. Wang, J. Yang, Z. Tian, X. Guan, “Energy management based on demand-side pricing: a supermodular game approach”, IEEE Access, vol. 5, pp. 18219-18228, 2017. 14. Sanjeeva Kumar R A, Sudarshana Reddy H R and Ananthapadmanabha T, “Multi-Objective Based Analytical Approach For Optimal Placement Of Distributed Generators In Power System”, in Journal of Emerging Technologies and Innovative Research, Vol5, Issue 7, page no.605-609, July 2018, 15. M. Aman, G. Jasmon, A. Bakar, H. Mokhlis, “A new approach for optimum simultaneous multi-DG distributed generation unit’s placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm”, Energy, vol. 66, pp. 202-215, 2014. 16. H. Bagheri, M. H. Ali, and M. Rizwan, “Novel hybrid fuzzy-intelligent water drops approach for optimal feeder multi objective reconfiguration by considering multiple-distributed generation”, J. Oper. Autom. Power Eng., vol. 2, pp. 91-102, 2014. 17. M. Moghaddam, A. Abdollahi, M. Rashidinejad, “Flexible demand response programs modeling in competitive electricity markets”, Appl. Energy, vol. 88, pp. 3257-3269, 2011. 18. Kavya prayaga, Sanjeeva Kumar R APower Quality Analysis Of Distribution System Using Hybrid Intelligent Algorithm By Optimal Integration Of Dg’s Journal of Emerging Technologies and Innovative Research, Vol 06, Issue 5, May 2019,[19] Xin-she. Yang, “Firefly algorithms for multimodal optimization”, arXiv: 1003.1466v1 [math.OC], , (7 Mar 2010). 19. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., vol. 6, pp. 182-197, 2002. Authors: Neeraja P K, Ramadass Narayanadass
Paper Title: A Modified Fused Floating Point Three Term Adder Abstract: This paper is about a modified architecture for a fused floating point three term adder. The important feature of a fused floating-point three-term adder is its ability to do multiple additions in same block to get better performance as well as accuracy compared to a conventional discrete floating point adder. The parallel prefix adder is one amongst the fastest adders and out of which the han-carlson adder represents a blend of the kogge- stone adders and brent-kung adder. In this work, han carlson adder is used to enhance the performance of the three term adder along with various optimization techniques. The adder is implemented using Verilog language in Xilinx ISE Design suite 14.2 and all Simulations are carried out in Isim simulator. Synthesis is done using Cadencetool.
70. Keywords: Floating point adder, Parallel prefix addition, Kogge Stone adder, Han Carlson adder. 415-419 References: 1. IEEE Standard for Floating-Point Arithmetic ANSI/IEEE Standard 754-2008, IEEE, Inc.,2008. 2. J.SohnandE.E.Swartzlander,Jr.,”AFusedFloating-PointThree- Term Adder”, IEEE Transactions On Circuits And Systems—I: Regular Papers, Vol. 61, No. 10, October2014. 3. A. Tenca, “Multi-operand floating-point addition,” in Proc. 21st Symp. Computer Arithmetic, 2009, pp.161–168. 4. Y. Tao, G. Deyuan, F. Xiaoya, and R. Xianglong, “Three-operand floating-point adder,” in Pro. 12th IEEE Int. Conf. Comput. Inf. Technol., 2012, pp. 192–196. 5. Swapna K. Gedam and Pravin P. Zode, “Parallel Prefix Han-Carlson Adder”, International Journal of Research in Engineering and AppliedSciences. 6. Sreenivaas Muthyala Sudhakar, Kumar P. Chidambaram andEarl E. Swartzlander Jr. “Hybrid Han-Carlson Adder”, 978-1-4673-2527-1/12/$31.00©2012 IEEE 7. Geeta Rani, Sachin Kumar,” Delay Analysis of Parallel-Prefix Adders”, International Journal of Science and Research(IJSR) 8. M. P. Farmwald, “On the Design of High Performance Digital Arithmetic Units,”Ph.D. dissertation, Computer Science, Stanford University, Stanford, CA, USA, 1981. 9. S.F.Oberman, H.Al-Twaijry, andM.J. Flynn,“The SNA Pproject: Designoffloatingpointarithmeticunits,”inProc.14thIEEESymp. Computer Arithmetic, 1997, pp.156–165. 10. P. M. Seidel and G. Even, “Delay-optimized implementation of 11. IEEE floating-point addition,” IEEE Trans. Computers, vol. 53, no. 2, pp. 97–113, Feb. 2004. Authors: Suneetha Eluri, Vasu Kumar Pilli
Paper Title: Global Word Sense Disambiguation of Polysemous Words in Telugu Language Abstract: Word Sense Disambiguation (WSD) is a significant issue in Natural Language Processing (NLP). WSD refers to the capacity of recognizing the correct sense of a word in a given context. It can improve numerous NLP applications such as machine translation, text summarization, information retrieval, or sentiment analysis. This paper proposes an approach named ShotgunWSD. Shotgun WSD is an unsupervised and knowledge-based algorithm for global word sense disambiguation. The algorithm is motivated by the Shotgun sequencing technique. Shotgun WSD is proposed to disambiguate the word senses of Telugu document with three functional phases. The Shotgun WSD achieves the better performance than other approaches of WSD in the disambiguating sense of ambiguous words in Telugu documents. The dataset is used in the Indo-WordNet.
Keywords: shotgun sequencing, Word sense disambiguation, Word embedding, Telugu.
References: 1. Anuja Bharate, Devendra Gadekar, “Survey Paper on Natural Language Processing”. International Journal of Computer Engineering and Applications, Volume VIII, Issue III, Part I, December 14. 2. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. P. Kuksa. Natural language processing (almost) from scratch. CoRR, abs/1103.0398, 2011. 3. Sense Disambiguation Techniques: A Survey Rekha Jain1, Sulochana Nathawat2, Dr. G. N. Purohit, Vol.1 Nov-Dec 2012. 4. Pushpak Bhattacharyya, IndoWordNet, Lexical Resources Engineering Conference 2010 (LREC 2010), Malta, May, 2010. 71. 5. Bhingardive, S., & Bhattacharyya, P. (2017). Word sense disambiguation using IndoWordNet. In The WordNet in Indian Languages (pp. 243-260). Springer, Singapore. 6. R.Navigli, Word Sense Disambiguation: a Survey, ACM Computing Surveys, Vol. 41, No.2, ACM Press, pp. 1-69 2009. 420-425 7. R. Mihalcea, P. Tarau, and E. Figa. PageRank on semantic networks with application to word sense disambiguation. In Proc. of COLING, 2004. 8. G.Tsatsaronis, IraklisVarlamis, and Kjetil Norvag, An experimental study on unsupervised graph-based word sense disambiguation, In Proc. of CICLING, 2010. 9. L. Vial, B. Lecouteux, and D. Schwab, ``Sense embeddings in knowledge-based word sense disambiguation,'' in Proc. IWCS, 2017 10. 10. O. Dongsuk, S. Kwon, K. Kim, and Y. Ko, ``Word sense disambiguation based on word similarity calculation using word vector representation from a knowledge-based graph,'' in Proc. COLING, Aug. 2018, pp. 2704_2714. 11. Suneetha Eluri, Sumalatha Lingamgunta “Rule Based Approach for finding Lexical Morphemes in Telugu, an Indian Language” Published, Journal of Advanced Research Dynamic Control Systems, Volume 10, Issue 12, Page No: 419-420, August 2018. ISSN 1943-023X [Elsevier Scopus Indexed(Free) Impact Factor 0.11] 12. Suneetha Eluri, Sumalatha Lingamgunta “ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language” published in i-manager Journal on Computer Science, Volume 7, Issue 1, Page No: 25-35, ISSN Print: 2347-2227, March-May 2019 [ Double Blind Peer Reviewed Free Journal with Impact Factor 0.750]. 13. Suneetha Eluri, Sumalatha Lingamgunta “A Statistical Method for Named Entity Recognition in Telugu, an Indian Language” published in International Journal of Recent Technology and Engineering (IJRTE): ISSN: 2277-3878, Volume -8 Issue-2, Page No:4211-4216, July 2019. [Free journal with Scopus Indexing from 2018]. 14. Meryeme Hdni et al, Word Sense Disambiguation for Arabic Text Categorization, IAJIT, Vol.13, 2016. 15. Neeraja Koppula, Dr. B. Padamaja Rani, Word Sense Disambiguation Using Knowledge based Approach in Regional Language, Vol. 10, 2018. Language Processing, NCRSTCST, Vol.4,2013. 16. A. Butnaru, R. T. Ionescu, and F. Hristea, ‘‘ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing,’’ in Proc. EACL, Apr. 2017, pp. 916–926. 17. Orkphol, K.; Yang, W. Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet. Future Internet 2019, 11, 114. Authors: Shawki Abouel-seoud, Mohamed N. Mansy, Mahmoud Elshaabany, Manar Eltantawie, Eid Ouda Evaluation of Vibration Characteristics and Theoretical Analysis for In-Wheel Driving Electric Paper Title: Road Vehicle Abstract: Hub-motor driven electric vehicles consider the upcoming technology in the vehicle industry. It has several merits such as lightweight, good accelerator responsiveness, flexibility when designing different drivetrains, operated at most operative efficiency points, and increased space-saving compared with the traditional electric vehicle driven by a central motor. The energy demand around the world is increasing dramatically. So, the researchers seek to find alternatives to the non-renewable resources represented by fossil fuels. Electric vehicles are the most suitable vehicle to avail of this type of energy due to their high efficiency 72. and zero fuel consumption and emission. The electric-powered vehicle is distinctive with low noise and vibration which improves the vibration characteristics compared with internal combustion engine vehicles. In this paper, eight degrees of freedom passive quarter car suspension system of an in-wheel drive powered electric 426-432 vehicle equipped with a battery/ultracapacitor hybrid energy storage system is studied and analyzed. The system was simulated and tested in both time and frequency domains via the MATLAB/Simulink environment.
Keywords: Electric Vehicles, Quarter Car, In-wheel Motors, Vibration Characteristics, battery/ultracapacitor.
References: 1. N. Ding, K. Prasad, and T. T. Lie, “The electric vehicle: A review”, International Journal of Electric and Hybrid Vehicles, vol. 9, pp. 49, 2017. 2. R. Vos, I. Besselink, and H. Nijmeijer, “Influence of in-wheel motors on the ride comfort of electric vehicles”, Proceedings of the 10th International Symposium on Advanced Vehicle Control (AVEC10), pp. 835-840, 2010. 3. M. S. Rahman, and K. M. G. Kibria, “Investigation of Vibration and Ride Characteristics of a Five Degrees of Freedom Vehicle Suspension System”, Procedia Engineering, vol. 90, pp. 96-102, 2014. 4. V. Vito, M. Januar, and P. Sejati, “Modeling of Passenger Ride-Comfort Enhancement through Designing Seat Cushion with Scilab- Xcos”, Cylinder: Jurnal Ilmiah Teknik Mesin, vol. 4, no. 1, 2018. 5. S. A. A. Bakar, R. Masuda, H. Hashimoto, T. Inaba, H. Jamaluddin, R. A. Rahman, and P. M. Samin, “Improving Electric Vehicle Conversion’s Ride and Handling Performance Using Active Suspension System”, Advanced Methods, Techniques, and Applications in Modeling and Simulation, pp. 258-267, 2012. 6. A. Seifi, R. Hassannejad, and M. A. Hamed, “Optimum design for passive suspension system of a vehicle to prevent rollover and improve ride comfort under random road excitations”, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, vol. 230, no. 4, pp. 426-441, 2015. 7. H. Fuse, T. Kawabe, and M. Kawamoto, “Speed Control Method of Electric Vehicle for Improving Passenger Ride Quality”, Intelligent Control and Automation, vol. 8, no. 1, pp. 29-43, 2016. 8. S. Rajendiran, and P. Lakshmi, “Simulation of PID and fuzzy logic controller for integrated seat suspension of a quarter car with driver model for different road profiles”, Journal of Mechanical Science and Technology, vol. 30, no. 10, pp. 4565-4570, 2016. 9. M. Agostinacchio, D. Ciampa, and S. Olita, “The vibrations induced by surface irregularities in road pavements – a Matlab® approach”, European Transport Research Review, vol. 6, no. 3, pp. 267-275, 2014. Authors: Madhusudhana G K, M B Sanjaypande, Raveesh B N Early Detection of Parkinson Disease Progression using Gaussian Naïve Bayes Machine Learning Paper Title: Approach by identifying Degeneration in Basal Ganglia Regions Abstract: Parkinson's Disease (PD) is a progressive neuro-degenerative disorder that affects millions of people across the globe. Analyzing volume changes in the basal ganglia seems to be a promising approach towards developing non-invasive and non-radioactive neuro-imaging markers for this disease. In this work, we report a study of classification based on the volumes of basal ganglia regions obtained from brain atlas. The study investigates the volume changes in certain anatomical structures of the basal ganglia region of PD affected subjects.
73. Keywords: basal ganglia, brain atlas, neuro-degenerative disorder, Parkinson's disease.
References: 433-435 1. Timothy R. Mhyre, James T. Boyd, Robert W. Hamill, and Kathleen A. Maguire-Zeiss, “Parkinson’s Disease”, PMCID: PMC4372387, NIHMSID: NIHMS659855, PMID: 23225012, PMC 2015 March 24. 2. Gennaro Pagano , Flavia Niccolini, Marios Politis, “Imaging in Parkinson’s disease”, Clinical Medicine 2016 Vol 16, No 4: 371–5, Clinical Medicine 2016 Vol 16, No 4: 371–5. 3. Cyril Atkinson-Clement, Serge Pint, Alexandre Eusebi, Olivier Coulon, “Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis”, SciencecDirect, NeuroImage:Clinical 16, 2017. 4. Prateek Majumder. (2020, May 10). Gaussian Naive Bayes [Online]. Available: https://iq.opengenus.org/gaussian-naive-bayes/ 5. Neurocon Image Repository, (2020, Jan 08) National Institute for Research and Development in Informatics, Romania, Department of Neurobiology, Beijing Institute of Geriatrics, Xuanwu Hospital, Capital Medical University, Parkinson Disease Centre of Beijing Institute for Brain Disorders.China, 6. [Online]Available: http://fcon_1000.projects.nitrc.org/indi/retro/parkinsons.html Authors: Digvijaysinh B Dodiya, Jayesh R Koisha
Paper Title: Design and Development of Chilli Seeder Abstract: India is the second-largest producer of chilli in the world. The seeds of chilli, capsicum etc. are commonly sown and developed in nurseries using plug tray for better germination. Then grown plants sent to farmland for further cultivation. It was observed that the process of sowing seeds in plug tray require more time because the seed are small and one has to drop it manually. Moreover, this is tedious work and the owner have to pay high for this. So, objective of the study was to evolve a Plug Tray Seeder which were affordable to nursery owners. Which work on pneumatic principle. It was designed in such manner that it has higher seedling rate and less power consumption. In addition, it should also reduce human effort and labour cost. The capacity of seeder, is assumed, depending on the tray size used which range between 27000 and 46800 cell per hour. The total cost of the precision plug seeder is estimated to be ₹17800 (US$242).
74. Keywords: The capacity of seeder, is assumed, depending on the tray size used which range between 27000 and 46800 cell per hour. 436-439
References: 1. Chen J M; Yu C C; Lei J H; Yu J M; Chang C F (1993). A multipurpose vacuum seed planter for vegetable crops plantings. Journal of Agriculture and Forestry, 42(1), 1-18 2. Kim D E; Chang Y S; Kim S H; Lee G I (2003). Development of vacuum nozzle seeder for cucurbitaceous seeds (I)—design factors for vacuum seeding large sized seeds. Journal of the Korean Society for Agricultural Machinery, 28(6), 525-530 3. Hu J; Hou J; Mao H (2003). Development and test of magnetic precision seeder for plug seedlings. Transactions of Chinese Society of Agricultural Engineering, 19(6), 122-125 4. B.B.Gaikwad, N.P.S. Sirohi (2007). Design of a low-cost pneumatic seeder for nursery plug trays. Biosystems Engineering 99(3):322- 329 5. Tarek H. A. Mohamed*; Hossam M. T. EL-Ghobashy;* Adel A. M. EL-Ashker *; Ahmed R.Hamed* (2017). An innovating precision sowing unit for tray nursery. Misr Journal of Agricultural Engineering 34(2), 725-750 6. D.A. Naik, H.M. Thakur (2017). Design and analysis of an automated seeder for small scale sowing applications for tray plantation method. International Journal of Engineering Research and Technology, Volume 10, Number 1. Authors: Suneetha Eluri, Vishala Siddu 75. Paper Title: A Knowledge Based Word Sense Disambiguation in Telugu Language Abstract: Telugu (遆졁屁) is one of the Dravidian languages which are morphologically rich. As within the other languages, it too consists of ambiguous words/phrases which have one-of-a-kind meanings in special contexts. Such words are referred as polysemous words i.e. words having a couple of experiences. A Knowledge based approach is proposed for disambiguating Telugu polysemous phrases using the computational linguistics tool, IndoWordNet. The task of WSD (Word sense disambiguation) requires finding out the similarity among the target phrase and the nearby phrase. In this approach, the similarity is calculated either by means of locating out the range of similar phrases (intersection) between the glosses (definition) of the target and nearby words or by way of finding out the exact occurrence of the nearby phrase's sense in the hierarchy (hypernyms/hyponyms) of the target phrase's senses. The above parameters are changed by using the intersection use of not simplest the glosses but also by using which include the related words. Additionally, it is a third parameter 'distance' which measures the distance among the target and nearby phrases. The proposed method makes use of greater parameters for calculating similarity. It scores the senses based on the general impact of parameters i.e. intersection, hierarchy and distance, after which chooses the sense with the best score. The correct meaning of Telugu polysemous phrase could be identified with this technique.
Keywords: Natural Language Processing (NLP), Polysemous, IndoWordNet, Word Sense Disambiguation (WSD), Intersection, Hierarchy, Senses, Distance measure.
References: 1. SuneethaEluri, SumalathaLingamgunta “ARPIT: Ambiguity Resolver for POS Tagging of Telugu, an Indian Language” published in i- manager Journal on Computer Science, Volume 7, Issue 1, Page No: 25-35, ISSN Print: 2347-2227, March-May 2019 [ Double Blind Peer Reviewed Free Journal with Impact Factor 0.750]. 2. SuneethaEluri, SumalathaLingamgunta “A Statistical Method for Named Entity Recognition in Telugu, an Indian Language” published in International Journal of Recent Technology and Engineering (IJRTE): ISSN: 2277-3878, Volume -8 Issue-2, Page No:4211-4216, July 2019. [Free journal with Scopus Indexing from 2018]. 3. Ralph Grishman, “Natural Language Processing”, Journal of the American Society for Information Science. 35(5): 291-296; 1984 440-445 4. Michael Lesk, "Automatic Sense Disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone", Proceedings of SIGDOC'86. 1986 5. Satanjeev Bannerjee and Ted Pederson, "An Adapted Lesk Algorithm for Word Sense Disambiguation using WordNet," Third International Conference, CICLing 2002 Mexico City, 2002 6. Eneko Agirre and German Rigau, "Word Sense Disambiguation using conceptual density," Proceedings of the 16th conference on Computational linguistics - Volume 1, 1996 7. Daniel Marcu and William Wong, “A Phrase-base Joint Probability Model for Statistical Machine Translation”, Proceedings of EMNLP, 2002. 8. Jyothi et al.,” Parts of speech tagging of marathi text using trigram method”, International Journal of Advanced Information Technology (IJAIT) Vol. 3, No.2, April2013. 9. Pushpak Bhattacharyya, “IndoWordnet” Department of Computer Science and Engineering Indian Institute of Technology Bombay http://www.cfilt.iitb.ac.in/indowordnet/index.jsp 10. Samhith.k, Arun Tilak.S, prof.G.Panda, “ Word Sense Disambiguation using WordNet Lexical Categories ” International conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016. 11. Neeraja Koppula, B. Padmaja Rani and Koppula Srinivas Rao, “Graph-based word sense disambiguation in Telugu language” International Journal of Knowledge-based and Intelligent Engineering Systems 23 (2019) 55–60 55 DOI 10.3233/KES-190399 IOS Press 12. Palanati DurgaPrasad, K. V. N. Sunitha and B. Padmaja Rani, “ Context-Based Word Sense Disambiguation in Telugu Using the Statistical Techniques ”Springer Nature Singapore Pte Ltd. 2018 V. Bhateja et al. (eds.), 13. Pradeep Sachadev, Surabi Verma, Sandeep Kumar Singh, “An Improved Approach to the Word Sense Disambiguation” 978-1-4799- 1812-6/14/$31.00 ©2014 IEEE 14. Pooja Sharma, Nisheeth Joshi “Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet” Vol. 9, No. 2, 2019, 3985-3989 15. Preeti Rana and Parteek Kumar, “Word Sense Disambiguation for Punjabi Language Using Overlap Based Approach” Springer International Publishing Switzerland 2015 El-Sayed M. El-Alfy et al. (eds.), Advances in Intelligent Informatics. 16. Alok Ranjan Pal and Diganta Saha, “Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications” Indian Academy of Sciences 17. S.Parameswarappa and V. N. Narayana, “Target Word Sense Disambiguation System for Kannada language” © 2011 lET. Proc. of Int. Conf on Advances in Recent Technologies in Communication and Computing 2011. Authors: Yannick Kiki, Vinasetan Ratheil Houndji
Paper Title: Prediction of the Purchase Intention of Users on E-Commerce Platforms using Gradient Boosting Abstract: In this paper, we propose a system that is able to forecast the purchase intention of users visiting e- commerce platforms from data collected as they browse on these websites. We use the Online Shoppers Purchasing Intention Dataset available at the University of California Irvine Machine Learning Repository. Thanks to some feature engineering methods, we deeply study the correlation between the various information. We also derive new information / features from the dataset by inference. The most relevant data is fed to gradient boosting, artificial neural networks and other algorithms in order to forecast whether or not a user 76. intends to make a purchase. We evaluate the performances with the precision metric and the F1-Score. The experiments show that our gradient boosting model performs better than the state-of-the-art models thanks to the 446-450 new features used. This also confirms that, in addition to being interpretable, some classic machine learning models such as gradient boosting can be very competitive compared to neural networks. This system thus conceived can allow e-commerce platforms to identify users intending to make a purchase. This gives them the possibility of offering personalized solutions to their potential customers in order to better attract them and guarantee their purchase, which will imply increased sales and better customer satisfaction.
Keywords: e-commerce, feature engineering, gradient boosting, machine learning.
References: 1. Carmona CJ, Ramı ́rez-Gallego S, Torres F, Bernal E, del Jesús MJ, Garcıa S (2012) Web usage mining to improve the design of an e- commerce website: OrOliveSur. com. Expert Syst Appl 39(12):11243–11249 2. Rajamma, Rajasree K.; Paswan, Audhesh K.; and Hossain, Muhammad M., "Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience" (2009). Business Faculty Publications. 205. 3. Ding AW, Li S, Chatterjee P (2015) Learning user real-time intent for optimal dynamic web page transformation. Inf Syst Res 26(2):339–359 4. Albert TC, Goes PB, Gupta A (2004) A model for design and management of content and interactivity of customer-centric web sites. MIS Q 28(2):161–182 5. Awad MA, Khalil I (2012) Prediction of user’s web-browsing behavior: application of markov model. IEEE Trans Syst Man Cybern B Cybern 42(4):1131–1142 6. Budnikas G (2015) Computerised recommendations on e-trans- action finalisation by means of machine learning. Stat Transit New Ser 16(2):309–322 7. Fernandes RF, Teixeira CM (2015) Using clickstream data to analyze online purchase intentions. Master’s thesis, University of Porto 8. Sakar, C.O., Polat, S.O., Katircioglu, M. et al. Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput&Applic 31, 6893–6908 (2019). https://doi.org/10.1007/s00521-018- 3523-0 9. UCI Machine Learning Repository. Available :https://archive.ics.uci.edu/. Last accessed: 19/08/2020. Authors: C.Rajeswari, S.Prakasam
Paper Title: An Efficient Functionality Learning Image Compression by Ift Technique Abstract: Image compression is the course towards apportioning an image into different size as adaptable for the customer to proceed , recollecting a definitive goal to change the depiction of a photo into something that is more fundamental and less referencing to survey. Value learning has been done in the zone of image compression. Since, most of the compression cycle just depends upon quality images. In Image compression, the quality of image is more important than other fields.This paper follows the regular Image picture division as a depiction issue and joins handiness getting the hang of recollecting as extreme goal to help the client from picking where to give sharp information. Explicitly part, our proposed structure overviews a surrendered division by building a "dubiousness field" over the image area thinking about cut-off, normal, flawlessness and entry terms. The client can continue managing the rule of the information on the solicitation plane, in this manner current giving extra preparing information where the classifier has the base conviction. Our strategy portrayals against capricious plane confirmation demonstrating a normal DSC [Dice similarity coefficient] change of 19% in the hidden five plane proposals (pack questions).
Keywords: Image compression, Functionality Learning, DSC [Dice similarity coefficient], Ambiguity Field, image view.
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(2005) “compression of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles.” Ultrasound in medicine & biology, vol. 31, no. 9, pp. 1143–58, 18. Tan, C., Yan, Z., Li, K., Metaxas, D., Zhang, S. (2015) “Laplacian shape editing with local patch based force field for interactive compression,” In: Proc. 2nd Int. Workshop on Patch-Based Tech. in Med. Imaging. p.p:95–193 19. Chiang, P., Zheng, J., Hou Mak, K., Magnenat Thalmann, N., Cai, Y. (2012) “Progressive surface reconstruction for heart mapping procedure”, Computer.-Aided Design 44, p.p:289–299 20. Yu, Y., Zheng, S., Li, K., Metaxas, D., Axel, L.: (2014) “Deformable models with sparsity constraints for cardiac motion analysis”, Med. Image Anal. 18, p.p:927–937 21. Kronman, A., Joskowicz, L. (2013): “Image compression errors correction by mesh compression and deformation” In: Proc. Med. Image Compute and Computer-Assist. Interven. (MICCAI-2013) p.p:218–213. 22. 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J. Hansegard, F. Orderud, and S. I. Rabben, (2018) “Real-time active shape models for compression of Image cardiac ultrasound,” in Computer Analysis of Images and Patterns, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, vol. 4673, pp. 157– 164. 27. K. Y. E. Leung et al. (2008) “Improving Image active appearance model compression of the left ventricle with jacobian tuning,” in Proc. SPIE 6914, Medical Imaging 2008: Image Processing, vol. 6914, 28. B. Georgescu, X. Zhou, D. Comaniciu, and A. Gupta, (2005) “Database-guided compression of anatomical structures with complex appearance,” in Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on, vol. 2, June 2005, pp. 429– 436. 29. Mahdi Hajiaghayi, Elliott M. Groves., M.Eng., Hamid Jafarkhani, (2016) “A Image Active Contour Method for Automated compression of the Left Ventricle from Magnetic Resonance Images” IEEE Transactions on Biomedical Engineering.p.p:1-2. 30. 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Paper Title: A Low Cost & Efficient Robotic Spraying Machine Abstract: It is well known fact that India is an agricultural country.It is considered as the lifeline of our Country since it provides the most employment and profit to the nation. But the current state of agriculture in India is not is not upto the mark. The farmers and other agriculture related people are suffering a lot. Considering the problems which are faced by the farmers in the agriculture industry which includes physical strains on the body we as engineers need to design a solution to this. We have seen that due to pesticide spraying the farmers face lot of physical as well as respiratory problems. Our solution to this problem includes designing a machine which helps to reduce manual work of farmers and save them from respiratory problems. The machine is a cost effective solution to help the farmers and can be used even by small scale farmers. Thus, this robotic spraying machine will be of great use. There are various pesticide sprayers available in the market. These spraying techniques are very tediousand time consuming. Purpose is to make an affordable pesticide spraying machine to boost the income of farmers the productivity of crops must be increased. Hence the design tries to improve the pesticide spraying techniques. Through this paper, a real life solution to link agriculture with technology in order to reduce human efforts & boost up the total output is suggested. This paper aims at 78. explaining the working ofa low cost & efficient robotic spraying machine that would be operated by the farmer through remote so that thefarmer is not in direct contact with harmful pesticides. On the command from the remote the spraying action will be controlled. The pesticide sprayer would operate with minimal pollution. The 456-459 model includes 2 nozzles for spraying purpose along with a DC pump which will help inthe total flow of liquid so that constant spraying will occur.
Keywords: Atmega 8 & 16 microcontroller, DC PumpNozzle, DC Pump, Remote
References: 1. Shailesh Malonde, Shubham Kathwate, Pratik Kolhe, Nishant ingole, Rupesh Khorgade “Design and development of Multipurpose Pesticide Spraying Machine”, International journal of Advance Engineering and Global Technology”, ISSN No:2309-4893 volume-04, Issue-03,(May-2016). pp:1945-1953 2. RajashekhargoudAngadi, Rohit L G, Satish Changond, Santosh Kagale “Cam Operated Agrochemical Pesticide Sprayer”, International journal of Engineering Research & Technology”, ISSN No:2278-0181 volume-06, Issue-01,January-2017, pp: 233-236 3. S R Kulkarni, R V Nyamagoud, Hareesh Naik, Mohan Futane “Fabrication of Portable Foot Operated Agricultural Fertilizers and Pesticides Spraying Pump”, International journal of Engineering Research &Technology”,ISSNNo:2278-0181 volume-04, Issue- 07,July-2015, pp:63-69 4. R. D. Fox, R. C. Derksen, “Visual and image system measurement of spray deposits using water–sensitive paper”, Applied Engineering in Agriculture Vol. 19(5), pp: 549–552, 2003 American Society of Agricultural Engineers ISSN 0883–8542 5. https://www.elprocus.com/atmega8-microcontroller Authors: Kehdinga George Fomunyam
79. Paper Title: Redefining the theory of Engineering for Relevance in the 21st Centuryin Africa Abstract: Academia and professionals’ attention has been drawn towards redefining theory of engineering for 460-467 relevance in the 21st century. This has become an imperative as it has brought changes in engineering courses, and yet engineering curriculum have not been modified to accommodate these changes. With increased intellectual demand for ground-breaking engineering performance in Africa, African engineering institutions are still lagging behind as they are yet to meet up with the 21st century needs; hence the crux of this paper. This paper was guided by Jean Piaget’s constructivism learning theory, focusing on individual’s understanding and knowledge, rooted on one’s experience erstwhile to learning setting. This paper takes a broad look at the overall investigation of redefining the theory of engineering for relevance in 21st century in Africa. The specific objectives explore the principles of theory of engineering as well as its applicability and to examine how theory of engineering can be improved for contextual relevance, as well as its implications for 21st centuryengineering curriculum. Thus, to address this gaps, recommendations on redefinition and relevance of theory of engineering pertaining to curriculum revision and providing adequate staff development for engineering educators with intellectual capacityand skill improvement were recommended.
Keywords: Africa, Engineering education, curriculum, redefining, theory of engineering
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Paper Title: MIMO Based Cognitive Radio Network with Efficient Spectrum Utilization Abstract: This paper presents an energy-efficient power control scheme for Multi-Input–Multi-Output (MIMO) based multi-channel cognitive radio networks (CRNs) with adaptive mode selection protocol. Cognitive radio device operates in the temporal domain to utilize spectrum holes and MIMO system operates in the spatial domain. Thus CRN in collaboration with MIMO can produce a significant improvisation for spectrum utilization, and therefore, the integration of MIMO and CRN has received widespread research interest. To address the challenges of the collaboration of MIMO and CRN, we propose a MIMO based CRN network in this paper, in which basic CRN is modified with the QPSK transmission technique having four antenna elements per primary users (PU) and secondary users (SU). Similarly, MIMO significantly improves channel capacity particularly in SU transmission under low SNR condition, but issues like several antenna elements and specific antenna selection issues increase the computational complexity and energy consumption. The proposed network protocol strives to improve communication stability and rate of throughput via selecting the MIMO operating parameters such as multiplexing, diversity, or hybrid multiplexing-diversity gains. Specifically, we designed transmission power distribution along with an antenna selection scheme for enhancing the energy efficiency of CRN’s under the influence of the maximum interference caused by the SU to PU, utmost power transmission from SU, and the least rate of transmission through the SU link. Finally, the simulation results are compared with basic CRN alone using the BPSK scheme to evaluate and validate the proposed scheme.
Keywords: Multi-input–multi-output system, cognitive radio networks, primary users, secondary users and 80. transmission power allocation.
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