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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 Chairman: Lattice Science, Bhopal (M), India CEO: Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India Specialization: Sound Processing, Image Processing and Recognition, Compression and Decompression

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Dr. Ali OTHMAN Al Janaby Ph.D. (LTE), MSc. (ECE), BSc (EE) Lecturer, Department of Communications Engineering, College of Electronics Engineering University of Ninevah, Iraq. Specialization: LTE-Advanced, IoT, 5G

Dr. Anil Singh Yadav Ph.D(ME), ME(ME), BE(ME) Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India. Specialization: Thermal Power and Renewable Energy, Mechanical Engineering

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Dr. Hao Yi Ph.D. (ME), MS (ME), BS (ME) Member of ASME, ACM, CMES, CSAA. Assistant Professor, Department of Mechanical Engineering, Chongqing University, China. Specialization: 3D Printing and Additive Manufacturing, Droplet Printing, Selective Laser Melting (SLM), Spray Forming, Coatings

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. Dr. Ramani Kannan Ph.D.(Power Electronics), M.E(Power Electronics), B.E(Electronics & Communication) Member of IEEE Power Electronics Society, Malaysia. Senior Lecturer, Department of Electrical and Electronics Engineering, Center for Smart Grid Energy Research, Institute of Autonomous system. Universiti Teknologi PETRONAS (UTP), Malaysia. Specialization: Electrical and Electronics Engineering

Dr. Sabyasachi Pramanik PhD. (CSE), M.Tech.(CSE), B.Tech. (ETE) Assistant Professor, Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia (West Bengal), India. Specialization: Data Hiding, Image Steganography, Machine Learning

Dr. M.L. Pavan Kishore PhD.(ME), M.Tech.(ME), B.Tech.(ME) Senior Assistant Professor, Department of Mechanical Engineering, The ICFAI Foundation for Higher Education, Hyderabad (Telangana), India. Specialization: Composites, Finite Element Analysis, Computational Fluid Dynamics

Dr. H S Prasantha PhD.(ECE), M.Tech. (EEE), B.Tech. (ECE) Senior Member of IEEE Professor, Department of Electronics & Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore (Karnataka), India. Specialization: Image & Video Processing, Biomedical Signal Processing, Signal Processing of Remote Sensed Data

Dr. Mohd Umar Farooq Ph.D.(CSE), M.Tech.(CSE), BE(CSE) Associate Professor, Department of Computer Science Engineering, Muffakham Jah College of Engineering and Technology Osmania University, Hyderabad (Telangana), India. Specialization: VANETs, IoT, ML & AI

Dr. Anil V. Turukmane PhD.(CSE), ME.(CSE), BE.(IT) Associate Professor & HOD, Department of Computer Science and Engineering, Sharad Institute of Technology College of Engineering, Kolhapur (Maharashtra), India. Specialization: Cyber Security, Network Security

Dr. Rajesh Kumar Pathak Ph.D.(CSE), M.Tech. (CSE), BE.(IT) Vice Chancellor, Department of Computer Science and Engineering, Shri Rawatpura Sarkar University, Raipur (Chhattisgarh), India. E-mail: [email protected] Links: Google Scholar Specialization: Data Mining, Cloud Computing, Data Science, IoT

Prof. Md. Abdul Raheem M.Tech (ME), B.E.(ME) Assistant Professor, Department of Mechanical Engineering, Muffakham Jah College of Engineering and Technology, Hyderabad (Telangana), India. Specialization: Mechanical Engineering, Thermal Engineering, Medical Device Simulation

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. Dr. K. Prathyusha Ph.D.(ECE), M.Tech.(ECE), B.Tech.(ECE) Member of PubMed Associate Professor, Department of Electronics & Communication Engineering, NRI Institute of Technology, Vijayawada (A.P), India. Specialization: Embedded Systems, Internet of Things, Industrial Internet of Things, Machine Learning, Deep Learning, Artificial Intelligence

Dr. T. Pridhar PhD.(Mechanical), M.E.(CAD), B.E.(Mechanical) Member of Elsevier Associate Professor, Department of Mechanical Engineering, Sri Krishna College of Technology, Kovaipudur (Tamil Nadu), India. Specialization: Metal Matrix Composites, Wear, Polymer Composites

Dr. Umesha PK Ph.D.(Civil Engineering), M.Tech.(Structural Engineering), B.E.(Civil Engineering) Member of Elsevier Professor, Department of Civil Engineering, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India. Specialization: Structural Engineering

Author (s) Name Title of the Article Page No

Deep Neural Network to Predict Diabetes: A Data Science Mafas Raheem 1-2 Approach

Retikal Anil Kumar Complicity of High-End SOC-FPGA’s for Data Centers 2-2 Runi Asmaranto, Dian Sisinggih, Fluctuation Effect of Reservoir Water Level on the Seepage 3-3 Ridho Nur Aziz Rastanto of Earth-Fill Dam Advanced Lecture for PID Controller of Nonlinear System in Dong Hwa Kim 4-5 Python Pooja Choudhary, Kanwal Garg Tensor Data Imputation by PARAFAC with Updated Chaotic 5-7 Biases by Adam Optimizer V. Yasaswini, Santhi Baskaran An Optimization of Feature Selection for Classification using 7-7 Bat Algorithm

Farisa T S, Elizabeth Isaac Sequence Based DNA-Binding Protein Prediction 8-8

Discovery of New Theory Analysis of Equilibrium Point Matius Irsan Kasau, ST. Aminah Population Versus Food on Theory of Thomas Robert 8-9 Dinayati Ghani Malthus and Its Development (Case Study of Indonesia) Vini.K, H.Padma Kumar, The Role of Bismuth on the Best Red Light Emitting 9-10 K.M.Nissamudeen Nanophosphors Luminescence Study of Red Light Emitting Y2O3:Sm3+ Vini.K, H.Padmakumar, Nanophosphors and Enhancement by Co-doping with 10-11 K.M.Nissamudeen Gadolinium oxide Design of IoT based Real-Time Bus Tracking App using HF- Anjali Jain, Agya Mishra 11-12 RFID An Enhanced Framework To Secure Big Data Based On Salim Raza Qureshi 12-13 Hybrid Machine Learning Technique: ANN-PSO G. Sudha Sadasivam Crop Yield Prediction using Granular SVM 14-15 Soumi Ghosh, Devanshu Tyagi, A Comprehensive Survey of Personalized Music Identifier Daksh Vashisht, Abhishek Yadav, 16-18 System Dharmendra Rajput Ushveen Kaur, Sugandha Gupta Evaluating Quality - Measures to Improve NAAC Ranking for 19-20 Higher Education Institutes The Impacts of Orientation and Building form on Internal Ali N Alzaed Temperature of Visitor Center Building for Moderate and Hot 20-21 Climate Sadashiva M, M. Yunus Sheikh, Nouman Khan, Ramesh Kurbet, A Review on Application of Shape Memory Alloys 21-25 T.M.Deve Gowda Effect of Well-being on People Surrounding the Airport Jotirmay Chari, B Shankar Corridor using Predictive Analysis on Road Accident 25-25 Correlation Bhishma Karki, Jeevan Jyoti Feasibility of Nitrate Removal using Hydroxylamine Nakarmi, Saddam Husain Dhobi Hydrochloride from Sundarijal River Water through a 26-27 Laboratory Scale Vijit Srivastava, Ashish Khare Identification of Power Quality Disturbance 27-28

Zain Ali, Bharat Lal Harijan, Digital FIR Filter Design by PSO and its variants Attractive Tayab Din Memon, Nazmus Nafi, and Repulsive PSO(ARPSO) & Craziness based 28-28 Ubed-u-Rahman Memon PSO(CRPSO) Implementing Hybrid Security Mechanism for Cloud Manju Sharma, Mukesh Kumar Considering Intrusion, Sql Injection and Performance 29-30 Sharma Degradation

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. Automatic Compensation of the Positional Error Utilizing Hirofumi Maeda 30-31 Localization Method in Pipe Effect of Aging and Deformation Treatments on Mechanical Víctor Alcántara Alza 31-32 Properties of Aluminum AA-6063 Benjamin Kwakye, Chan Tze Haw Theoretical Overview of Sentiment Analysis in the Real 32-34 Estate Market Hershey R. Alburo, Cherry Lyn C. Sentiment Analysis of the Academic Services of ESSU Sta. Romana, Larmie S. Feliscuzo Salcedo Campus using Plutchik Model And Latent Dirichlet 35-36 Allocation Algorithm Hatem Sadek, Mohammad H. Implementation of Low-Pressure Water Mist System for Fire Alenezi, Mostafa A. Ismail 37-37 Suppression inside a Model of Road Tunnel

R.Jeena, G.Dhanalakshmi, S.Irin A Novel Approach for Healthcare Information System using Sherly, S.Ashwini, R.Vidhya 37-38 Cloud

B.Devaneshwar, K.B.Amarthian, Calibrating Best Route Based on Battery Percentage and M.Yuvanthika Meenakshi, 38-38 Availability of Charging Station V.M.Saradha B. Nadimulla, S. Aruna Mastani Adjustable PRPG for Low Power Test Patterns 38-39

Disease Classification and Prediction using Ensemble B.Meena Preethi, P.Radha 39-40 Machine Learning Classification Algorithm S.Reginold Jebitta, Durga Devi P R, Deva Dharshini L, Theerdham A Comprehensive Review on Protein Isolates from Legumes 40-42 Naga Sai Harika, Vignesh K Fadare Oluwaseun Gbenga, Towards Optimization of Malware Detection using Extra-Tree Adetunmbi Adebayo Olusola, and Random Forest Feature Selections on Ensemble 42-43 Oyinloye Oghenerukevwe Elohor Classifiers

Plant Leaf Disease Detection and Classification using Prabavathi S, Kanmani P 44-45 Optimized CNN Model Nazia Tazeen, K.Sandhya Rani A Survey on Some Big Data Applications Tools and 45-45 Technologies Mahassine Bekkari, Abdellah El Modelling and Analyzing the Employees’ Engagement in 46-47 Fallahi Workplace using Machine learning Tools Sanniv Shome, Shushil Mhaske, Mine Waste as Resource: Indian Mining Scenario of Coal and 47-48 K. Pathak, M S Tiwari Non Coal Mining Sector Smart Internet of Things Based Induction Motor Parameter M. Ambika ME, M. Madhunisha 48-49 Monitoring and Control System Dielectric Cover Layer Thickness Effect on Circular V. Saidulu 49-49 Microstrip Antenna Parameters

© All rights are reserved. For more details, please read ‘copyright Grants and Ownership Declaration’ from the journal website. International Journal of Recent Technology and Engineering (IJRTE) Volume-9 Issue-6, March 2021, ISSN: 2277-3878 (Online) Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

Author(s): Mafas Raheem Title of the Article: Deep Neural Network to Predict Diabetes: A Data Science Approach

Abstract: Diabetes has become a famous and lethal disease among the low and medium-income countries. People could not overcome this deadly abnormal condition due to the current lifestyle, food habit and the genetic transmittance. Medical practitioners provide advice to prevent the diabetic condition and medications to control as this disease does not have a permanent cure. However, the detection of the disease is being a tidy process and deployment of machine learning predictive models to conduct smart diagnosis/detection is vital in the healthcare domain nowadays. Though several machine learning models were built in this regard, deploying a Deep Neural Network seems less focused. Therefore, a Deep Neural Network model was built with the support of complete preprocessing, class balancing, normalization, feature selection process and hyper-parameter tuning using the cross-validated searching technique. The model achieved 88% of accuracy and 0.88 ROC score and standing out as a promising predictive model in diagnosing/detecting diabetes

Keywords: Diabetes, Healthcare, Predictive Modelling, Deep Neural Network, Optimization.

References: 1. K. Kaul, M. T. Joanna, I. A. Shamim, M. K. Eva, and C. Rakesh, “Introduction to Diabetes Mellitus. Diabetes: An Old Disease, a New Insight,” Springer New York CY - New York, 2012. 2. WHO. (2021). Diabetes [Online]. Available: https://www.who.int/health-topics/diabetes#tab=tab_1 [Accessed: 2 January 2021]. 3. I. Yoo, P. Alafaireet, M. Marinov, K. Pena-Hernandez, R. Gopidi, J. F. Chang, and L. Hua, “Data mining in healthcare and biomedicine: A survey of the literature,” Journal of Medical Systems, 36(4), Aug 2012, pp. 2431-48. 4. J. Tyrer, S. W. Duffy, J. Cuzick, “A breast cancer prediction model incorporating familial and personal risk factors,” Stat Med, 15 Apr 2004, 23(7), pp. 1111-1130. 5. Er. Orhan, N. Yumuşak and F. Temurtas, “Chest diseases diagnosis using artificial neural networks,” Expert Systems with Applications, 37, 2010, pp. 7648-7655. 6. C. Chang, and C. Chen, “Applying decision tree and neural network to increase quality of dermatologic diagnosis,” Expert Systems with Applications, 2009, 36, pp. 4035-4041. 7. S, Moon, S, Kang, W. Jitpitaklert, and S. B. Kim, “Decision tree models for characterizing smoking patterns of older adults,” Expert Systems with Applications, 2012, 39, pp. 445-451. 8. H. S. Ruchlin, “An Analysis of Smoking Patterns among Older Adults,” Med Care, 1999, 37, pp. 615 – 619. 9. J. Muñoz-Pérez, C. Martínez, and N. García-Pedrajas, “COVNET: A cooperative coevolutionary model for evolving artificial neural networks,” Ieee Transactions on Neural Networks, 2003, 14(3), pp. 575-596. 10. H. Kahramanli, and N. Allahverdi, “Design of a hybrid system for the diabetes and heart diseases,” Expert Systems with Applications, 2008, 35, pp. 82-89. 11. K. Kayaer, and T. Yildirim, “Medical diagnosis on Pima Indian diabetes using general regression neural networks,” Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing, 2003. 12. R. Ahuja, S. C. Sharma, and M. Ali, “A Diabetic Disease Prediction Model Based on Classification Algorithms,” Annals of Emerging Technologies in Computing (AETiC), 2019, 3(3), pp. 44-52. 13. K. Harleen, and V, Kumari, “Predictive modelling and analytics for diabetes using a machine learning approach,” Saudi Computer Science, 2018, 12, pp. 1-6. 14. S. Bashir, U. Qamar, and F. H. Khan, “IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework,” Journal of Biomedical Informatics, 59, 2016, pp. 185-200. 15. D. Bani-Hani, P. Patel, and T. Alshaikh,(2019). An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes. Global Journal of Computer Science and Technology. 2019, 19(2). pp. 1-11. 16. R. Sushant, H. Balaji, N. Ch. S. N, Iyengar and D. C. Ronnie, “Optimal Predictive analytics of Pima Diabetics using Deep Learning,” International Journal of Database Theory and Application, 2017, 10(9), pp. 47-62. 17. H. Guang-Bin, Z. Qin-Yu and S. Chee, “Extreme learning machine: A new learning scheme of feedforward neural networks,” IEEE International Conference on Neural Networks - Conference Proceedings, 2004, 2. pp. 985-990. 18. H. Temurtas, N. Yumusak and F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks,” Expert Systems with Applications, 2009, 36, pp. 8610-8615. 19. M. Nilashi, O. Ibrahim, M. Dalvi, H. Ahmadi, and L. Shahmoradi, “Accuracy Improvement for Diabetes Disease Classification: A case on a Public Medical Dataset,” Fuzzy Information and Engineering, 2017, 9(8), pp. 245-257.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 1

International Journal of Recent Technology and Engineering (IJRTE) Volume-9 Issue-6, March 2021, ISSN: 2277-3878 (Online) Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

20. KDnuggets. 2021. Deep Neural Networks. [Online] Available at: https://www.kdnuggets.com/2020/02/deep-neural- networks.html. 21. S. Dalwinder, S. Birmohan, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing, 2020, 97.

Author(s): Retikal Anil Kumar

Title of the Article: Complicity of High-End SOC-FPGA’s for Data Centers

Abstract: As the network traffic increasing significantly due to increase in Data streaming, Big Data Analytics, Cloud Computing, Increasing the load on Data Centers, Which leads to demand for high computational capabilities, low latency, high-bandwidth, power efficient data accelerators. As Re-Configurability of FPGA’s are more flexible for developing customized applications, so the FPGA hardware based data accelerators are the potential devices to achieve low latency and power efficient requirements. The modern FPGA’s are coming up with the embedded communication hard IP’s like PCIe, Ethernet, & DDR based memory controllers, which makes easy for the deployment of network attached FPGA’s in data centers. This paper presents the role of FPGA’s in datacenters and analysis of high-end FPGA’s by various vendors, which are suitable for deployment in data centers.

Keywords: ASIC, Data Center, FPGA, Re-Configurable Logic, EFPGA, Big Data Analytics, Cloud Computing.

References: 1. “Versal Architecture and Product Data Sheet: Overview”, DS950 (v1.6) May11,2020,Available: https://www.xilinx.com/support/documentation/data_sheets/ds950-versal-overview.pdf 2. https://en.wikipedia.org/wiki/GDDR_SDRAM 3. F. Abel, J. Weerasinghe, C. Hagleitner, B. Weiss and S. Paredes, "An FPGA Platform for Hyperscalers", 2017 IEEE 25th Annual Symposium on High-Performance Interconnects (HOTI), Santa Clara, CA, 2017, pp. 29-32. doi: 10.1109/HOTI.2017.13. 4. J. Weerasinghe, R. Polig, F. Abel and C. Hagleitner, "Network-attached FPGAs for data center applications”, 2016 International Conference on Field-Programmable Technology (FPT), Xian, 2016, pp. 36-43. doi: 10.1109/FPT.2016.7929186 5. “Intel® Agilex™ F-Series FPGA and soc fpga Product Table” https://www.intel.in/content/dam/www/programmable/us/en/pdfs/literature/pt/intel-agilex-f-series-product- table.pdf 6. “Intel® Agilex™ I-Series SoC FPGA Product Table”, Available: https://www.intel.in/content/dam/www/programmable/us/en/pdfs/literature/pt/intel-agilex-i-series-product-table.pdf 7. J. Weerasinghe, F. Abel, C. Hagleitner and A. Herkersdorf, "Disaggregated FPGAs: Network Performance Comparison against Bare-Metal Servers, Virtual Machines and Linux Containers", 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Luxembourg City, 2016, pp. 9-17. doi: 10.1109/CloudCom.2016.0018. 8. “Speedster7t FPGA Datasheet (DS015)”, Available: https://www.achronix.com/product/speedster7t-fpgas 9. Microchip PolarFire® SoC FPGAs Architecture, Applications, Security Features, Design Environment, Design Hardware. Available: https://www.microsemi.com/product-directory/soc-fpgas/5498-polarfire-soc- fpga#documentation 10. https://www.microsemi.com/product-directory/soc-fpgas/1692-smartfusion2#design-resources

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 2

Author(s): F Runi Asmaranto, Dian Sisinggih, Ridho Nur Aziz Rastanto

Title of the Article: Fluctuation Effect of Reservoir Water Level on the Seepage of Earth-Fill Dam

Abstract: Lots of dam failures are the result of uncontrolled seepage. The collapse of the Situ Gintung Dam in Tangerang, Banten-Indonesia in 2009 due to heavy rains caused the dam structure to collapse. This is due to increased pore water pressure in the landfill. To anticipate collapse due to uncontrolled seepage, it is necessary to monitor it based on the behavior of changes in rainfall and reservoir water levels. Seepage within the dam body is often monitored using instrumentation tools such as standpipe piezometer (standpipe piezometer) or electric piezometer. But often the piezometer cannot work properly because it is clogged, so it cannot monitor the condition of the seepage. Other instrumentations such as V-Notch are also used to measure seepage discharge. This study aims to determine the behavior of changes in the reservoir water level caused by changes in rainfall and its effect on body seepage of the earth-fill Type dam. By knowing the phenomenon of the behavior of the relationship between reservoir water infiltration and rainfall, it will obtain information on rainfall that endangers the dam which will affect the downstream. In this study, a case study of the Selorejo Dam was taken which has a large enough reservoir capacity of about 31 million m3 which is included in the Brantas River Basin. The results showed that 5 piezometers devices were damaged (SL 1, SL 2, SL 4, SL 6, and SL 7) where they could not read the phreatic water level properly, and 2 piezometers were less sensitive to reading fluctuations in reservoir water levels. namely SL 10 and SL 11 which showed R2 values of 29.78% and 39.4%, respectively. While the maximum seepage discharge is recorded at 1474 liters/minute, this is still below the critical discharge of 1630 liters/minute allowed for this dam, but this needs to be a concern, especially the discharge from toe drain from the left side seepage and C-area which is the leakage from the left support pedestal also contributes a larger discharge than other observation points.

Keywords: Ngancar Reservoir, Erosion And Sedimentation.

References: 1. Gui S, Zhang R, Turner JP, Xue X. Probabilistic slope stability analysis with stochastic soil hydraulic conductivity. J Geotech Geoenviron Eng 2000;126 (1):1–9. 2. Liu, Lei-Lei, et al. "Effects of spatial autocorrelation structure of permeability on seepage through an embankment on a soil foundation." Computers and Geotechnics 87 (2017): 62-75. 3. Asmaranto, r., Suryono, A., & Hidayat, M.2019. Inspections of Hydro-Geotechnical on Ngancar Dam. Civil and Environmental Science Journal, 2(2), pp.117-127. doi:https://doi.org/10.21776/ub.civense.2019.00202.5 4. Guo, X., et al. "An analytical model for the monitoring of pore water pressure inside embankment dams." Engineering Structures 160 (2018): 356-365. 5. Al Islami, Auliya Nusyura, et al. "Analisis Stabilitas Bendungan Selorejo Akibat Rapid Drawdown Berdasarkan Hasil Survey Electrical Resistivity Tomography (Ert)." Jurnal Mahasiswa Jurusan Teknik Sipil Universitas Brawijaya, vol. 1, no. 3, 2014. 6. USBR (1987) Design of Small Dams. A Water Resources Technical Publication. United States Department of The Interior, BUREAU OF RECLAMATION. Third Edition 7. Kementerian Pekerjaan Umum dan Perumahan Rakyat. (2017). Modul Instrumentasi Bendungan Urugan Pelatihan Perencanaan Bendungan Tingkat Dasar. Bandung: Kementerian Pekerjaan Umum dan Perumahan Rakyat 8. Sosrodarsono, Suyono. (1989). Bendungan Type Urugan. Jakarta: PT Pradnya Paramita. 9. Perusahaan Umum Jasa Tirta I (2019). Operation and Maintenance Manual for the Selorejo Dam. Malang: PJT I 10. Duncan, W., et al. Design of small dams . A water resources technical publication. Final report. No. PB-95- 176368/XAB. Bureau of Reclamation, Denver, CO (United States). Engineering and Research Center, 1987.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 3

Author(s): Dong Hwa Kim

Title of the Article: Advanced Lecture for PID Controller of Nonlinear System in Python

Abstract: PID controller is very well known in engineering areas and it has a long history. So, there are many materials such as control knowhow for application, research paper, tuning method proven through a long history. It is an important to have an advanced lecture for design and tuning as much as development. However, it is very difficult to find for teaching knowhow. Current teaching style is implementation by MATLAB. However, MATLAB S/W is quite expensive as commercial based business focusing S/W. Advanced country or rich institute can provide site license. However, it is impossible for under developing country or small institute that cannot ready because of price. So, we must find alternative S/W to teach and research for implementation. Currently, many are interested in Python because it is open source and huge communities. This paper provides teaching experience of PID controller to nonlinear system to share knowhow and develop teaching method for teacher and students, effectively.

Keywords: About PID Tuning, Python, Nonlinear Control, Control Lecture.

References: 1. https://www.educba.com/python-vs-matlab/ 2. https://www.educba.com/software-development/courses/?source=footer 3. Software Development Course - All in One Bundle (https://www.educba.com/software- development/courses/software-development-course/?source=footer) 4. Become a Python Developer (https://www.educba.com/software-development/courses/python-certi 5. Ziegler and Nichols, “Optimum setting for automatic controllers,” Transaction ASME, Nov. pp. 759-768, 1942. 6. C. C. Hang and K. J. Astrom, “Refinements of the Ziegler-Nichols tuning formular, “Proc. Inst. Elect. Eng., Vol. 138, pt. D, pp. 111-118, 1991. 7. Download Anaconda. [Online]. Available: http://continuum.io/downloads 8. Obtaining NumPy and SciPy libraries. [Online]. Available: http://www.scipy.org/scipylib/download.html 9. Python Control toolbox. [Online]. Available: https://github.com/python-control/python- control 10. NumPy for Matlab Users. [Online]. Available: http://wiki.scipy.org/NumPy for Matlab Users 11. Dong Hwa Kim, “Experimental Research of Intelligent Multivariable 2-DOF PID Control System for DCS,” International Journal of Systems Applications, Engineering & Development, July 2013, pp. 148-157. 12. Dong Hwa Kim, “Getachew Teshome**, Dawit Dubela**, Yosef Dentamo**, Hinsermu Alemayehu, “Optimal Conversion of DC-DC Converter Considered Optimal Switching Time and Optimal Switching Mode of PWM by Fuzzy Based PID Tuning’, IJITEE, pp. 1-6, March 2020. 13. Dong Hwa Kim, “A Study on Improving Lecture Skill and Implementation of Anti-reset and Bampless Using 2- DOF-PID Controller and Python IARJSET, Vol. 7, Issue 10, October 2020, 2394-1588. 14. Dong Hwa Kim, Hinsermu, “A study on Teaching Method of Control Engineering by Using Python Based PID”, IARJSET, Vol. 7, Issue 9, September 2020, 2394-1588. 15. Dong Hwa Kim, “Advanced Lecture Skill of Fuzzy Control in Python”, IARJSET, Vol. 7, Issue 10, October 2020, 2394-1588. 16. C. H. Lee and C. C. Teng, “A Novel Robust PID Controllers Design by Fuzzy Neural Network,” Asian Journal of Control, Vol. 4, No. 4, pp. 433-438, 2002. 17. [J. X. Xu, Y. M. Pok, C. Liu, and C. C. Hang, “Tuning and Analysis of a Fuzzy PI Controller Based on Gain and Phase Margins,” IEEE Trans. on Systems, Man, and Cybernetics- Part A: Systems and Humans, Vol. 28, No. 5, pp. 685-691, 1998. 18. Kraus, T.W., & Mayron, T.J, “Self-tuning PID controllers based on a pattern recognition approach,” Control Engineering Practice, 106–111, 1984. 19. Zhuang, M. and D. P. Atherton, “Automatic tuning of optimum PID controllers,” IEE Proc. Part D, 14, 216-224, 1993. 20. C. H. Lee and C. C. Teng, “Tuning PID Controller of Unstable Processes: A Fuzzy Neural Network Approach,” Fuzzy Sets and Systems, Vol. 128, No.1, pp. 95-106, 2002. 21. Dong Hwa Kim, “Tuning of a PID controller using an artificial immune network model and fuzzy set” IFSA, July 28, Vancouver, 1998. 22. S. Matsummura, Adaptive control for the steam temperature of thermal power plants,” Proceedings the 1993 IEEE on Control applications,” PP. 1105 - 1109, Sept. 1998. 23. Teng Fong-Chwee, “Self-tuning PID controllers dor dead time process,” IEEE Trans., Vol. 35, No. 1, pp. 119-125, 1988.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 4

24. Ya-Gang Wang, “PI tuning for processes with dead time,” AACC2000, Chicago, Illinois, June 2000. 25. W. K. Ho, “PID tuning for unstable process based on gain and phase-margin specifications,” IEE Proc. Control Theory Appl. vol. 45, no. 5, pp. 392-396, Sept. 1998. 26. Nichols, "Instrumentation for process flow engineering," Technonis publishing company,' 1987. 27. Dong Hwa Kim. “Tuning of 2 - DOF PID controller by immune algorithm," IEEE international conference on evolutionary computation, Hawaii, May 12 - 17, 2002. 28. Dong Hwa Kim, “Auto-tuning of reference model based PID controller using immune algorithm,” IEEE international conference on evolutionary computation, Hawaii, May 12 - 17, 2002. 29. Dong Hwa Kim, “Comparison of PID Controller Tuning of Power Plant Using Immune and genetic algorithm. Measurements and Applications,” Ligano, Switzerland, 29-31 July 2003. 30. Dong Hwa Kim, “Robust PID controller tuning multiobjective optimization based on clonal selection of immune algorithm,” Proc. Int. Conf. Knowledge-based intelligent information and engineering systems. Springer-Verlag. pp. 50-56, 2004. 31. Zwe-Lee Gaing, "A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System", IEEE Trans. Energy Con. Vol. 19, pp. 384-391, No. 2, June 2004. 32. MATLAB TOOL BOX Manual. 33. B. Stuart, “Development of PID controller,” IEEE control systems, vol. pp. 58-62, Dec.1993. 34. Y. Stephen, “A laboratory course on fuzzy control,” IEEE Trans. on Education, vol. 42, no. 1, pp. 15-21, May 1998. 35. 35.https://en.wikipedia.org/wiki/Ziegler%E2%80%93Nichols_method

Author(s): Pooja Choudhary, Kanwal Garg

Title of the Article: Tensor Data Imputation by PARAFAC with Updated Chaotic Biases by Adam Optimizer

Abstract: The big data pattern analysis suffers from incorrect responses due to missing data entries in the real world. Data collected for digital movie platforms like Netflix and intelligent transportation systems is Spatio-temporal data. Extracting the latent and explicit features from this data is a challenge. We present the high dimensional data imputation problem as a higher-order tensor decomposition. The regularized and biased PARAFAC decomposition is proposed to generate the missing data entries. The biases are created and updated by a chaotic exponential factor in Adam's optimization, which reduces the imputation error. This chaotic perturbed exponentially update in the learning rate replaces the fixed learning rate in the bias update by Adam optimization. The idea has experimented with Netflix and traffic datasets from Guangzhou, China.

Keywords: Tensor decomposition, PARAFAC, Adam optimization, Data imputation, etc.

References: 1. Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei, “Sequential prediction of social media popularity with deep temporal context networks”, Proceedings of the 26th International Joint Conference on Artificial Intelligence Melbourne, Australia,2017, pp 3062-3068. 2. S. De, A. Maity, V. Goel, S. Shitole and A. Bhattacharya, “Predicting the popularity of instagram posts for a lifestyle magazine using deep learning, “2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), Mumbai, 2017, pp. 174-177. 3. Sufal Das, Brandon Victor Syiem, Hemanta Kumar Kalita, “Popularity Analysis on Social Network: A Big Data Analysis”, International Conference on Computing, Communication and Sensor Network, 2014, pp 27-31. 4. Stefan Stieglitz, MiladMirbabaie, Björn Ross, Christoph Neuberger, “Social media analytics – Challenges in topic discovery, data collection, and data preparation”, International Journal of Information Management, Volume 39, 2018, pp 156-168. 5. MarouaneBirjali, AbderrahimBeni-Hssane, Mohammed Erritali, “Analyzing Social Media through Big Data using InfoSphereBigInsights and Apache Flume”, Procedia Computer Science, Volume 113, 2017, pp 280-285. 6. Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, Yong Jae Lee, “Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks”, 2017, arXiv:1705.09275v4 [cs.CV]. 7. Kota Yamaguchi, Tamara L Berg, Luis E Ortiz, “Chic or Social: Visual Popularity Analysis in Online Fashion Networks”, ACM Multimedia 2014, pp 773-776. 8. Benjamin Shulman, Amit Sharma, Dan Cosley, “Predictability of Popularity: Gaps between Prediction and Understanding”, Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016), pp 348-357.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 5

9. Van Canneyt, Steven, Philip Leroux, Bart Dhoedt, et al. “Modeling and Predicting the Popularity of Online News Based on Temporal and Content-related Features.” Multimedia Tools and Applications, 2017, pp 1409-1436. 10. M. T. Uddin, M. J. A. Patwary, T. Ahsan and M. S. Alam, “Predicting the popularity of online news from content metadata,” International Conference on Innovations in Science, Engineering and Technology (ICISET), Dhaka, 2016, pp. 1-5. 11. Shestakov Andrey, EngelbertMephuNguifo, “Predicting web-page popularity with Machine Learning and Heuristic Time-Series Prediction approaches”, ECML/PKDD Discovery Challenge on Predictive Web Analytics, Nancy, France, September ,2014, pp 1-5. 12. Gitte Vanwinckelen and WannesMeert, “Predicting the popularity of online articles with random forests”, ECML/PKDD Discovery Challenge on Predictive Web Analytics, Nancy, France, September, 2014, pp 1-6. 13. Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, Ambuj K. Singh, “GPOP: Scalable Group-level Popularity Prediction for Online Content in Social Networks”, Proceedings of the 26th International Conference on World Wide Web, 2017, pp 725-733. 14. Kieu B.T., Ichise R., Pham S.B., “Predicting the Popularity of Social Curation”, Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, volume 326. Springer, Cham, 2015, 413-424. 15. Ying Hu, Changjun Hu, Shushen Fu, Peng Shi and Bowen Ning, “Predicting the Popularity of Viral Topics Based on Time Series Forecasting”, Volume 210, 2016, pp 55-65 16. S. Aghababaei and M. Makrehchi, “Mining Social Media Content for Cri Prediction,” IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, 2016, pp. 526-531. 17. Sérgio Moro, Paulo Rita, Bernardo Vala, “Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach”, Journal of Business Research, Volume 69, 2016, pp 1-11. 18. S. T. Barnard, “PMRSB: Parallel Multilevel Recursive Spectral Bisection,” Supercomputing :Proceedings of the ACM/IEEE Conference on Supercomputing, San Diego, CA, USA, 1995, pp. 27-27. 19. G. Karypis and V. Kumar, “Parallel Multilevel k-way Partitioning Scheme for Irregular Graphs,” Supercomputing :Proceedings of the ACM/IEEE Conference on Supercomputing, Pittsburgh, PA, USA, 1996, pp. 35-35 20. Chen, X., He, Z., Sun, L., “A bayesian tensor decomposition approach for spatiotemporal traffic data imputation”, Transportation Research Part C: Emerging Technologies 98, 73 – 84, 2019. 21. Zhao, Q., Zhang, L., Cichocki, A., “Bayesian cp factorization of incomplete tensors with automatic rank determination”, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (9), 1751–1763, 2015. 22. Y. Chen, C. Hsu and H. M. Liao, “Simultaneous Tensor Decomposition and Completion Using Factor Priors,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, March 2014, pp. 577-591. 23. L. Sorber, M. Van Barel, and L. De Lathauwer, “Optimization-based algorithms for tensor decompositions: Canonical polyadic decomposition, decomposition in rank-(Lr, Lr, 1) terms, and a new generalization,”SIAM J. Optim., vol. 23, no. 2, 2013, pp. 695–720. 24. E. S. Allman, P. D. Jarvis, J. A. Rhodes, and J. G. Sumner, “Tensor rank, invariants, inequalities, and applications,”SIAM J. Matrix Anal. Appl., vol. 34, no. 3, 2013, pp. 1014–1045. 25. C. J. Hillar and L.-H. Lim, “Most tensor problems are NP-hard,”J. ACM, vol. 60, no. 6, Nov. 2013, Art. ID 45. 26. D. Goldfarb and Z. Qin, “Robust low-rank tensor recovery: Models and algorithms,”SIAM J. Matrix Anal. Appl., vol. 35, no. 1, 2014, pp. 225–253. 27. Maehara, Takanori, Kohei Hayashi, and Ken-ichiKawarabayashi. “Expected tensor decomposition with stochastic gradient descent.” In Thirtieth AAAI conference on artificial intelligence. 2016. 28. Paatero, Pentti. “Construction and analysis of degenerate PARAFAC models.” Journal of Chemometrics: A Journal of the Chemometrics Society 14, no. 3 (2000): 285-299. 29. Chen, Xinyu, Zhaocheng He, Yixian Chen, Yuhuan Lu, and Jiawei Wang. “Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model.” Transportation Research Part C: Emerging Technologies 104 (2019): 66-77. 30. Chen, Xinyu, Zhaocheng He, and Jiawei Wang. “Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition.” Transportation research part C: emerging technologies 86 (2018): 59-77. 31. Koren, Yehuda, Robert Bell, and Chris Volinsky. “Matrix factorization techniques for recommender systems.” Computer 8 (2009): 30-37. 32. Charlier, Jeremy, Gaston Ormazabal, Radu State, and Jean Hilger. “VecHGrad for solving accurately complex tensor decomposition.” arXiv preprint arXiv:1905.12413 (2019). 33. Koren, Yehuda. “Collaborative filtering with temporal dynamics.” In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 447-456. ACM, 2009. 34. Hu, Yifan, Yehuda Koren, and Chris Volinsky. “Collaborative filtering for implicit feedback datasets.” In 2008 Eighth IEEE International Conference on Data Mining, pp. 263-272. Ieee, 2008.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 6

35. Kingma DP, Adam BJ.,“A method for stochastic optimization:,arXiv preprint arXiv:1412.6980. 2015, pp 1-15. 36. E. Ceulemans and H. A. L. Kiers, Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method, British J. Math. Statist. Psych., 59 (2006), pp. 133–150.

Author(s): V. Yasaswini, Santhi Baskaran

Title of the Article: An Optimization of Feature Selection for Classification using Bat Algorithm

Abstract: Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f- Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Bat algorithm on UCI datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset (UCI) has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly.

Keywords: Optimization, Meta-Heuristic, Feature Extraction, Deep Learning.

References: 1. Tan, Steinbach, Kumar. (2005). “Introduction to Data Mining”. 2. Hassan AbouEisha et.al, (2018) “Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining” 3. Sunil Kawale,”Datamining and Optimization Techniques” International Journal of Statistika and Mathematika”, ISSN. 2277- 2790, E-ISSN. 2249-8605, Volume 6, Issue 2, 2013 pp 70-72 4. Nidhi Tomar and Prof. Amit Kumar Manjhvar ”A Survey on Data mining optimization Techniques” International Journal of Science Technology & Engineering | Volume 2 | Issue 06 | December 2015 ISSN (online). 2349-784X 5. Basturk B, Karaboga D (2006) “An artificial bee colony (ABC) algorithm for numeric function optimization”. IEEE Swarm Intelligence Symposium, 12–14 May, Indianapolis 6. Bergh F, Engelbrecht AP (2006) “A study of particle swarm optimization particle trajectories”. Inf Sci 176. 937– 971. 7. Rao, R. Venkata. “Teaching Learning Based Optimization Algorithm. And Its Engineering Applications”. Springer, 2015. 8. Rao, R. Venkata, and V. D. Kalyankar. "Parameter optimization of modern machining processes using teaching– learning-based optimization algorithm”. Engineering Applications of Artificia Intelligence 26, no. 1 (2013). 524-531. 9. Shunmugapriya .P and Kanmani S, P.Sindhuja, G.Koperundevi, V.Yasaswini, “Firefly Algorithm Approach for the Optimization of Feature Selection to Perform Classification”, International Conference on Advances in Engineering & Technology, IEEE-ICAET 2014. 10. Xin-She Yang, Suash Deb, “Cuckoo Search Via Levy Flights”, World Congress On Nature and Biologically Inspired Computing (NaBIC 2009) 11. Xin-She Yang and X. He. “Bat algorithm: Literature review and applications”. International Journal of Bio-Inspires Computation, 5(3):141-149, 2013. 12. Richardson, P.: The secrete life of bats. http://www.nhm.ac.uk

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 7

Author(s): Farisa T S, Elizabeth Isaac

Title of the Article: Sequence Based DNA-Binding Protein Prediction

Abstract: Protein and DNA have vital role in our biological processes. For accurately predicting DNA binding protein, develop a new sequence based prediction method from the protein sequence. Sequence based method only considers the protein sequence information as input. For accurately predicting DBP, first develop a reliable benchmark data set from the protein data bank. Second, using Amino Acid Composition (AAC), Position Specific Scoring Matrix (PSSM), Predicted Solvent Accessibility (PSA), and Predicted Probabilities of DNA-Binding Sites (PDBS) to produce four specific protein sequence baselines. Using a differential evolution algorithm, weights of the properties are taught. Based on those attained properties, merge the characteristics with weights to create an original super feature. And tensor-flow is used to paralyze the weights. A suitable feature selection algorithm of tensor flow’s binary classifier is used to extract the excellent subset from weighted feature vector. The training sample set is obtained in the training process, after generating final features. The classification is learned through the support vector machine and the tensor flow. And the output is measured using a tensor surface. The choice is done on the basis of threshold of likelihood and protein with above-threshold chance is considered to be DBP and others are non-DBP.

Keywords: AAC, DBP,PSA, PSSM.

References: 1. K. C. Wong, Y. Li, C. Peng et al, A Comparison Study for DNA Motif Modeling on Protein Binding Microarray, in:IEEE/ACM Transactions on Computational Biology & Bioinformatics, vol. 13, no. 2, pp. 1-1, 2016. 2. J. N. Si, R. Zhao, and R. L. Wu, An Overview of the Prediction of Protein DNA-Binding Sites, in:International Journal of Molecular Sciences, vol. 16, no. 3, pp. 5194-5215, 2015. 3. P. W. Rose, A. Prlic´, C. Bi et al., The RCSB Protein Data Bank: views of structural biology for basic and applied research and education, Nucleic acids research, vol. 43, no. D1, pp. D345-D356, 2015. 4. X. P. Schmidtke, and X. Barril, Understanding and predicting drug- gability. A high-throughput method for detection of drug binding sites, in:Journal of medicinal chemistry, vol. 53, no. 15, pp. 5858-5867, 2010. 5. Pradnya P. Mandlik, Samruddhi S. Mhatre, Hiding Data into Reserve Space before Image Encryption using Blowfish Algorithm, Volume 140– No.10, April 2016.

Author(s): Matius Irsan Kasau, ST. Aminah Dinayati Ghani

Title of the Article: Discovery of New Theory Analysis of Equilibrium Point Population Versus Food on Theory of Thomas Robert Malthus and Its Development (Case Study of Indonesia)

Abstract: The future of humans on this tiny planet earth has entered a grim beginning in the midst of rapidly growing technological progress. How not, human life whose population is growing fast is not comparable to food as a source of life that grows slowly. This study aims to calculate the cross point or equilibrium point between population and food using the population series and food series of Thomas Robert Malthus original and the results of its development by Matius Irsan Kasau. The data and methods used are types of secondary data sourced from the Indonesian Central Statistics Agency (BPS), which is processed by a mathematical method that compares the population with food in each series. The results of research with Indonesian data in 2010 showed that for the original Malthus theory with the number of children on average per couple of 4 people, the distance between generations 25 years, 75 years of life expectancy, population 237 million, food 66.5 million tons obtained equilibrium point occurred in 2085, namely in the third generation. As for Malthus's theory of development results with an average number of children of 2.6 people, a distance between 23 years generation, and 69 years of life expectancy, the equilibrium point was obtained in 2171, namely the seventh generation of the current generation.

Keywords: Cross Point, Equilibrium Point, Malthus, Matius.

References: 1. Bhende A Asha & Kanitar Tara. 1988. Principles of Population Studies. Himalaya Publisher House. New Delhi.

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2. Kasau Matius Irsan. 2018. Penemuan Teori Demografi baru “Teori Umum Populasi dan Pangan” (Pengembangan Teori Populasi dan Pangan Thomas Robert Malthus. Jilid 1, Penerbit Celebes Media Perkasa Makassar, Indonesia 3. Kasau Matius Irsan. 2018. Penemuan Teori Demografi baru “Teori Umum Populasi dan Pangan” (Pengembangan Teori Populasi dan Pangan Thomas Robert Malthus. Jilid 2, Penerbit Celebes Media Perkasa Makassar, Indonesia 4. Kasau, Matius Irsan. 2009. Pembuktian Secara Mathematik Kebenaran Rentang Waktu dalam Bible. Jurnal Pembangunan Wilayah dan Masyarakat. ISSN:1412-1484. Volume 8. Nomor 2. Universitas Atma Jaya Makassar. 5. Kasau, Matius Irsan. 2009. Disain Formula Matematik untuk Menghitung Populasi Manusia dan Komposisinya. Jurnal Pembangunan Wilayah dan Masyarakat. ISSN:1412-1484. Volume 9. Nomor 1. Universitas Atma Jaya Makassar. 6. Kasau, Matius Irsan, 2012. Teori Umum Pertumbuhan Populasi & Pangan, Kajian Matematis Pengembangan Teori Populasi & Pangan Thomas Robert Malthus. Edisi Hak Cipta HKI Kemenkumham Indonesia. 7. Malthus, Thomas. 1798. An Essay on the Principle of Population. Printed for J. Johnson, in St. Paul’s Church- Yard. London. 8. Misra D, Bhaskar. 1980. The Study of Population. South Asia Publisher. New Delhi. 9. P.C. Saxena & P.P Talwar. 1987. Recent Advances in the Techniques for Demographic Analysis. Himalaya Publisher House. New Delhi. 10. S. Shryock, Hendry, Cs. 1980. The Methods and Materials of Demography. Volume 1. Department of Commerce, Bureau of the Census. 11. S. Shryock, Hendry, Cs. 1980. The Methods and Materials of Demography. Volume 2. Department of Commerce, Bureau of the Census.

Author(s): Vini.K, H.Padma Kumar, K.M.Nissamudeen

Title of the Article: The Role of Bismuth on the Best Red Light Emitting Nanophosphors

Abstract: This paper explains the role of bismuth in the luminescence enhancement of Y2O3:Eu nanophosphors prepared by Combustion method. Bi ions serve as effective sensitizers for visible emitting rare earths for Light Emitting Diodes. From the X-ray diffraction studies, bismuth co-activated nanophosphors exhibit an early crystallization. Bismuth incorporation not only results in the luminescence enhancement at 612 nm, due to 5D0 to 7F2 transition but also reduces the processing temperature for intense photoemission.

Keywords: Bismuth, Doping, Luminescence, Nanophosphor.

References: 1. A. Scarangella, R. Reitano, G. Franzò, F. Priolo, and M. Miritello, J. Lumin., vol. 191, Nov. 2017, pp. 92–96. 2. A. Yousif, V. Kumar, R. M. Jafer, and H. C. Swart, Appl. Surf. Sci., vol. 424, Dec. 2017 ,pp. 407–411. 3. A. Yousif, R. M. Jafer, S. Som, M. M. Duvenhage, E. Coetsee, and H. C. Swart, Appl. Surf. Sci., vol. 365, Mar. 2016,pp. 93–98. 4. J. Hao, S. A. Studenikin, and M. Cocivera, J. Lumin., vol. 93, no. 4, Aug. 2001, pp. 313–319. 5. K. M. Nissamudeen, S. Sankar, A. H. Bahna, and K. G. Gopchandran, J. Phys. Chem. Solids, vol. 70, no. 5, May 2009, pp. 821–826. 6. G. Siddaramana Gowd, Manoj Kumar Patra, Sandhya Songara, Anuj Shukla, Manoth Mathew, Sampat Raj Vadera and Narendra Kumar , J. Lumin., vol. 132, no. 8, Aug. 2012, pp. 2023–2029. 7. Z. Liu, L. Yu, Q. Wang, Y. Tao, and H. Yang, J. Lumin., vol. 131, no. 1, Jan. 2011, pp. 12–16. 8. K. M. Nissamudeen, R. G. A. Kumar, V. Ganesan, and K. G. Gopchandran, J. Alloys Compd., vol. 484, no. 1–2, Sep. 2009,pp. 377–385. 9. T. Yan, D. Zhang, L. Shi, H. Yang, H. Mai, and J. Fang, Mater. Chem. Phys., vol. 117, no. 1, Sep. 2009, pp. 234– 243. 10. J. S. Bae, J. H. Jeong, S. Yi, and J.-C. Park, Appl. Phys. Lett., vol. 82, no. 21, May 2003,pp. 3629–3631. 11. B. K. Gupta, D. Haranath, S. Saini, V. N. Singh, and V. Shanker, Nanotechnology, vol. 21, no. 5, Feb. 2010,p. 055607. 12 J. R. Jayaramaiah, B. N. Lakshminarasappa, and B. M. Nagabhushana, Sens. Actuators B Chem., vol. 173, Oct. 2012,pp. 234–238. 13. J. Zhang, Z. Zhang, Z. Tang, Y. Lin, and Z. Zheng, J. Mater. Process. Technol., vol. 121, no. 2–3, Feb. 2002,pp. 265–268. 14 J. Dhanaraj, R. Jagannathan, T. R. N. Kutty, and C.-H. Lu, J. Phys. Chem. B, vol. 105, no. 45, Nov. 2001, pp. 11098–11105.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 9

15. G. A. Hirata, J. McKittrick, M. Avalos-Borja, J. M. Siqueiros, and D. Devlin, Appl. Surf. Sci., vol. 113–114, Apr. 1997, pp. 509–514. 16. V. Kumar Rai, A. Pandey, and R. Dey, J. Appl. Phys., vol. 113, no. 8, Feb. 2013, p. 083104. 17. J. Y. Cho, K.-Y. Ko, and Y. R. Do, Thin Solid Films, vol. 515, no. 7–8, Feb. 2007,pp. 3373–3379.

Author(s): Vini.K, H.Padmakumar, K.M.Nissamudeen

Title of the Article: Luminescence Study of Red Light Emitting Y2O3:Sm3+ Nanophosphors and Enhancement by Co-doping with Gadolinium oxide.

Abstract: This work presents the optical and structural properties of samarium oxide doped and gadolinium oxide codoped yttrium oxide nanophosphors prepared by Combustion method. The photoluminescence emission intensity was maximum for 2wt% Sm3+ doped Y2O3 powders, that results 4G5/2 -6H7/2 transition within Samarium, emits red light at 608 nm under the excitation of 260 nm. In the case of co-dopant, maximum intensity is obtained for 3wt% Gd3+ under the excitation of 255 nm.The Y:Sm:Gd exhibit luminescence intensity of 4.21 times more than that of Y:Sm nanophosphors. These results indicate that the prepared nanophosphors can be used in optoelectronic devices.

Keywords: Bandgap, Co-Doping, Energy Transfer, Nanophosphor.

References: 1. Murthy, K. V. R. & Virk, H. S. Luminescence Phenomena: An Introduction. Defect Diffus. Forum 347, 2013,1–34 2. Verma, T. & Agrawal, S. Photoluminescence characteristics of Sm3+ and Eu3+ doped yttrium oxide phosphors. J. Mater. Sci. Mater. Electron. 29, 2018, 13397–13406 . 3. Kang, Y. C., Roh, H. S. & Park, S. B. Preparation of Y2O3:Eu Phosphor Particles of Filled Morphology at High Precursor Concentrations by Spray Pyrolysis. Adv. Mater. 12, 2000, 451–453. 3+ 4. Bae, J. S., Jeong, J. H., Yi, S. & Park, J.-C. Improved photoluminescence of pulsed-laser-ablated Y2O3:Eu thin- film phosphors by Gd substitution. Appl. Phys. Lett. 82,2003 , 3629–3631. 5. Zhou, Y., Lin, J. & Wang, S. Energy transfer and upconversion luminescence properties of Y2O 3 :Sm and Gd2O3 :Sm phosphors. J. Solid State Chem. 171, 2003, 391–395. 6. Som, S., Sharma, S. K. & Shripathi, T. Influences of Doping and Annealing on the Structural and Photoluminescence Properties of Y2O3 Nanophosphors. J. Fluoresc. 23, 2013, 439–450 . 7. Liu, Z., Yu, L., Wang, Q., Tao, Y. & Yang, H. Effect of Eu,Tb codoping on the luminescent properties of Y 2O3 nanorods. J. Lumin. 131,2011, 12–16 . 8. Yan and T. Dramicanin , Reflux synthesis, formation mechanism, and photoluminescence performance of 3+ monodisperse Y2O3:Eu nanospheres. Mater. Chem. Phys. 117, 2009,234–243. 9. Culubrk, S., Lojpur, V., Djordjevic, V. & Dramicanin, M. D. Annealing and doping concentration effects on Y2O3: Sm3+ nanopowder obtained by self-propagation room temperature reaction. Sci. Sinter. 45, 2013,323–329. 3+ 10. Boukerika, A. & Guerbous, L. Annealing effects on structural and luminescence properties of red Eu -doped Y2O3 nanophosphors prepared by sol–gel method. J. Lumin. 145, 2014,148–153. 11. Nissamudeen, K. M., Kumar, R. G. A., Ganesan, V. & Gopchandran, K. G. Enhanced photoemission from 3+ nanoscale agglomerations in Li co-activated Y2O3:Eu thin films. J. Alloys Compd. 484, 2009,377–385. 12. Jadhav and A. P. Park, Effect of Different Surfactants on the Size Control and Optical Properties of Y2O3 :Eu Nanoparticles Prepared by Coprecipitation Method. J. Phys. Chem. C 113, 2009,13600–13604. 13. G. Siddaramana Gowd, Manoj Kumar Patra, Sandhya Songara, Anuj Shukla, Manoth Mathew, Sampat Raj Vadera and Narendra Kumar , Effect of doping concentration and annealing temperature on luminescence 3+ properties of Y2O3:Eu nanophosphor prepared by colloidal precipitation method. J. Lumin., vol. 132, no. 8, Aug. 2012, pp. 2023–2029. 3+ 14. Packiyaraj, P. & Thangadurai, P. Structural and photoluminescence studies of Eu doped cubic Y2O3 nanophosphors. J. Lumin. 145,2014, 997–1003. 15. Srinivasan, R., Yogamalar, R., Vinu, A., Ariga, K. & Bose, A. C. Structural and Optical Characterization of Samarium Doped Yttrium Oxide Nanoparticles. J. Nanosci. Nanotechnol. 9, 2009,6747–6752. 16. Atabaev, T. Sh., Thi Vu, H. H., Kim, H.-K. & Hwang, Y.-H. The optical properties of Eu3+ and Tm3+ codoped Y2O3 submicron particles. J. Alloys Compd. 525, 2012,8–13. 3+ 17. K.M. Nissamudeen and K.G. Gopchandran, Nanostructured transparent and luminescent Y2O3:Eu thin films. J Optoelectron. Adv.Mater. 10,2008,2719–2726. 3+ 18. Robindro Singh and L. D. Haranath, Luminescence study on Eu doped Y2O3 nanoparticles: particle size, concentration and core–shell formation effects. Nanotechnology 19, 2008, 055201.

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3+ 19. Li, J.-G., Li, X., Sun, X. & Ishigaki, T. Monodispersed Colloidal Spheres for Uniform Y2O3 :Eu Red-Phosphor Particles and Greatly Enhanced Luminescence by Simultaneous Gd3+ Doping. J. Phys. Chem. C 112, 2008,11707– 11716. 20. Jayaramaiah, J. R., Nagabhushana, K. R. & Lakshminarasappa, B. N. Effect of lithium incorporation on 3+ luminescence properties of nanostructured Y2O3:Sm thin films. J. Anal. Appl. Pyrolysis 123,2017, 229–236 . 3+ 21. Devaraju, M. K., Yin, S. & Sato, T. Solvothermal Synthesis and Characterization of Eu Doped Y2O3 Nanocrystals. J. Nanosci. Nanotechnol. 10, 2010,731–738 . 22. Medina Velazquez, D. Y. Zhang, H.G. Paris and C.J. Summers,White luminescence of bismuth and samarium codoped Y2O3 phosphors. Ceram. Int. 41, 2015,8481–8487. 23. Kodaira, C. A., Stefani, R., Maia, A. S., Felinto, M. C. F. C. & Brito, H. F. Optical investigation of nanophosphor prepared by combustion and Pechini methods. J. Lumin. 127, 2007, 616–622 . 24. Krishna, R. H. A. Gupta and N. Brahme, Effect of Calcination Temperature on Structural, Photoluminescence, and 3+ Thermoluminescence Properties of Y2O3 :Eu Nanophosphor. J. Phys. Chem. C 117, 2013,1915–1924 . 25. Ćulubrk, S., Lojpur, V., Antić, Ž. & Dramićanin, M. D. Structural and optical properties of europium doped Y2O3 nanoparticles prepared by self-propagation room temperature reaction method. J. Res. Phys. 37, 2013,39–45 . 26. Gupta, B. K., Haranath, D., Saini, S., Singh, V. N. & Shanker, V. Synthesis and characterization of ultra-fine 3+ Y2O3 :Eu nanophosphors for luminescent security ink applications. Nanotechnology 21, 2010,055607. 3+ 27. Huang, H.C.He, Y. Guan and L. Yao, Ultra-small sized Y2O3:Eu nanocrystals: One-step polyoxometalate- assisted synthesis and their photoluminescence properties. J. Lumin. 132, 2012,2155–2160. 3+ 28. Jiang, Y. D., Wang, Z. L., Zhang, F., Paris, H. G. & Summers, C. J. Synthesis and characterization of Y2O3: Eu powder phosphor by a hydrolysis technique. J. Mater. Res. 13, 1998,2950–2955. 29. Gupta, A., Brahme, N. & Prasad Bisen, D. Electroluminescence and photoluminescence of rare earth (Eu,Tb) doped Y2O3 nanophosphor. J. Lumin. 155, 2014,112–118 . 30. M. Buijs, A. Meyerink and G. Blasse, Energy transfer between Eu ions in a lattice with two different. J. Lumin 37(1),1987, 9–20. 31. Hong, G. Y., Jeon, B. S., Yoo, Y. K. & Yoo, J. S. Photoluminescence Characteristics of Spherical Y2O3:Eu Phosphors by Aerosol Pyrolysis. J. Electrochem. Soc. 148, 2001,H161. 32. Mhlongo, G. H., Dhlamini, M. S., Swart, H. C., Ntwaeaborwa, O. M. & Hillie, K. T. Dependence of 3+ 3+ 3+ photoluminescence (PL) emission intensity on Eu and ZnO concentrations in Y2O3:Eu and ZnO·Y2O3:Eu nanophosphors. Opt. Mater. 33, 2011,1495–1499 .

Author(s): Anjali Jain, Agya Mishra

Title of the Article: Design of IoT based Real-Time Bus Tracking App using HF-RFID

Abstract: Public Transportation is the major means of Bus among people. A recent survey by the National Sample Survey Organization says that about 62-66% of people use the bus as their mode of transport. Public Bus tracking system aims at providing the instant status of the bus to the users via an automated system. This paper describes a design of IoT enabled real time bus tracking system. In this work a bus tracking mobile phone app is developed, using that people can exactly locate the bus status and time to bus arrival at bus-stop. This work uses high frequency RFID tags at buses and RFID receivers at bus-stops and with NodeMCU real time RIFD tagging (bus running) information is collected and uploaded on cloud. Users can access the bus running and status from cloud on mobile app in real time.

Keywords: Internet on Things, UHF-RFID, Bus-monitoring, NodeMCU, Blynk cloud, FAR, FRR.

References: 1. A. A. Larionov, R. E. Ivanov and V. M. Vishnevsky, "UHF RFID in Automatic Vehicle Identification: Analysis and Simulation," in IEEE Journal of Radio Frequency Identification, vol. 1, no. 1, pp. 3-12, March 2017. 2. A. Deebika Shree, J. Anusuya and S. Malathy, "Continuous Bus Tracking and Location Updation System," 2019 fifth International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 242-245. 3. P. A. Kamble and R. A. Vatti, "Bus following and checking to utilize RFID," 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla, 2017, pp. 1-6. 4. T. Lindgren, B. Kvarnström and J. Ekman, "Monte Carlo reenactment of a radio recurrence recognizable proof framework with moving transponders utilizing the fractional component equal circuit technique," in IET Microwaves, Antennas and Propagation, vol. 4, no. 12, pp. 2069-2076, December 2010.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 11

5. A. A. Habadi and Y. S. Abu Abdullah, "Keen Safety School Buses System Using RFID and Carbon Dioxide Detection," 2018 first International Conference on Computer Applications and Information Security (ICCAIS), Riyadh, 2018, pp. 1-7.

6. Sarah Aimi Saad, Amirah 'Aisha Badrul Hisham, Mohamad Hafis Izran Ishak, Mohd Husaini Mohd Fauzi, Muhammad Ariff Baharudin, Nurul Hawaii Idris "Continuous on-Campus Public Transportation Monitoring System " IEEE fourteenth International Colloquium on Signal Processing and its Applications (CSPA 2018), 9 - 10 March 2018. 7. Darshan Ingle, Dr A. B. Bagwan " Real-Time Analysis and Simulation of Efficient Bus Monitoring System" second International meeting on Electronics, Communication and Aerospace Technology (ICEC 2018) 8. Manini Kumbhar, Meghana Surface, Pratibha Mastud, Avdhut Salunke " Real-Time Web-Based Bus Tracking System" IRJET-International Research Journal of Engineering and Technology, Volume: 03| Issue: 02 | Feb-2016 9. Nusrath Jahan, Kamal Hossein and Muhammad Kamrul, Hossain Patwary "Execution of a Vehicle Tracking System utilizing Smartphone and SMS administration" 2017 fourth International Conference on Advances in Electrical Engineering (ICAEE) 28-30 September. 10. Jerrin George James, Sreekumar Nair "Effective, Real-time Tracking of Public Transport, Using LoRa WAN and RF Transceivers" Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017 11. Leeza Singla, Dr Parteek Bhatia "GPS Based Bus Tracking System" IEEE International Conference on Computer, Communication and Control (IC4-2015). 12. Supriya Sinha, Pooja Sahu, Monika Zade, Roshni Jambhulkar, Prof. Shrikant V. Sonekar "Ongoing Analysis and Simulation of Efficient Bus Monitoring System" second International meeting on Electronics, Communication and Aerospace Technology (ICEC 2018) 13. Maria Anu. V, Sarika D., Sai Keerthy G., Jabez J. "An RFID Based System For Bus Location Tracking And Display " 2015 International Conference on Innovation Information in Computing Technologies(ICIICT). 14. Chengdu Jia, Quanhu Li, Nan Li "Plan and Implementation of Bus Real-time Human Traffic Statistics System" 2016 twelfth International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC- FSKD). 15. Darshan Ingle "Exploratory Estimates of Low-Cost Bus Tracking System Using Area-Trace Algorithm" 2015 Fifth International Conference on Communication Systems and Network Technologies. 16. Jitendra Damade, Agya Mishra,Indoor RFID Tracking System Based on UKF Fusion Estimation Techniques, Digital Signal Processing, Vol 9, issue 7, pp129-134 17. Jitendra Damade, Agya Mishra, RFID Based Application Algorithms for Communication System: A Literature, International Journal on Computer Science and Engineering (IJCSE), vol . 9, issue 3, 2017, pp 61-69

Author(s): Salim Raza Qureshi

Title of the Article: An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO

Abstract: With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a novel technique based on Artificial Neural Network-and Particle Swarm Optimization Algorithm (ANN-PSO) for enabling a highly secured framework. The ANN-PSO method was created to predict health status from a database and its functions were selected from these data sets. The particle swarm optimization algorithm matches the ANN for better results by reducing errors. The results show the potential of the ANN-PSO-based methodology for satisfactory health prediction results. This proposed approach will be tested using large medical data in a Hadoop environment. The proposed work will be carried out in the JAVA work phase.

Keywords: ANN-PSO, Accuracy, Classifier, Error,GOA, Health condition.

References: 1. Loai A. Tawalbeh, Rashid Mehmood, Elhadj Benkhlifa, Houbing Song,” Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications”, IEEEAccess, Volume: 4,2016

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 12

2. DongxiaoGu,Jingjing Li, XingguoLi,Changyong Liang,” Visualizing the knowledge structure and evolution of big data research in healthcare informatics”, International Journal of Medical Informatics, Volume 98, Pages 22-32, 2017 3. Aisha Siddiqa, Ahmad Karim, Abdullah Gani,” Big data storage technologies: a survey”, Frontiers of Information Technology & Electronic Engineering, August 2017, Volume 18, Issue 8, pp 1040–1070 4. Min Chen,ShiwenMao, Yunhao Li,” Big Data: A Survey”, Mobile Networks and Applications, Volume 19, Issue 2, pp 171–209, 2014 5. Lopez, D., Gunasekaran, M., Murugan, B. S., Kaur, H., Abbas, K. M.,” Spatial big data analytics of influenza epidemic in Vellore, India”, In Big Data (Big Data), IEEE International Conference on (pp. 19–24). IEEE, 2014 6. Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub AitLahcen, Samir Belfkih,” Big Data Technologies: A Survey” Journal of King Saud University - Computer and Information Sciences, 2017 7. S. Wang, X. Chang, X. Li, G. Long, L. Yao, and Q. Z. Sheng, ``Diagnosis code assignment using sparsity-based disease correlation embedding,'' IEEE Trans. Knowl. Data Eng., vol. 28, no. 12, pp. 3191_3202, Dec. 2016. 8. Gang Luo,” Predict-ML: a tool for automating machine learning model building with big clinical data”, Health Information Science and Systems, 2016 9. V. Tresp, J. M. Overhage, M. Bundschus, S. Rabizadeh, P. A. Fasching, and S. Yu, ``Going digital: A survey on digitalization and large-scale data analytics in healthcare,'' Proc. IEEE, vol. 104, no. 11, pp. 2180_2206, Nov. 2016. 10. Dimitrios H. Mantzaris, George C. Anastassopoulos and Dimitrios K. Lymberopoulos,” Medical Disease Prediction Using Artificial Neural Networks”, IEEE International Conference on Bioinformatics and BioEngineering,2008 11. S. Gopakumar, T. Tran, T. D. Nguyen, D. Phung, and S. Venkatesh, ``Stabilizing high-dimensional prediction models using feature graphs,'' IEEE J. Biomed. Health Inform., vol. 19, no. 3, pp. 1044_1052, May 2015. 12. H. Li, X. Li, M. Ramanathan, and A. Zhang, ``Prediction and informative risk factor selection of bone diseases,'' IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 12, no. 1, pp. 79_91, Jan./Feb. 2015. 13. Sudha Ram, Wenli Zhang, Max Williams, Yolande Pengetnze,” Predicting Asthma-Related Emergency Department Visits Using Big Data”, IEEE Journal of Biomedical and Health Informatics, Volume: 19, Issue: 4, 2015 14. Trang Pham,Truyen Tran, Dinh Phung, Svetha Venkatesh,” Predicting healthcare trajectories from medical records: A deep learning approach”, Journal of Biomedical Informatics, Volume 69, Pages 218-229, 2017 15. Fan Zhang, JunweiCao, Samee U. Khan,KeqinLi, Kai Hwang,” A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications”, Future Generation Computer Systems, Volumes 43–44, Pages 149-160, 2015 16. Liqiang Nie, Meng Wang, Luming Zhang, Shuicheng Yan, Bo Zhang, Tat-Seng Chua,” Disease Inference from Health-Related Questions via Sparse Deep Learning”, IEEE Transactions on Knowledge and Data Engineering, Volume: 27, Issue: 8, 2015 17. Shiva Pratap Gopakumar, Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh,” Stabilizing High- Dimensional Prediction Models Using Feature Graphs”, IEEE Journal of Biomedical and Health Informatics, Volume: 19, Issue: 3, 2015 18. J. Henriques, P. Carvalho, S. Paredes, T. Rocha, J. Habetha, M. Antunes, J. Morais,” Prediction of heart failure decompensation events by trend analysis of telemonitoring data”, IEEE Journal of Biomedical and Health Informatics, Volume: 19, Issue: 5, 2015 19. Gunasekaran Manogaran, R. Varatharajan, M. K. Priyan,” Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System”, Multimedia Tools and Applications, Volume 77, Issue 4, pp 4379–4399, 2018 20. PrasantKumarSahoo, savedKumarMohapatra, shih-linewoo,” Analyzing Healthcare Big Data with Prediction for Future Health Condition”, IEEE Access, Volume: 4 ,2016 21. M. Rizwan, M. Jamil, and D. P. Kothari, “Generalized neural network approach for global solar energy estimation in India,” IEEE Transactions on Sustainable Energy, vol. 3, no. 3, pp. 576–584, 2012. 22. J. Yuan and S. Yu, “Privacy preserving back-propagation neural network learning made practical with cloud computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, pp. 212–221, 2014.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 13

Author(s): G. Sudha Sadasivam

Title of the Article: Crop Yield Prediction using Granular SVM

Abstract: Agriculture is the backbone of the Indian economy. Farming is a major source of income for many people in developing countries. Prediction of yield of crops is desirable as it can predict the income and minimise losses for the farmersunder unfavorable conditions. But predicting crop yield is a challenging task in developing countries like India. Conventionally, crop yield prediction is done using farmer’s expertise. The sustainability and productivity of a crop growing area are dependent on suitable climatic, soil, and biological conditions. So, data mining techniques based on neural networks, Neuro-Fuzzy Inference Systems, Fuzzy Logic, SMO, and Multi Linear Regression can be used for prediction. Previous work has performed yield prediction based on crop models considering only some of the environmental factors. This work uses a Support Vector Machine (SVM) to predict the crop yield under different environmental conditions that include soil, climate, and biological factors. Applying granular computing enables dividing the problem space into a sequence of subtasks. So, the hyperplane construction of SVM can be parallelized by splitting the problem space. Testing can also be parallelized. The main advantage is that linear SVM can be used to handle higher dimension space. Time complexity is reduced. Prediction using granular SVM can be parallelized using appropriate techniques like MapReduce/GPGPU. IoT-based agriculture increases crop yield by accurate prediction, automation, remote monitoring, and reducing wastage of resources. IoT-based monitoring systems can be used by farmers, researchers, and government officials to analyze crop environments and statistical information to predict crop yield. This paper proposes an IoT-based system to predict crop yield based on climatic, soil, and biological factors using parallelized granular support vector machines.

Keywords: Yield Prediction, SMO, Granular Support Vector Machines, MapReduce, GPGPU, IoT, Automation, Remote monitoring.

References: 1. Duan Yan-e, “Design of Intelligent Agriculture Management Information System Based on Io”, Fourth International Conference on Intelligent Computation Technology and Automation 2011, vol. 1 pp.1045 – 1049. 2. X. Hu and S. Qian, "IoT application system with crop growth models in facility agriculture”, 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), Seogwipo, Korea (South), 2011, pp. 129-133. 3. Andreas Kamilaris, Feng Gao, Francesc X. Prenafeta-Boldú and Muhammad Intizar Ali., "Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications", IEEE World Forum on the Internet of Things (WF-IoT), Reston, VA, USA, December 2016, pp. 442-447 4. Junyan Ma, Xingshe Zhaou, Shining Li, Zhigang Li, “Connecting Agriculture to the Internet of Things through Sensor Networks", IEEE International Conference, 2011, pp. 184-187 5. Sean Dieter Tebje Kelly, Nagender Kumar Suryadevara and Subhas Chandra Mukhopadhyay, "Towards the Implementation of IoT for Environmental Condition Monitoring in Homes", IEEE Sensors Journal,vol.13, no.10, October 2013, pp.3846-3853, 6. Hemlata Channe, Sukhesh Kothari, Dipali kadam,"Multidisciplinary model for Smart Agriculture using IoT, Sensors, Cloud-Computing, Mobile Computing & Big data Analysis", Int.J.Computer Technology & Applications,Vol 6 (3), 2018, pp. 374-382 7. Prem Prakash Jayaraman, Doug Palmer, Arkady Zaslavsky, Dimitrios Georgakopoulos, "Do-it-Yourself Digital Agriculture Applications with Semantically Enhanced IoT Platform", IEEE Tenth International Conference (ISSNIP) Singapore,7-9 April 2015, pp. 1-6. 8. D Ramesh, B Vishnu Vardhan, “Data Mining Techniques and Applications to Agricultural Yield Data”, International Journal of Advanced Research in Computer and Communication Engineering Vol.2, Issue.9, September 2013, pp. 233-240. 9. E.Manjula, S.Djodiltachoumy," Analysis of Data Mining Techniques for Agriculture Data", International Journal of Computer Science and Engineering Communications, Vol.4, Issue.2, 2016, pp.1311-1313. 10. D Ramesh, B Vishnu Vardhan, "Analysis Of Crop Yield Prediction Using Data Mining Techniques”, International Journal of Research in Engineering and Technology, vol.04,Issue:01, Jan 2015, pp.470-474. 11. Raorane A.A, Kulkarni R.V, "Data Mining: An effective tool for yield estimation in the agricultural sector", International Journal of Emerging Trends &Technology in Computer Science(IJETTCS), vol.1, Issue 2, August 2012, pp. 220-225.

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 14

12. Rajshekhar Borat, Rahul Ombale, Sagar Ahire, Manoj Dhawade, P.S. Kulkarni, “Data Mining Technique to Predict Annual Yield for Major Crops”, International Journal for Scientific Research & Development| vol. 4, Issue 03, 2016, pp.1835-1837. 13. B.VishnuVardhan, D. Ramesh "Density-Based Clustering Technique on Crop Yield Prediction", International Journal of Electronics and Electrical Engineering Vol. 2, No. 1, March 2014, pp.246-250. 14. N.Gandhi, Leisa J. Armstrong and OwaizPetkar, Amiya Kumar Tripathy, “Rice Crop Yield Prediction in India using Support Vector Machines”, 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016, pp.1-5. 15. Yuchun Tang, Bo Jin, Yan-Qing Zhang, "Granular Support Vector machines with association rules mining for protein homology prediction”, Artificial Intelligence in Medicine, vol. 35, Feb.2005, pp. 121-134. 16. Monali Paul, Santosh K. Vishwakarma, Ashok Verma, “Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach”, International Conference on Computational Intelligence and Communication Networks, 2015, pp. 766-771 17. Narayanan Balakrishnan and Dr.Govindarajan Muthukumarasamy, “Crop Production-Ensemble Machine Learning Model for Prediction”, International Journal of Computer Science and Software Engineering, Volume 5, Issue 7, July 2016, pp.32-39. 18. Naushina Farheen, R. V. Argiddi, “Annual Crop Yield Prediction and Recommend Planting of different crops by Using Data Mining Techniques", International Journal of Innovative Research in Computer and Communication Engineering., vol. 4, Issue 10, October 2015, pp. 1-6. 19. A.T.M Shakil Ahamed, Navid Tanzeem Mahmood, Nazmul Hossain, Mohammad Tanzir Kabir, Kallal Das, Faridur Rahman, Rashedur M Rahman, “Applying Data Mining Techniques to Predict Annual Yield of Major Crops and Recommend Planting Different Crops in Bangladesh”, IEEE SNPD 2015, June 2015, pp.1-8. 20. S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh, “Machine Learning Approaches for Forecasting Crop Yield based on Climatic Parameters”, International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 – 05, 2014. 21. Raju Prasad Paswan, Shahin Ara Begum, “Regression and Neural Networks Model for Prediction of Crop Production”, International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September 2013, pp.98-108. 22. Aakunuri Manjula1 and Dr. G.Narsimha, “Crop Yield Prediction with Aid of Optimal neural network in spatial data mining: new approaches”, International Journal of Information & Computation Technology, vol. 6, no.1, 2016, pp.25-33.. 23. Ashwani Kumar Kushwala, SwetaBhattachrya, “Crop yield prediction using Agro Algorithm in Hadoop”, IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 5, No2, April 2015, pp.271-274. 24. N.Gandhi, Leisa J. Armstrong and Owaiz Petkar, Amiya Kumar Tripathy, "Predicting Rice Crop Yield Using Bayesian Networks", Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 2016, pp.795-799. 25. Haedong Lee, Aekyung Moon, “Development of Yield Prediction System Based on Real-time Agricultural Meteorological Information”, ICACT2014, 2014, pp. 1302-1305. 26. Aditya Shastry, Sanjay H A and Madhura Hegde, “A Parameter Based ANSIF Model for Crop Yield Prediction”, IEEE International Advance Computing Conference (IACC), 2015, pp.253-257 27. V.R.Thakare, H.M.Baradkar , "Fuzzy System for Maximum Yield from Crops", International Journal of Applied Information Systems, 2013, pp. 4-9

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 15

Author(s): Soumi Ghosh, Devanshu Tyagi, Daksh Vashisht, Abhishek Yadav, Dharmendra Rajput

Title of the Article: A Comprehensive Survey of Personalized Music Identifier System

Abstract: Music occupies a very important space in the heart and life of common people and it is rather subjective and universal nature indeed. Music Identifier System is obviously concerned with providing a very meaningful and personalized recommendation of items i.e. songs, music, playlist according to the mood, emotion, interest and preference of the users or listeners. With the advancement of technologies, rapid development of internet, it has become very common to use the streaming services to listen and enjoy music or songs in more convenient ways. In this paper, an attempt has been made to perform a comparative analysis, systematic research, empirical thorough review on various approaches or strategies proposed and applied by different researchers in the task of designing an effective system for music identification or recommendation. The basic theme of the paper includes music identifier system, its components, and different features along with emphasize on the methods, metrics, general framework and state-of-art strategies proposed during the last two decades or so, have been empirically reviewed. The existing studies were found lacking with systematic research work on the behaviour, requirements and preferences of the users plus poor level of extraction of features and limitations in the area of evaluation of performance of the music identifier systems. Although, the study reveals that systems based on effective, social information, emotional-traits, content, context and knowledge have been widely applied and improved the quality of identification or recommendation of music to a large extend but still it is not enough. In future, more in-depth studies or research work need to be conducted based on enlarging the scope of further development of personalized contextual awareness based music identifier system and generating a continuous and automatic top playlist of music and songs with added tracks matching with profile, mood, emotional traits, and behaviour of the user in a mobile environment.

Keywords: About four key words or phrases in alphabetical order, separated by commas.

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Author(s): Ushveen Kaur, Sugandha Gupta

Title of the Article: Evaluating Quality -Measures to Improve NAAC Ranking for Higher Education Institutes

Abstract: Quality in education is imperative and thus it is a matter of great concern for the universities, colleges and institutions to maintain it. There are varied criteria to measure quality and methods to improve it with time. A lot of Higher Education Institutions (HEI) offer courses across streams for the students to pursue. The success of an educational institute depends on the quality of education. Educationalists, policy makers, researchers and scholars across the world are working towards quality management for continuous improvement, student/faculty satisfaction and institutional excellence. The National Assessment and Accreditation Council, NAAC, an autonomous institution has been established by the University Grants Commission with the prime agenda of assessing and accrediting Higher Education Institutes(HEI), facilitating them to work continuously towards improving the quality of education. The assessment process is carried out in three stages, which comprises of, viz., Self Study Report (SSR), Student Satisfaction Survey and the Peer Team Report. In the NAAC’s Self study report seven criterion are used for assessment; among all, criteria II: Teaching, Learning and Evaluation carry a major weightage of 350 points. In this paper, we will be briefly discussing the quality measures that can be taken up by any HEI regarding Teaching, Learning and Evaluation methodologies to improve upon its overall score and ranking. A survey was also conducted amongst graduation level students from various universities asking them multiple research questions related to measures that can be taken up by the colleges to improve quality in teaching, learning and evaluation.

Keywords: NAAC, HEI, Criteria, Key Indicators, Quantitative Measures, Teaching, Learning, Qualitative Measures, Higher Education, Accreditation, A&A.

References: 1. Harvey L., Williams J., 2010, ‘Fifteen Years of Quality in Higher Education - Part II’, Quality in Higher Education, 16 (2), 81–113 2. Williams J., Harvey L., 2015, Quality Assurance in Higher Education. In: Huisman J., de Boer H., Dill D.D., Souto-Otero M. (eds) The Palgrave International Handbook of Higher Education Policy and Governance. Palgrave Macmillan, London 3. Zhao, Jing, Dorinda J. Gallant, 2012, ‘Student Evaluation of Instruction in Higher Education: Exploring Issues of Validity and Reliability’, Assessment and Evaluation in Higher Education, 37(2), 227–235 4. http://naac.gov.in/images/docs/Manuals/TeacherEducationManual-15-11-2019.pdf 5. Horsburgh, M. (1999) ‘Quality monitoring in higher Education: The impact on student learning’, Quality in Higher Education, 5(1), 9–25 6. European University Association (EUA) (2006) Quality Culture in European Universities: A Bottom-up Approach (Brussels: European Universities Association) 7. Leiber, Theodor, BjørnStensaker, and Lee Harvey. 2015. “Impact Evaluation of Quality Assurance in 8. Higher Education: Methodology and Causal Designs.” Quality in Higher Education 21 (3): 288–311 9. Dill, D. D. (1995) ‘Through Deming’s eyes: A cross-national analysis of quality assurance policies in higher education’, Quality in Higher Education, 1(2), 95–110. 10. Harvey, L. (2005) ‘A history and critique of quality evaluation in the UK’, Quality Assurance in Education, 13(4), 263–276. 11. Kaur, Dr. Harpreet, & Kaur, Ushveen, (2019). “Governmental Initiative of DigiLocker: An Empirical Study with Respect to Undergraduate College Students”, International Journal of Research in Engineering, IT and Social Sciences, Volume 09 Issue 03, Pg. 135-141 12. Blanco Ramírez, G. (2014) ‘Trading quality across borders: Colonial discourse and international quality assurance policies in higher education’, Tertiary Education and Management, 20(2), 121–134 13. Harvey, Lee, and Diana Green. 1993. “DefiningQuality.” Assessment and Evaluation in Higher 14. Education 18 (1): 9–34. 15. Filippakou, O. and T. Tapper (2008) ‘Quality assurance and quality enhancement in higher education: contested territories? ’Higher Education Quarterly 62(1–2), 84–100. 16. Rosa, M. J. and A. Amaral (2014) Quality Assurance in Higher Education Contemporary Debates (Houndsmills: Palgrave Macmillan). 17. Swinglehurst, D., Russell, J. and T. Greenhalgh (2008) ‘Peer observation of teaching in an online environment: An action research perspective’, Journal of Computer Assisted Learning 24(5), 383–393. 18. Gosling, D. and V.-M. D’Andrea (2001) ‘Quality development: A new concept for higher education’, Quality in Higher Education, 7(1), 7–17.

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19. Harvey, L. and D. Green (1993) ‘Defining quality’, Assessment and Evaluation in Higher Education, 18(1), pp. 9– 34. 20. Tam, M. (2001) ‘Measuring quality and performance in higher education’, Quality in Higher Education, 7(1), 47– 54. 21. Massy, W., Graham, S. and P. M. Short (2007) Academic Quality Work: A Handbook for Improvement (Bolton, MA: Anker Publishing). 22. Woodhouse, D. (2003) ‘Quality improvement through quality audit’, Quality in Higher Education, 9(2), 133–139. 23. Narasimhan, K. (2001) ‘Improving the climate of teaching sessions: The use of evaluations by students and instructors’, Quality in Higher Education, 7(3), 179–190. 24. Newton, J. (2000) ‘Feeding the beast or improving quality? Academics ’perceptions of quality assurance and quality monitoring’, Quality in Higher Education, 6(2), 153–163.

Author(s): Ali N Alzaed

Title of the Article: The Impacts of Orientation and Building form on Internal Temperature of Visitor Center Building for Moderate and Hot Climate

Abstract: Passive building strategies such as building form, orientation and window ratio can have an essential impact on the indoor temperature. Building form and ordination can obtain heat gains. The designer usually designs buildings with little consideration of heat gains. This study pointed to the influence of building form and orientation in internal temperature in moderate and hot climates. In the present paper, the impact of building orientation on the indoor thermal comfort conditions expressed in terms of internal temperature is numerically investigated. This is motivated by required achievement of the thermal comfort conditions in such buildings located on hot climate regions. Moreover, the moderate climate regions are also incorporated in the present study. The numerical simulation is carried out using the TAS EDSL software to assess the optimum form model for a prefab visitor center. The result, in a moderate climate, showed that the ideal direction was obtained when the visitor center faces the south direction. Different models for building orientations have been studied and the results are presented. The results should that the internal temperature was 37.85oC for the currently model orientation and 37.71oC for the other model (known as model D), where the external temperature was 37.9oC. The worst orientations were the west direction for the case study and the east for the D model. In terms of hot climate, the internal temperature decreased by 1.0oC when west-facing. However, models with openings decreased 0.5oC. There are other passive design strategies that can be installed to models which can lead to improving the thermal comfort. The strategies can be considered for further future research.

Keywords: Building Design In Moderate climate, Internal Temperature, Orientation and Visitor Center Building form.

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Author(s): Sadashiva M, M. Yunus Sheikh, Nouman Khan, Ramesh Kurbet, T.M.Deve Gowda

Title of the Article: A Review on Application of Shape Memory Alloys

Abstract: SMA has drawn massive interest and hobby in today’s years in a great form of an extensive sort of commercial applications, due to their precise and superior properties, this concern improvement has been bearing with the useful resource of way of improvement and carried out research studies. SMA can heal its original shape at a certain temperature even under maximum loads applied and huge inelastic deformation. In this overview, we describe the primary functions of SMAs, their constitutive models, and their features. We also explained various properties that help to build a device/system. These devices help in cueing health issues such as heart treatment emptying urine so on. SMA has important in reducing the vibration of structures by increasing damping of the materials and this has effective in energy dissipating comparing with other materials. In the aerospace industry wing aircraft, rotorcraft, spacecraft, and micro-electromechanical systems are made up of SMA. In the automobile sector, fuel injectors and thermal valves are constructed with SMA materials. Current work focuses on various applications and properties of SMA, in the field of Medical, Civil structure, Automobile, and Aerospace industry.

Keywords: Shape Memory, Pseudoelasticity, Stents, Catheter, Isolator, Hydroxyapatite, Multi-Functionality, Energy Dissipation.

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72. K. Mori, S. Okamoto, and M. Akimoto, “Placement of the urethral stent made of shape memory alloy in management of benign prostatic hypertrophy for debilitated patients,” Journal of Urology, vol. 154, no. 3, pp. 1065–1068, 1995. 73. Chang WS, Araki Y. Use of shape-memory alloys in construction: A critical review. Proc Inst Civ Eng Civ Eng. 2016;169(2):87–95. 74. Song G, Ma N, Li HN. Applications of shape memory alloys in civil structures. Eng Struct. 2006;28(9):1266–74. 75. Wilde K, Gardoni P, Fujino Y. Base isolation system with shape memory alloy device for elevated highway bridges. Engineering Structures 2000; 22:222–9. 76. Khan MM, Lagoudas D. Modeling of shape memory alloy pseudoelastic spring elements using Preisach model for passive vibration isolation. Proceedings of SPIE 2002;4693:336–47. 77. Mayes JJ, Lagoudas D, Henderson BK. An experimental investigation of shape memory alloy pseudoelastic springs for passive vibration isolation. In: AIAA space 2001 conference and exposition. 2001 78. Dolce M, Cardone D, Marnetto R. SMA re-centering devices for seismic isolation of civil structures. Proceedings of SPIE 2001;4330: 238–49. 79. 79. Corbi O. Shape memory alloys and their application in structural oscillations attenuation. Simulation Modeling Practice and Theory 2003; 11:387–402. 80. 80. D. Stoeckel. Shape memory actuators for automotive applications, Materials & Design. 11 (1990) 302-7. 81. Melton KN. Ni–Ti based shape memory alloys. In: Duerig TW, Melton KN, Stoeckel D, Wayman CM, editors. Engineering aspects of shape memory alloys. London: Butterworth-Heinemann Ltd, 1990. p. 21–35. 82. Ming H Wu. Cu-based shape memory alloys. In: Duerig TW, Melton KN, Stoeckel D, Wayman CM, editors. Engineering aspects of shape memory alloys. London: Butterworth-Heinemann Ltd, 1990. p. 69 –87. 83. Saburi T. Ti–Ni shape memory alloys. In: Otsuka K, Wayman CM, editors. Shape memory materials. Cambridge, United Kingdom: Cambridge University Press, 1999. p. 49 –96. 84. Suzuki Y. Fabrication of shape memory alloys. In: Otsuka K, Wayman CM, editors. Shape memory materials. Cambridge, United Kingdom: Cambridge University Press, 1999. p. 133– 48. 85. Dolce M, Cardone D. Mechanical behaviour of shape memory alloys for seismic applications 2. Austenite NiTi wires subjected to tension. Int J Mech Sci. 2001;43(11):2657–77. 86. Casciati, F. Faravelli, L and Petrini L., (1998) Energy dissipation in shape memory alloy devices, Computed-Aided Civil and Infrastructure Engineering, 13:433-442. 87. Humbeeck, J.V. (2003) Damping capacity of thermoelastic martensite in shape memory alloys, Journal of Alloys and Compounds, 355:58-64. 88. Kahn H, Huff MA, Heuer AH. The TiNi shape-memory alloy and its applications for MEMS. J Micromechanics Microengineering. 1998;8(3):213–21. 89. Hartl DJ, Lagoudas DC. Aerospace applications of shape memory alloys. Proc Inst Mech Eng Part G J Aerosp Eng. 2007;221(4):535–52. 90. Renner, E. Thermal engine, US Pat. 3 937 019, 1976. 91. Huff M, Gilbert J and Schmidt M Proc. IEEE Int. Conf. on Solid-State Sensors and Actuators, Transducers ’93 (Yokohama, 1993). 92. Zdeblick M J, Anderson R, Jankowski J, Kline-Schoder B, Christel L, Miles R and Weber W 1994 Proc. IEEE Solid-State Sensor and Actuator Workshop (Hilton Head, SC) p 251. 93. Jerman H 1990 Proc. IEEE Solid-State Sensor and Actuator Workshop (Hilton Head, SC) p 65. 94. Van Lintel H T G, Van De Pol F C M and Bouwstra S 1988 Sensors Actuators 15 153. 95. Folta J A, Raley N F and Lee E W 1992 Proc. IEEE Solid-State Sensor and Actuator Workshop (Hilton Head, SC) p 22. 96. Ahn C H and Allen M G 1995 Proc. IEEE Int. Micro Electro Mech. Syst. Workshop (Amsterdam, 1995). 97. Walker J 1995 Proc. IEEE Int. Conf. on Solid-State Sensors and Actuators, Transducers ’95 (Stockholm, 1995). 98. Van Alstyne L J 1984 Texas Instruments US Patent 4 441 791. 99. Bloom D M 1997 Proc. Conf. on Projection Displays III SPIE vol 3013 (Bellingham, WA: SPIE). 100. Roy S and Mehregany M 1995 Proc. IEEE Int. Micro Electro Mech. Syst. Workshop (Amsterdam) p 353. 101. McDonald Schetky L. Shape memory alloy applications in space systems. Mater Des. 1991;12(1):29–32. 102. Chau ETF, Friend CM, Allen DM, Hora J, Webster JR. A technical and economic appraisal of shape memory alloys for aerospace applications. Mater Sci Eng A. 2006;438–440(SPEC. ISS.):589–92. 103. J. Webster, Proceedings of the International Society of Air Breathing Engines Conference, Bangalore, India, September, 2001. 104. GM. Chevrolet Debuts Lightweight ‘Smart Material’ on Corvette. General Motors News; 2013. 105. Borroni-Bird CE. Smarter vehicles. Smart Structures and Materials 1997: Industrial and Commercial Applications of Smart Structures Technologies. San Diego, CA, 1997.

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Author(s): Jotirmay Chari, B Shankar

Title of the Article: Effect of Well-being on People Surrounding the Airport Corridor using Predictive Analysis on Road Accident Correlation

Abstract: Transportation demands in urban regions continue to upsurge due to population growth and travel modes’ alterations. Due to Bangalore airport location and improper road planning, there is an increase in the traffic volume, which leads to traffic congestion and road traffic accidents in the city. The present study analyses the effect of well- being on the airport corridor residents based on road traffic accidents, traffic volume, and road design. The study collected the traffic accident data from the Traffic Police department for the period from 2014-2015 to 2018-2019, and traffic volume data collected from Essel Devanhalli Tollway Pvt Ltd (EDTPL) for the similar period was analyzed. The study found a significant relationship between improper road pl Manuscript | Research Paper anning, increased traffic volume, and road traffic accidents. The study could be used for road planning as well as better traffic management.

Keywords: Airport; Road Traffic Accident; Traffic Volume; Road Planning; Well-Being

References: 1. Bengaluru Traffic Police.(Retrieved May 2020).http://www.bangaloretrafficpolice.gov.in/Accidentstats.aspx 2. Bharath, H. A., Vinay, S., Chandan, M. C., Gouri, B. A., & Ramachandra, T. V. (2018). Green to gray: Silicon Valley of India. Journal of environmental management, 206, 1287-1295. 3. Burdzik, R., & Konieczny, Ł. (2013). Research on structure, propagation and exposure to general vibration in passenger car for different damping parameters. Journal of vibroengineering, 15(4), 1680-1688. 4. Gopalakrishnan, S. (2012). A public health perspective of road traffic accidents. Journal of family medicine and primary care, 1(2), 144. 5. Hiroe, M., Ogata, S., Tamura, A., Suzuki, S., Yamada, I., & Yasuoka, M. (2016, August). A questionnaire survey on health effects of aircraft noise for residents living in the vicinity of Narita International Airport: Part-2 Analysis and result detail. In INTER-NOISE and NOISE CON Congress and Conference Proceedings (Vol. 253, No. 6, pp. 2327- 2335). Institute of Noise Control Engineering. 6. Jacyna, M., Lewczuk, K., Szczepański, E., Gołębiowski, P., Jachimowski, R., Kłodawski, M., ... & Jacyna-Gołda, I. (2014). Effectiveness of national transport system according to costs of emission of pollutants. In Safety and Reliability: Methodology and Applications (pp. 595-604). CRC Press. 7. Kim, K. H., Jahan, S. A., & Kabir, E. (2013). A review on human health perspective of air pollution with respect to allergies and asthma. Environment international, 59, 41-52. 8. Levy, J. I., Buonocore, J. J., & Von Stackelberg, K. (2010). Evaluation of the public health impacts of traffic congestion: a health risk assessment. Environmental health, 9(1), 65. 9. Maqbool, Y., Sethi, A., & Singh, J. (2019). Road safety and Road Accidents: An Insight. International journal of information and computing science volume, 6, 93-105. 10. Oreko, B. U., Nwobi-Okoye, C. C., Okiy, S., & Igboanugo, A. C. (2017). Modelling the impact of intervention measures on total accident cases in Nigeria using Box-Jenkins methodology: A case study of federal road safety commission. 11. Road Accidents in India. (2018). (Retrieved from https://morth.nic.in/sites/default/files/Road_Accidednt.pdf) 12. Sreenatha, M., Suresh, B., Vinayaka, B., Chandraprakash, K., & Kanimozhee, S. (2020). Comprehensive Study Of Traffic Congestion, Travel Time And Traffic Variation At Hebbal Flyover Using VISSIM Software. International Journal of Scientific & Technology. 13. Stübig, T., Petri, M., Zeckey, C., Brand, S., Müller, C., Otte, D., ... & Haasper, C. (2012). Alcohol intoxication in road traffic accidents leads to higher impact speed difference, higher ISS and MAIS, and higher preclinical mortality. Alcohol, 46(7), 681-686. 14. Symons, J., Howard, E., Sweeny, K., Kumnick, M., & Sheehan, P. (2019). Reduced road traffic injuries for young people: A preliminary investment analysis. Journal of Adolescent Health, 65(1), S34-S43. 15. Zegeer, C. V., & Bushell, M. (2012). Pedestrian crash trends and potential countermeasures from around the world. Accident Analysis & Prevention, 44(1), 3-11.

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Author(s): Bhishma Karki, Jeevan Jyoti Nakarmi, Saddam Husain Dhobi

Title of the Article: Feasibility of Nitrate Removal using Hydroxylamine Hydrochloride from Sundarijal River Water through a Laboratory Scale

Abstract: Sundarijal River supply drinking water in Kathmandu city, Nepal and to study the nitrate concentration, 10 different sample from different locations of the Sundarijal River was taken. The method for the removal of presence nitrate in River was tested using hydroxylamine hydrochloride dose at 25±20C with 35 minutes contact time. Samples was tested for different dose of hydroxylamine hydrochloride and reduction of nitrate increase with increasing hydroxylamine hydrochloride dosages, up to certain limit. That mean with 0.5g, 0.6g and 0.8g dosages of hydroxylamine hydrochloride, reduction of nitrate was not observed when tested with 10mg/L, 50mg/L and 100mg/L river water, orderly. This tested samples shows the feasibility of nitrate removal from River water, Sundarijal.

Keywords: Kinetic Study; Thermodynamic Parameters; Intraparticle Diffusion; Breakthrough Analysis

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22. M. Mahramanlioglu, I. Kizilcikli, and I. O. Bicer, “Adsorption of fluoride from aqueous solution by acid treated spent bleaching earth”, Journal of Fluorine Chemistry, 115, 41–47 , 2002. 23. P. V. Messina, and P. C. Schulz, “Adsorption of reactive dyes on titania-silica mesoporous materials”, Journal Colloid Interface Science, 299, 305-320, 2006. 24. A. Mittal, “Adsorption kinetics of removal of a toxic dye, Malachite Green, from wastewater by using hen feathers”, Journal of Hazardous Materials, B133, 196-202, 2006. 25. C. Namasivayam, and D. Sangeetha, “Removal of Molybdate from Water by Adsorption onto ZnCl2 Activated Coir Pith Carbon” Journal of Bioresearch Technique, 97, 1194-1200, 2006. 26. M. Islam, and R. K. Patel, “Evaluation of removal efficiency of fluoride from aqueous solution using quick lime”, Journal of Hazardous Materials, 143, 303–310, 2007. 27. H. S. Peavy, D. R. Rowe, and G. Tchobanoglous, “Environmental Engineering”, McGraw -Hill Book Company, New York, 1985. 28. B. Karki, J. J. Nakarmi, and R. B. Singh, “Analysis of PhotoElectro Catalytic Purification of Water”, International Journal of Engineering Research & Technology (IJERT), 6(6), 12-14, 2017. 29. B. Karki, J. J. Nakarmi, M. J. Keshavani, “Water Purification from organic pollutants using a photo-oxidation”, Research Journal of Applied Science, 14(6), 192-187, 2019.

Author(s): Vijit Srivastava, Ashish Khare

Title of the Article: Identification of Power Quality Disturbance

Abstract: The nature of electric force and unsettling influences happened in power signal has gotten a significant issue between the electric force providers & clients. For enhancing the force quality constant checking of force is required that is being conveyed at client's destinations. Thusly, recognition of “POWER QUALITY” aggravations, and appropriate characterization of “POWER QUALITY” D is profoundly attractive. The location and characterization of the “POWER QUALITY” D in appropriation frameworks are significant errands for insurance of force conveyed network. The majority of the unsettling influences are non-fixed and temporary in quality thus it needs progressed apparatuses and methods for the evaluation of “Power quality” unsettling influences. In this research a cross breed method is utilized for describing “POWER QUALITY” unsettling influences utilizing wavelet change and fluffy rationale. A no of “POWER QUALITY” is showed in this work before include extrication measure. Two unmistakable highlights basic to all “POWER QUALITY” unsettling influences as Energy and Total Harmonic Distortion (THD) are differently utilises discrete wavelet change and are taken care of as contributions to the fluffy master framework for exact location and order of different “POWER QUALITY” unsettling influences. The fluffy master framework characterizes the “POWER QUALITY” aggravations as well as shows whether the unsettling influence is unadulterated or accommodates music. A neural organization follow PQ Disturbance (“POWER QUALITY” D) location framework is included displayed executing many layer feed forward Neural Network ‘MFNN’.

Keywords: Power Quality, Wavelet Transformation. Occurrence Power Quality

References: 1. Abdelazeem A.Abdelsalam, Azza A.Eldesouky,Abdelhay A.Sallam,”characterization of power quality disturbances using hybrid technique of linear kalman filter and fuzzy expert system,” ELSEVIER Electric power system Reaserch 83 (2012) 41-50. 2. L.C.Saikia, S.M.Borah, S.Pait,”detection and classification of power quality disturbances using wavelet transform and neural network,” IEEE annual india conference 2010. 3. J. J. Burke, D. C. Grifith, and J. Ward, “Power quality—Two different perspectives,” IEEE Transactions on Power Delivery, vol. 5, no.3, June 1990, pp. 1501-1513. 4. G. Beylkin, R. Coifman, I. Daubechies, S. G. Mallat, Y. Meyer, L. Raphael and M. B. Ruskai, Introduction to Wavelets, Jones and Bartlett, Boston, 1991. 5. I. Daubechies, “Ten Lectures on Wavelets”, CBMS-NSF Regional Conference Series in Applied Mathematics for the Society for Industrial and Applied Mathematics, Philadelphia, 1992. 6. S. G. Mallat, “Multiresolution approximations and wavelet orthonormal bases” Transactions of American Mathematical Society, vol. 315, no. 1, 1989, pp. 69-87. 7. M. P. Collins, W. G.Hurley, and E.Jones, “The application of wavelet theory in an expert system for power quality diagnostics,” 30th Universal Power Engineering Conference, 1995. 8. Oliver Poisson, Pascal Rioual and Michel Meunier, “New Signal processing tools applied to power quality analysis”, IEEE transactions on Power Delivery, vol. 14, no. 2, July 1999, pp. 324-327.

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9. Oliver Poisson, Pascal Rioual and Michel Meunier, “Detection and Measurement of Power quality disturbances using Wavelet transform”, IEEE transactions on Power Delivery, vol. 15, no. 3, July 2000, pp. 214-219. 10. P K Dash, B K Panigrahi and G Panda, “Power quality analysis using S transform”, IEEE transactions on power delivery, vol. 18, no. 2, April 2003, pp. 23-29. 11. M. P. Collins, W. G. Hurley, and E. Jones, “The application of wavelet theory to power 12. quality diagnostics,” 29th Universal Power Engineering Conference, 1994.

Author(s): Zain Ali, Bharat Lal Harijan, Tayab Din Memon, Nazmus Nafi, Ubed-u-Rahman Memon

Title of the Article: Digital FIR Filter Design by PSO and its variants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)

Abstract: Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

Keywords: Craziness Based Particle Swarm Optimization (CRPSO), Attractive And Repulsive Particle Swarm Optimization (ARPSO), Particle Swarm Optimization (PSO), Lowpass Filter

References: 1. Abdelazeem A.Abdelsalam, Azza A.Eldesouky,Abdelhay A.Sallam,”Characterization Of Power Quality Disturbances Using Hybrid Technique Of Linear Kalman Filter And Fuzzy Expert System,” ELSEVIER Electric Power System Reaserch 83 (2012) 41-50. 2. L.C.Saikia, S.M.Borah, S.Pait,”Detection And Classification Of Power Quality Disturbances Using Wavelet Transform And Neural Network,” IEEE Annual India Conference 2010. 3. J. J. Burke, D. C. Grifith, And J. Ward, “Power Quality—Two Different Perspectives,” IEEE Transactions On Power Delivery, Vol. 5, No.3, June 1990, Pp. 1501-1513. 4. G. Beylkin, R. Coifman, I. Daubechies, S. G. Mallat, Y. Meyer, L. Raphael And M. B. Ruskai, Introduction To Wavelets, Jones And Bartlett, Boston, 1991. 5. I. Daubechies, “Ten Lectures On Wavelets”, CBMS-NSF Regional Conference Series In Applied Mathematics For The Society For Industrial And Applied Mathematics, Philadelphia, 1992. 6. S. G. Mallat, “Multiresolution Approximations And Wavelet Orthonormal Bases” Transactions Of American Mathematical Society, Vol. 315, No. 1, 1989, Pp. 69-87. 7. M. P. Collins, W. G.Hurley, And E.Jones, “The Application Of Wavelet Theory In An Expert System For Power Quality Diagnostics,” 30th Universal Power Engineering Conference, 1995. 8. Oliver Poisson, Pascal Rioual And Michel Meunier, “New Signal Processing Tools Applied To Power Quality Analysis”, IEEE Transactions On Power Delivery, Vol. 14, No. 2, July 1999, Pp. 324-327. 9. Oliver Poisson, Pascal Rioual And Michel Meunier, “Detection And Measurement Of Power Quality Disturbances Using Wavelet Transform”, IEEE Transactions On Power Delivery, Vol. 15, No. 3, July 2000, Pp. 214-219. 10. P K Dash, B K Panigrahi And G Panda, “Power Quality Analysis Using S Transform”, IEEE Transactions On Power Delivery, Vol. 18, No. 2, April 2003, Pp. 23-29. 11. M. P. Collins, W. G. Hurley, And E. Jones, “The Application Of Wavelet Theory To Power 12. Quality Diagnostics,” 29th Universal Power Engineering Conference, 1994.

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Author(s): Manju Sharma, Mukesh Kumar Sharma

Title of the Article: Implementing Hybrid Security Mechanism for Cloud Considering Intrusion, Sql Injection and Performance Degradation

Abstract: Considering the demand of cloud services research has considered the issues or problems related to cloud computing. Various approaches adopted by existing research have limited scope and there is need to increase the security of cloud computing environment. The issues of security threat in cloud environment are explained in this paper. There have been several security threats to cloud environment such as Intrusion, brute force, Sql injection, Trozen horse that could affect the security of cloud services. There remains issue of Un-authentic access. Moreover the identity management is becoming a great challenge. Previous researches have proposed cryptographic approach while some provided solution to hacking attempts along with unauthentic external access but these security mechanisms are not sufficient to protect the cloud. Research paper is introducing intelligent system that is capable to trace the intrusion using LSTM based training model. The model is trained in order to categorize intrusion accordingly. The focus of research is to increase the security from intrusion by providing intelligent LSTM approach. This mechanism would classify the transmission in different categories such as Dos-synflooding, MITM ARP spoofing, Mirai-Ackflooding, Mirai-Http flooding, Mirai-Hostbruteforceg, Mirai-UDP Flooding, scan hostport and Normal. Moreover research paper has focused on prevention of Sql injection attacks. In order to increase the security between sender and receiver research has also allowed two way port based hand shaking in order to transmit data more securely. The transmission would be initiated using default port but the actual transmission would be made using random port that would be set for specific time slot.

Keywords: Cloud Computing, IDS, Port, Sql Injection.

References: 1. Li, Peisong, and Ying Zhang. "A Novel Intrusion Detection Method for Internet of Things." In 2019 Chinese Control And Decision Conference (CCDC), pp. 4761-4765. IEEE, 2019. 2. Yin, Chuanlong, Yuefei Zhu, Jinlong Fei, and Xinzheng He. "A deep learning approach for intrusion detection using recurrent neural networks." Ieee Access 5 (2017): 21954-21961. 3. Dong, Bo, and Xue Wang. "Comparison deep learning method to traditional methods using for network intrusion detection." In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 581-585. IEEE, 2016. 4. Ullah, Imtiaz, and Qusay H. Mahmoud. "A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks." In Canadian Conference on Artificial Intelligence, pp. 508-520. Springer, Cham, 2020. 5. Althubiti, Sara A., Eric Marcell Jones, and Kaushik Roy. "Lstm for anomaly-based network intrusion detection." In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1-3. IEEE, 2018. 6. Jianghong Wei, Wenfen Liu, Xuexian Hu, “Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption”. In JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 7. S. Ruj, M. Stojmenovic, and A. Nayak, “Decentralized access control with anonymous authentication of data stored in clouds,” Parallel and Distributed Systems, IEEE Transactions on, vol. 25, no. 2, pp. 384–394, 2014. 8. X. Huang, J. Liu, S. Tang, Y. Xiang, K. Liang, L. Xu, and J. Zhou, “Cost-effective authentic and anonymous data sharing with forward security,” Computers, IEEE Transactions on, 2014, doi: 10.1109/TC.2014.2315619. 9. C.-K. Chu, S. S. Chow, W.-G. Tzeng, J. Zhou, and R. H. Deng, “Key-aggregate cryptosystem for scalable data sharing in cloud storage,” Parallel and Distributed Systems, IEEE Transactions on, vol. 25, no. 2, pp. 468–477, 2014. 10. D. Boneh and M. Franklin, “Identity-based encryption from the weil pairing,” SIAM Journal on Computing, vol. 32, no. 3, pp. 586– 615, 2003. 11. W. Aiello, S. Lodha, and R. Ostrovsky, “Fast digital identity revocation,” in Advances in Cryptology–CRYPTO 1998. Springer, 1998, pp. 137–152. 12. A. Boldyreva, V. Goyal, and V. Kumar, “Identity-based encryption with efficient revocation,” in Proceedings of the 15th ACM conference on Computer and communications security. ACM, 2008, pp. 417–426. 13. K. Liang, J. K. Liu, D. S. Wong, and W. Susilo, “An efficient cloudbased revocable identity-based proxy re- encryption scheme for public clouds data sharing,” in Computer Security-ESORICS 2014. Springer, 2014, pp. 257– 272. 14. Pratik H Sailor, Prof. Jaydeep Gheewala. "Detection and Prevention of SQL Injection Attacks", International Journal of Engineering Development and Research (IJEDR), ISSN: 2321-9939, Vol.2, Issue 2, and pp.2660-2666, June 2014, Available: http://www.ijedr.org/papers/IJEDR1402215.pdf

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15. Tejinderdeep Singh Kalsi, Navjot Kaur, “Detection and Prevention Of Sql Injection Attacks Using Novel Method In Web Applications”, Int J Adv Engg Tech/Vol. VI/Issue IV/Oct.-Dec.,2015/11-15, E-ISSN 0976-3945 16. Atefeh Tajpour, Suhaimi Ibrahim, Maslin Masrom, “SQL Injection Detection and Prevention Techniques” International Journal of Advancements in Computing Technology Volume 3, Number 7, August 201110.4156/ijact.vol3.issue7.11 Author(s): Hirofumi Maeda

Title of the Article: Automatic Compensation of the Positional Error Utilizing Localization Method in Pipe Abstract: Since 1965, a numerous number of cities implementing sewerage systems have increased rapidly throughout Japan, and sewerage development is considered to be becoming more widespread in various regions. However, with the increase of management facilities, the aging of facilities for long-term use is becoming more and more apparent. The standard expected durability of these pipes is approximately 50 years, but there is a tendency and a risk that the number of collapsed roads will increase rapidly 30 years after the pipes are laid. Against this background, maintenance of drainage and sewage pipes is critical and must be carried out continuously. Therefore, in recent years, investigation using robots have been actively conducted in order to reduce manual workload of the workers. However, these robots have a large-scale system as a whole, and as a result, they are poorly maintainable and expensive. Therefore, in this research, I have developed an autonomous and portable pipe inspection robot through the know-how on rescue robots which I have studied so far. However, for inspections using a pipe inspection robot, there is always the risk that the robot itself will tip over due to steps or small gaps at the joints of the pipes or slips caused by sludge. Therefore, to prevent tumbles and rollovers of the robot, I propose a localization method only by straight-driving control without relying on hardware. In addition, taking possible slips inside pipes into account, this method utilizes only acceleration sensor. In this study, localization method using only accelerometer mounted on the robot, which focuses on the relation between the pipe and the contact point of the tires, was shown as well as presenting a method using numerical analysis to derive the estimated values. Furthermore, it was confirmed that the estimation was stable as a result of an estimation experiment using autonomous small pipe inspection robot with and without a gradient (approx. 4/100) of a pipe, with a diameter of 189mm.

Keywords: Exploration Robot, Mobile Robot, Water Pipe, Localization, Robot Control

References: 1. Japan institute of wastewater engineering technology, “Development foundation survey of sewerage facilities management robot”, Sewer new technology Annual report of the Institute, 1992, pp.43-52. 2. Rome, E., Hertzberg, J., Kirchner, F., Licht, U. and Christaller, T., “Towards Autonomous Sewer Robots: the MAKRO Project”, Urban Water, Vol. 1, 1999, pp. 57-70. 3. Streich, H. and Adria, O., “Software approach for the autonomous inspection robot MAKRO”, in Proceedings of the 2004 IEEE International Conference Robotics and Automation, 2004, pp. 3411-3416. 4. Birkenhofer, C., Regenstein, K., Zöllner, J. M. and Dillmann, R., “Architecture of multi-segmented inspection Robot KAIRO-II”, DOI: 10.1007/978-1-84628-974-3_35, In book: Robot Motion and Control, 2007, pp.381-389. 5. Alireza, A., Yoshinori, K. and Masumi, I., “A laser scanner for landmark detection with the sewer inspection robot KANTARO”, Proceedings of the IEEE International Conference on System of Systems Engineering, 2006, pp.310- 315. 6. Alireza, A., Yoshinori, K. and Masumi, I., “An automated intelligent fault detection system for inspection of sewer pipes”, IEEJ Transactions on Electronics, Information and Systems, Vol.127, No.6, 2007, pp.943-950. 7. Amir A. F. N., Yoshinori K., Alireza A., Yoshikazu M. and Kazuo I., “Concept and design of a fully autonomous sewer pipe inspection mobile robot “KANTARO””, IEEE International Conference on Robotics and Automation, 2007, pp.4088-4093. 8. Hirokazu, U. and Kazuo, I., “Basic research on crack detection for sewer pipe inspection robot using image processing”, Proceedings of the 2009 JSME Conference on Robotics and Mechatronics, 2009, 1A1-C02. 9. Ryosuke, I., Yoshikazu, O., Shigeru, K., Yasuhiro, K., Yoshihiro, Y., Sakae, U., Takayuki, K., Hirofumi, M., Toshi, T. and Satoshi, T., “Development of remote control system for search and rescue robot in confined space”, 2010 Symposium on System Integration, 2010, pp.1238-1241. 10. Hirofumi, M., Kiyoshi, I., Yoshikazu, O., Shigeru, K. and Toshi, T., “Development of Middleware for Rescue Robots to Facilitate Device Management”, Proceedings of the JSME, No.115-1, 2011, p.123-124. 11. Hirofumi, M., Shigeru, K. and Toshi, T., “Development of system for rescue robots to facilitate device management”, Bulletin of National Institute of Technology, Yuge College, Vol. 34, 2012, pp.48-53. 12. Ayaka, N., Kazutomo, F., Toshikazu, S., Mikio, G. and Hirofumi M., “Prototype design for a piping inspection robot”, 43rd Graduation Research Presentation Lecture of Student Members of the JSME, 2013, 716.

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13. Kazutomo, F., Yoshiki, I. and Hirofumi M., “Modularization for a piping inspection robot”, 2013 Symposium on System Integration, 2013, pp.1297-1300. 14. Kazutomo, F., Toshikazu, S., Mikio, G., Yoshiki, I. and Hirofumi M., “Miniaturization of the piping inspection robot by modularization”, 44rd Graduation Research Presentation Lecture of Student Members of the JSME, 2014, 613. 15. Hirofumi, M., Takuya, K., Kazutomo, F., Yoshiki, I., Toshikazu, S. and Mikio, G., “Research and development about a piping inspection robot - Report1: Prototype design for a miniaturization -”, Bulletin of National Institute of Technology, Yuge College, Vol. 36, 2014, pp.79-82. 16. Hirofumi, M., Yoshiki, I., Toshikazu, S. and Mikio, G., “Research and development about a piping inspection robot - Report2: Prototype design for maintenance improvement -”, Bulletin of National Institute of Technology, Yuge College, Vol. 37, 2015, pp.75-79. 17. Hiroumi M., Ryota, K., “Development of a small autonomous pipe inspection robot (Modularization of hardware using the technique of wooden mosaic work)”, Transactions of the Japan Society of Mechanical Engineers, Vol.82, No.839, 2016, pp.1-16.

Author(s): Víctor Alcántara Alza

Title of the Article: Effect of Aging and Deformation Treatments on Mechanical Properties of Aluminum AA-6063

Abstract: How the parameters of artificial aging heat treatments, in AA 6063 aluminum samples, previously solubilized and deformed in cold, influence on the mechanical properties of hardness and traction was investigated. The experiments followed the sequence: First, the solubilization treatment was carried out for 2h using prismatic specimens of 10mm thickness; then the samples were cold deformed with area reductions: 30% -60% -80%. Finally, the aging treatment was carried out on all samples, using temperatures: 150-250-350-450 °C, and holding times: 1-10-30-60-90- 120 min. After aging the samples were machined according to the standards. The hardness was measured on the Vickers scale (HV) and the tensile tests followed the ASTM E 8M-95ª standard. Microscopy was performed at the optical and Electronic SEM level, complemented with an EDS analysis. It was found that the highest hardness values occur at 150 °C. The yield point YS increases as decreasing aging temperature, and decreases whith increasing deformation degree. The mechanical strength UTS increases as decreasing temperature and increasing whith deformation degree. Regarding the mechanical properties of traction, the optimal condition is found for the samples deformed at 80% and aged at 250 °C, presenting a (UTS) of 193 MPa, and 15% elongation. The samples with 80% reduction, aged at 450 °C for 120 min are those with the best recrystallization index. It would take a time greater than 120 min for the grains to thicken and the precipitates completely disappear to reach complete recrystallization. EDS analysis indicates the presence of Mg2Si precipitates and the β phase.

Keywords: Exploration Robot, Mobile Robot, Water Pipe, Localization, Robot Control

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9. Ryosuke, I., Yoshikazu, O., Shigeru, K., Yasuhiro, K., Yoshihiro, Y., Sakae, U., Takayuki, K., Hirofumi, M., Toshi, T. and Satoshi, T., “Development of remote control system for search and rescue robot in confined space”, 2010 Symposium on System Integration, 2010, pp.1238-1241. 10. Hirofumi, M., Kiyoshi, I., Yoshikazu, O., Shigeru, K. and Toshi, T., “Development of Middleware for Rescue Robots to Facilitate Device Management”, Proceedings of the JSME, No.115-1, 2011, p.123-124. 11. Hirofumi, M., Shigeru, K. and Toshi, T., “Development of system for rescue robots to facilitate device management”, Bulletin of National Institute of Technology, Yuge College, Vol. 34, 2012, pp.48-53. 12. Ayaka, N., Kazutomo, F., Toshikazu, S., Mikio, G. and Hirofumi M., “Prototype design for a piping inspection robot”, 43rd Graduation Research Presentation Lecture of Student Members of the JSME, 2013, 716. 13. Kazutomo, F., Yoshiki, I. and Hirofumi M., “Modularization for a piping inspection robot”, 2013 Symposium on System Integration, 2013, pp.1297-1300. 14. Kazutomo, F., Toshikazu, S., Mikio, G., Yoshiki, I. and Hirofumi M., “Miniaturization of the piping inspection robot by modularization”, 44rd Graduation Research Presentation Lecture of Student Members of the JSME, 2014, 613. 15. Hirofumi, M., Takuya, K., Kazutomo, F., Yoshiki, I., Toshikazu, S. and Mikio, G., “Research and development about a piping inspection robot - Report1: Prototype design for a miniaturization -”, Bulletin of National Institute of Technology, Yuge College, Vol. 36, 2014, pp.79-82. 16. Hirofumi, M., Yoshiki, I., Toshikazu, S. and Mikio, G., “Research and development about a piping inspection robot - Report2: Prototype design for maintenance improvement -”, Bulletin of National Institute of Technology, Yuge College, Vol. 37, 2015, pp.75-79. 17. Hiroumi M., Ryota, K., “Development of a small autonomous pipe inspection robot (Modularization of hardware using the technique of wooden mosaic work)”, Transactions of the Japan Society of Mechanical Engineers, Vol.82, No.839, 2016, pp.1-16.

Author(s): Benjamin Kwakye, Chan Tze Haw

Title of the Article: Theoretical Overview of Sentiment Analysis in the Real Estate Market

Abstract: With the assertion that most empirical studies are underpinned by a theory,this study aims to presents the theoretical underpinnings of sentiment analysis in the housing market. It discussed four main theories namely: the behavioral finance theory, the bubbles theory, the theory of irrational exuberance and the theory of noise traders. To the best of the authors knowledge, this overview is the first in recent past to discuss the theoretical foundations of sentiment analysis in relation to housing prices. The study contributes to the extant literature in the field through the development of theoretical framework and the identification of new research gaps for future research. It has theoretical relevance to researchers and students in the finance fraternity who are beset with or struggling to identify the most appropriate finance theories that underpins their study in real estate sentiment analysis

Keywords: Sentiment, Real Estate Market, Housing Prices And Stock Market.

References: 1. Admati, A. R. (1985). A Noisy Rational Expectations Equilibrium for Multi-Asset Securities Markets. Econometrica, 53(3), 629–658. https://www.jstor.org/stable/1911659 2. Aggarwal, R. (2014). Animal spirits in financial economics: A review of deviations from economic rationality. International Review of Financial Analysis, 32, 179–187. https://doi.org/10.1016/j.irfa.2013.07.018 3. Agnello, L., Castro, V., & Sousa, R. M. (2019). The Housing Cycle: What Role for Mortgage Market Development and Housing Finance? Journal of Real Estate Finance and Economics. https://doi.org/10.1007/s11146-019-09705-z 4. Aizenman, J. (1984). TESTING DEVIATTONS FROM PURCHASING POWER PARITY (ppp) (Issue 1475). 5. Aizenman, J., Jinjarak, Y., & Zheng, H. (2019). Housing Bubbles, Economic Growth, and Institutions. Open Economies Review. https://doi.org/https//doi.org/10.1007/s1107/s11079-019-09535-9 6. Baker, M., &Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x 7. Baker, M., &Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129 8. Barlevy, G. (2015). Bubbles and fools (pp. 54–76). 9. Bernanke, B. S. (1983). Irreversibility , Uncertainty , and Cyclical Investment. Quarterly Journal of Economics, 98(1), 85–106. http://www.jstor.com/stable/1885568 10. Black, F. (1986). Noise. The Journal of Finance, XLI(3), 528–543. https://doi.org/10.1111/j.1540- 6261.1986.tb04513.x

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11. Bracha, A., & Brown, D. J. (2013). (Ir)rational Exuberance: Optimism, Ambiguity, and Risk. In Cowles Foundation Discussion Paper No. 1898 (Vol. 1898). https://doi.org/10.2139/ssrn.2281250 12. Brunnermeier, M. K., &Oehmke, M. (2013). Bubbles, Financial Crises, And Systemic Risk. In Handbook of the Economics of Finance (Vol. 2, Issue PB). Elsevier B.V. https://doi.org/10.1016/B978-0-44-459406-8.00018-4 13. Case, K. E., & Shiller, R. J. (2003). Is There a Bubble in the Housing Market? Brookings Papers on Economic Activity, 362(2), 299–362. https://doi.org/10.1353/eca.2004.0004 14. Cerutti, E., Dagher, J., &Dell’Ariccia, G. (2017). Housing finance and real-estate booms: A cross-country perspective. Journal of Housing Economics, 38, 1–13. https://doi.org/10.1016/j.jhe.2017.02.001 15. Cesa-Bianchi, A., Cespedes, L. F., &Rebucci, A. (2015). Global liquidity, house prices, and the macroeconomy: Evidence from advanced and emerging economies. Journal of Money, Credit and Banking, 47(S1), 301–335. https://doi.org/10.1111/jmcb.12204 16. Das, P., Roland, F., Hanle, B., Russ, I. N., Hanle, B., & Russ, I. N. (2020). The Cross-Over Effect of Irrational Sentiments in Housing , Commercial Property , and Stock Markets. Journal of Banking and Finance, 105799. https://doi.org/10.1016/j.jbankfin.2020.105799 17. Ding, M. (2014). The Bubble Theory Towards a framework of Enlightened Needs. https://doi.org/10.1007/978-3- 319-00921-6 18. Dow, J., & Gorton, G. (2006). Noise Traders (Working Paper 12256; Vol. 12256). https://doi.org/10.1017/CBO9781107415324.004 19. Ernst, E., &Saliba, F. (2018). Are House Prices Responsible for Unemployment Persistence? Open Economies Review, 29(4), 795–833. https://doi.org/10.1007/s11079-018-9494-z 20. Fabozzi, F. J., Kynigakis, I., Panopoulou, E., &Tunaru, R. S. (2019). Detecting Bubbles in the US and UK Real Estate Markets. In Journal of Real Estate Finance and Economics. 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News and noise bubbles in the housing market. Review of Economic Dynamics, 1(2009), 1– 27. https://doi.org/10.1016/j.red.2019.08.001 27. Goetzmann, W. N., Labio, C., Geert, R. K., & Young, T. G. (2012). The Great Mirror of Folly: Finance, Culture, and the Crash of 1720. New Haven, CT: Yale University Press. 28. Grossman, B. S. J., & Stiglitz, J. E. (1980). On the Impossibility of Informationally Efficient Markets. American Economic Review, 70(3), 393–408. 29. Hammond, R. C. (2015). Behavioral finance : Its history and its future. Selected Honors Theses, 44. http://firescholars.seu.edu/cgi/viewcontent.cgi?article=1030&context=honors 30. Hausler, J., Ruscheinsky, J., & Lang, M. (2018). News-based sentiment analysis in real estate: a machine learning approach. Journal of Property Research, 35(4), 344–371. https://doi.org/10.1080/09599916.2018.1551923 31. Hebron, J. (2010). George Soros Market Bubble Theory & What Regulators Should Do About It. 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38. Kibunyi, D., Ndiritu, S. W., Carcel, H., & Gil-Alana, L. A. (2017). Real estate prices in Kenya: is there a bubble? Journal of Housing and the Built Environment, 32(4), 787–804. https://doi.org/10.1007/s10901-017-9541-x 39. Korkmaz, Ö. (2019). The relationship between housing prices and inflation rate in Turkey: Evidence from panel Konya causality test. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-05- 2019-0051 40. Lama, C. H.-L., & Hui, E. C.-M. (2018). How does investor sentiment predict the future real estate returns of residential property in Hong Kong? Habitat International, 75(February), 1–11. https://doi.org/10.1016/j.habitatint.2018.02.009 41. Leamer, E. E. (2015). Housing really is the business cycle: What survives the lessons of 2008-09? Journal of Money, Credit and Banking, 47(S1), 43–50. https://doi.org/10.1111/jmcb.12189 42. Ling, D. C., Ooi, J. T. L., & Le, T. T. T. (2015). Explaining house price dynamics: Isolating the role of nonfundamentals. Journal of Money, Credit and Banking, 47(S1), 87–125. https://doi.org/10.1111/jmcb.12194 43. Long, J. B. De, Shleifer, A., Summers, L. H., &Waldmann, R. J. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703–738. https://doi.org/10.1093/0198292279.003.0002 44. Martínez-García, E., & Grossman, V. (2020). Explosive dynamics in house prices? An exploration of financial market spillovers in housing markets around the world. Journal of International Money and Finance, 101. https://doi.org/10.1016/j.jimonfin.2019.102103 45. Miao, J. (2014). Introduction to economic theory of bubbles. Journal of Mathematical Economics, 53, 130–136. https://doi.org/10.1016/j.jmateco.2014.06.002 46. Milgrom, P., &Stokey, N. (1982). Information, Trade and Common Knowledge. Journal of Economic Theory, 26, 17–27. 47. Saydometov, S., Sabherwal, S., &Aroul, R. R. (2020). Sentiment and its asymmetric effect on housing returns. Review of Financial Economics, June 2019, 1–21. https://doi.org/10.1002/rfe.1097 48. Shi, S. (2017). Speculative bubbles or market fundamentals? An investigation of US regional housing markets. Economic Modelling, 66(June), 101–111. https://doi.org/10.1016/j.econmod.2017.06.002 49. Shiller, R. (2000). Exuberance Irrational. Princeton University Press. First edition. 50. Shiller, Rbert J. (2015). Irrational Exuberance: Revised and Expanded Third Edition. Princeston University press. 51. Shiller, Robert J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal OfEconomic Perspectives, 17(1), 83–104. https://doi.org/10.3905/jwm.2000.320382 52. Shiller, Robert J. (2008). Historic turning points in real estate. Eastern Economic Journal, 34(1), 1–13. https://doi.org/10.1057/palgrave.eej.9050001 53. Shiller, Robert J. (2014). Speculative asset prices. American Economic Review, 104(6), 1486–1517. https://doi.org/10.1257/aer.104.6.1486 54. Shukor, N. B. B. M., Said, R. Bin, & Majid, R. B. A. (2016). The Relationship between Housing Finance and Macroeconomics Variables in Malaysia. MATEC Web of Conferences, 66. https://doi.org/10.1051/matecconf/20166600100 55. Soo, C. K. (2018). Quantifying sentiment with news media across local housing markets. Review of Financial Studies, 31(10), 3689–3719. https://doi.org/10.1093/rfs/hhy036 56. Suciu, T. (2015). From the Classical Finance To the Behavioral Finance. Journal of Public Administration, Finance and Law, 07, 80–88. 57. T. Glindro, E., Subhanij, T., Szeto, J., & Zhu, H. (2011). Determinants of House Prices in Nine Asia-Pacific Economies. International Journal of Central Banking, 52, 163–204. https://doi.org/10.2139/ssrn.1333646 58. Tang, Y., Zeng, T., & Zhu, S. (2020). Bubbles and house price dispersion in the United States during 1975–2017. Journal of Macroeconomics, 63(July 2018), 103163. https://doi.org/10.1016/j.jmacro.2019.103163 59. Uluc, A. (2018). Stabilising House Prices: the Role of Housing Futures Trading. Journal of Real Estate Finance and Economics, 56(4), 587–621. https://doi.org/10.1007/s11146-017-9606-3 60. Yang, Z., Fan, Y., & Zhao, L. (2018). A Reexamination of Housing Price and Household Consumption in China: The Dual Role of Housing Consumption and Housing Investment. Journal of Real Estate Finance and Economics, 56(3), 472–499. https://doi.org/10.1007/s11146-017-9648-6 61. Zhang, X., & Guo, L. (2018). Research on the Impacts of Real Estate on Economic Growth: A Theoretical Model- Based Analysis. Chinese Journal of Urban and Environmental Studies, 06(04), 1850025. https://doi.org/10.1142/s2345748118500252.

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Author(s): Hershey R. Alburo, Cherry Lyn C. Sta. Romana, Larmie S. Feliscuzo

Title of the Article: Sentiment Analysis of the Academic Services of ESSU Salcedo Campus using Plutchik Model And Latent Dirichlet Allocation Algorithm

Abstract: The continuous pursuit of quality education has always been a concern of higher institutions. This can be seen in the way university teachers deliver academic services to the students in terms of professionalism, commitment, knowledge of the subject matter, teaching for independent learning, and management of learning. Students as recipients of these services are significant sources of information about their course interaction that takes place in an educational system. Utilizing Latent Dirichlet Allocation (LDA) algorithm and sentiment analysis through NRC emotion lexicons based on Plutchik Model, this study aimed to decipher students’ sentiments of the academic services and reveal commonalities contained in their qualitative responses. Results revealed five latent themes in the students’ responses as: The Disparity of Teaching Assignment to Professors Field of Expertise, Professors’ Expression of Willingness to Help Students in School-Related Matters, Desirable Traits Portrayed by a Professional Teacher, Professor’s Commitment and Dedication to Classroom Instruction, and Enhancement of Teaching Practices to Improve Quality of Academic Services. The results also suggest that majority of the students have a positive sentiments (64.42%), some of were negative (34.62%), and very few were neutral (0.95%). This study aimed to give inputs to any academic interventions undertaken by institution.

Keywords: Lda, Sentiment Analysis, Plutchik, Academic Services, Essu Salcedo, Philippines

References: 1. Agaoglu M. 2016. Predicting Instructor Performance Using Data Mining Techniques in Higher Education. IEEE, Vol. 4, 2016. 2. Sembiring P, Sembiring S, Tarigan G, Sembiring OD. 2017. Analysis of Student Satisfaction in the Process of Teaching and Learning Using Importance Performance Analysis. International Conference on Information and Communication Technology. 3. Apilado, M, 2012. Students’ Satisfaction of Instruction and Non-Instructional Services of ESSU Salcedo”, Faculty Research, ESSU Salcedo Campus 4. Suroto, S., Purba, H., & Nindiani, A., 2017. Students Satisfaction on Academic Services in Higher Education using Importance – Performance Analysis, ComTech.Journal. 8.37-43. 10.21512/ comtech. v8i1.3776 5. Liu, B. 2012. Sentiment Analysis and Opinion Mining Syntehsis Lectures on Human Language Technologies. https://doi.org.10.2200 6. Balpande L. Ketkar D. Patil M. 2017. Analysis of Students Opinions on Social Networking Site for Understanding Students Learning Practices. International Journal of Engineering Development and Research, Vol. 5(3) 7. Chen, Y., Yu, B., Zhang, X., & Yu, Y., 2016. Topic Modeling for Evaluating Students’ Reflective Writing. A Case Study of Pre-Service Teachers’ Journals. Proceedings of the 6th International Conference on Learning Analytics and Knowledge. ACM 8. Xu Y. Reynolds N. 2012. Using Text Mining Techniques to Analyze Students’Responses to a Teacher Leadership Dilemma. International Journal of Computer Theory and Engineering. Vol. 4. 9. Lin, Y.Y., Chung, SF. 2016. A Corpus-Based Study on the Semantic Prosody of Challenges. Taiwan Journal of TESOL., Vol.13, 2, 99-146 10. Creswell JW, Clark VL. 2017. Designing and conducting mixed methods research. 2nd ed. Thousand Oaks. CA:SAGE Publications; 2017. p. 1-520. 11. Berman, E. 2017. An Exploratory Sequential Mixed Methods Approach to Understanding Researchers’ Management Practices at UVM” Integrated Findings to Develop Research Data Services. Journal of Science and Librarianship 6 (1): 1-24. https://doi.org./10.2191/jeslib.2017.1104 Palmquist, ME., Karley, KM., & Dale TA. 1997. Applications of Computer-Aided Text Analysis Analyzing Literary and Nonliterary Texts. Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts. 1997. P. 131-145 12. Caluza LJ. 2018 Deciphering West Philippine Sea: A Plutchik and VADER Algorithm Sentiment Analysis. Indian Journal of Science and Technology, Vol 11(47), DOI:10.17485/ijst/2018/vlli47/130980. 13. Niyugi M, Pal, AK. 2017. Discovering Conversational Topics and Emotions Associated with Demonetization Tweets in India. arXiv:1711.04115vl[cs.CL] 14. Jivani AG. 201. A Comparative Study of stemming algorithms. International Journal of Computer Technology Applications. 2(6):1930-8

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15. Feinerer I, Hornik K, Feinerer MI. 2008. Text Mining Infrastructure in R. Journal of Statistical Software. 25:1-54 https://doi.org/10.18637//jss.v025.i05 Tong Z, Zhang H. 2016. A Text Mining Research Based on LDA Topic Modelling. DOI: 10.5121/csit. 16. Wang W, Feng Y, Dai W. 2018. Topic Analysis of Online Reviews for Two Competitive Products using Latent Dirichlet Allocation. Electronic Commerce Research and Applications. https://doi.org/10.1016/j.elerap.2018.04.003 Caluza LJ. Deciphering Published Articles on Cyberterrorism: A Latent Dirichlet Allocation Algorithm Application. International Journal of Data Mining, Modelling and Management Vol. 11, No.1, 2019 17. Blei DM, Ng AY, Jordan MI. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research.3,993-1022 18. Yildirim, I. (2012). Bayesian inference: Gibbs sampling. Technical Note, University of Rochester. 19. Verecio R. 2017. Applications of Latent Dirichlet Allocation Algorithm of Published Articles on Cyberbullying. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21. 20. Turney P, Mohammad S. 2013. Crowdsourcing: A Word-Emotion Association Lexicon.arXiv:1308.6297vi[cs.cl] 21. Tromp E. Pechenizkiy M. 2014. Rule-based emotion detection on social media:putting tweets on Plutchik’s wheel.arXiv preprint arXiv 22. Opanda JA. 2014. Implications of Mismatch Between Training and Placement in Teacher Training Colleges: A Case of Mosoriot Teachers Training College, Kenya. MIER Journal of Educational Studies, Trends & Practices. Vol 4(1), pp.88-100 23. Butt B.Z., Rehman, K. 2010. A Study Examining Students Satisfaction in Higher Education, Procedia Social and Behavioral Sciences,2 (2010) 5446-5450 24. Kaufman SR, Sandilos L. 2019. Improving Students’ Relationships with Teachers Provide Essential Supports for Learning. American Psychological Association.apa.org. 25. Pedler M. 2018. Teachers play a key role in helping students feel they ‘belong’ at school. Souther Cross University. The Conversation. theconversation.com 26. Olson J, Carter J. 2014. Caring and the College Professor. Focus on Colleges, Universities and Schools. vol. 8, No. 1 27. Sawney N. 2015. Professional Commitment among Secondary School teachers in relation to location of their school. global Journal for Research Analysis. vol4.p13-14 28. Mart C.T., 2013.A Passionate Teacher: Teacher Commitment and Dedication to Student Learning”, International Journal of Academic Research in Progressive Education and Development, January 2013, Vol. 1, ISSN: 2226- 6348. 29. Jalbani LN. 2014. The Impact of Effective Teaching Strategies on the Students’ Academic Performance and Learning Outcomes. German National Library. http://dub.dub.de. Rosenshine, B., & Furst, W., Research on Teacher Performance Criteria: In B.0 Smith(ed) Research in Teacher Education: Englewood Cliffs. Prentice Hall 30. Putri R, Kusumanigrum.2016. Latent Dirichlet Allocation for Sentiment Analysis Towards Tourism Review in Indonesia. J. Phys.Conf.Ser. 805012073 31. Syed S., Weber CT. 2018. Using Machine Learning to Uncover Latent Research Topics in Fishery Models. Reviews in Fisheries Science & Agriculture 26:3, 319-336 32. Roman, CV., Delgado, HF., Cordero, SS., 2019. Topic Modelling Applied to Business Research: A Latent Dirichlet Allocation (LDA)-Based Classification for Organization Studies, Springer 33. Chafale, D., Pimpalkar, A. 2014. A Review on Developing Corpora for Sentiment Analysis Using Plutchik’s Wheel of Emotions with Fuzzy Logic. International Journal of Computer Science and Engineering, 2014 2(10):14- 18 34. Colace, F., De Santo, M., Greco, L., Moscato, V., Picariello,A. 2016. Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction. Springer 35. Jockers, ML. 2015. “Syuzhet: Extract Sentiments & Plot Arcs from Text. https://github.com/mjockers/syuzhet Abassi, MM.,Beltiukov, A., 2019. Summarizing Emotions from Text Using Plutchik’s Wheel of Emotions. Advances in Intelligent System Research Vol. 166 36. Sanstosh, DT., Babu, KS., Prasaad, SPV., Vivekanda, A. 2016. Opinion Mining of Online Product Reviews from Traditional LDA topic Clusters using Feature Ontology Tree and Sentiwordnet. International Journal of Education and Management Engineering., 2016, 6, 34-44 DOI. 10.5815/ijeme.2016/06.04 37. Ye, J., Jing, X., Li, J., 2018. Sentiment Analysis Using Modified LDA. Springer Nature Singapore Pte. Ltd. Signal and Information Processing, Networking and Computers, Lecture Notes in Electrical Engineering 473.

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Author(s): Hatem Sadek, Mohammad H. Alenezi, Mostafa A. Ismail

Title of the Article: Implementation of Low-Pressure Water Mist System for Fire Suppression inside a Model of Road Tunnel

Abstract: Previous studies have proven the performance of certain water mist system in general or in suppressing certain tunnel fires. The southern tunnel under the Suez Canal in the province of Ismailia length of 4 kilometers and 800 meters is serving the movement from Ismailia to Sinai through the Suez Canal old and new, while serving the northern tunnel movement from Sinai to Ismailia through the two channels. This tunnel in Ismailia is the largest in the world, with outer diameter of 12.6 meters, the internal 11,40 meters, the length of the tunnel is 4830 meters and reaches 6830 meters with the entrances and exits, the distance between the north and south tunnels 12 meters, and the maximum depth of the tunnel 45 meters down both Suez Canals. Since completing this project in the begin of 2019, this Tunnel did not experimentally test. This paper describes an experimental study of a low-pressure water-mist system (LPWMS) used in a scaled fire test conducted in a section of a scaled down road tunnel. The length, width, and height of the tunnel were 6 m, 2.4 m, and 2 m, respectively, which are in a ratio of 1:4 to the dimensions of an actual tunnel. The LPWMS used a pump pressure of 5.5 bar, and the system configuration was designed according to the pressure generated by the pump. Without a ventilation fan, the fire suppression time was 275 s, and amount of water required to fully suppress the fire was 696.67 L. When a ventilation fan was used, the maximum temperature location was moved from the center of the 6 m long tunnel toward the air inlet end of the tunnel (upstream). While this study will find the performance of the LPWMS in suppressing a fire in a small section of the Ismailia tunnel, determining the times spent and the amount of water consumed in the various stages of fire suppression, and in addition to studying the effect of the ventilation fan on These results and the location of the maximum temperature in the tunnel.

Keywords: Ismailia-Sinai Tunnel; Water-Mist; Fire Safety; Froude Scaling

References: 1. SCOR Global Property & Casualty, Fire protection in tunnels: focus on road and train tunnels (2014) 1–12. 2. H. Z. Ya, Physical scaling of water mist protection of 260-m3 machinery enclosure, in International Water Mist Conference, Amsterdam (2015). 3. W. Sweda, E.E. Khalil, O.A. Huzzayyin, Temperature distributions in an underground road tunnel: effect of car fire heat release, in 55th AIAA Aerospace Sciences Meeting, Texas (2017). https://doi.org/10.2514/6.2017-1995. 4. FM Approvals, Approval standard for water mist systems, FM Approvals LLC. (2012). 5. Y.Z. Li, H. Ingason, Scaling of wood pallet fires, SP Report, 57 (2014). 6. P. Zhang, X. Tang, X. Tian, C. Liu, M. Zhong, Experimental study on the interaction between fire and water mist in long and narrow spaces, Appl. Therm. Eng. 94 (2015) 706–714. https://doi.org/10.1016/j.applthermaleng.2015.10.110. 7. Y.Z. Li, H. Ingason, Maximum ceiling temperature in a tunnel fire, SP Report 51 (2010). 8. E. Cafaro, V. Bertola, Fires in tunnels: experiments and modelling, Open Thermodyn. J. 4 (2010) 156–166. https://doi.org/10.2174/1874396X01004010156. 9. International Tunneling Association, Guidelines for structural fire resistance for road tunnels, Working Group 6 (2004).

Author(s): R.Jeena, G.Dhanalakshmi, S.Irin Sherly, S.Ashwini, R.Vidhya

Title of the Article: A Novel Approach for Healthcare Information System using Cloud

Abstract: The main objective of this paper is to outline a Cloud Computingbased Healthcare Information System that helps bridge the gap between various hospitals, patients and clinics by creating a central hub of patient details and health care history that is accessible via two interfaces- either the mobile app or the web application.

Keywords: Cloud Computing.

References: 1. Rubin, Daniel L., et al. "BioPortal: A Web Portal to Biomedical Ontologies.." AAAI Spring Symposium - Technical Report (2008):74- 77. 2. Musen, M. A., et al. "The National Center for Biomedical Ontology." Journal of the American Medical Informatics Association 19.2(2012):190-195.

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3. Oberkampf, Heiner, et al. "From Symptoms to Diseases - Creating the Missing Link." Lecture Notes in Computer Science 9088(2015):652-667. 4. Sun, Yizhou, et al. "Mining knowledge from interconnected data: a heterogeneous information network analysis approach." Proceedings of the Vldb Endowment 5.12(2012):2022-2023 5. Oberkampf, Heiner, et al. "Towards a Ranking of Likely Diseases in Terms of Precision and Recall." 1st International Workshop on Artificial Intelligence and NetMedicine. In conjunction with ECAI 2012.

Author(s): B.Devaneshwar, K.B.Amarthian, M.Yuvanthika Meenakshi, V.M.Saradha

Title of the Article: Calibrating Best Route Based on Battery Percentage and Availability of Charging Station

Abstract: The electric vehicle market is increasing rapidly. Smart cars and AI integrated cars are under development for automatic driving. Embedded software is necessary for an electric vehicle to function properly. Almost all cars have inbuilt software navigation purposes. The user's main concern about electric vehicles is the driving range. Electric cars having an inbuilt navigation system that indicates appropriate charging points suitable for the user. The planning route is essential to reach the destination before the battery dies. The software can provide a solution here by analyzing and optimizing the data which is stored in the cloud. Battery swapping can also be done by booking batteries at charge stations before the time of travel. This solution will promote users to drive electric vehicles for even long travels.

Keywords: Electric Vehicle, Battery, Zero Input Response, Open-Circuit Voltage, Zero State Response, Charge Station, MQ Telemetry Transport.

References: 1. Henry Lee and Alex Clark “Charging the Future: Challenges and Opportunities for Electric Vehicle Adoption” 2. Devaneshwar B, Amarthian K. B and Yuvanthika Meenakshi M, ”Monitoring and controlling electric car through IoT using NodeMCU”, IJARIIT, 10 October, 2020 3. Martin Murnane and Adel Ghazel,” A Closer Look at State of Charge (SOC) and State of Health (SOH) Estimation Techniques for Batteries”, Analog Devices, Technical Article 4. Maria Carmen Flavio, I Safak Bayram, Michael Devetsikitos, Danilo Antonio Sbordone,” EV charging stations and modes: International Standards”, Conference paper at SPEED AM 2014. 5. Sierra Hovet, Blair Farley, Jason Perry, Kevin Kirsche,” Introduction of Electric Vehicle Charging Stations to University Campuses: A Case Study for the University of Georgia from 2014 to 2017”, June 2018.

Author(s): B. Nadimulla, S. Aruna Mastani

Title of the Article: Adjustable PRPG for Low Power Test Patterns

Abstract: As the power consumption is more in the processes of testing, test vector set compression and controlling of toggling plays a crucial role in reducing the power consumption during test mode. In exploring the controlling techniques of toggling, Pre-Selected Toggling (PRESTO) of test patterns is a technique that can control the toggling of a test patterns in a precise manner in Built-in Self -Test (BIST) architectures. In this paper we modify the architecture of existing Full Version PRESTO that can be used to generate test vectors and in addition binary sequences used as scan chins such that the controlling of sequence of test vectors depends on number of 1’s present in the switch code which is user defined thus reducing the testing time with significant fault coverage, and in addition the optimization is also observed in area and power. The area has decreased by 12.2% and power consumption by 15.43%. The Synthesis and implementation of the architectures are done using Artix7 (xc7a100tcsg324-3) FPGA family. The simulation results have been analyzed through Mentor-graphics Questa-sim 10.7C.

Keywords: Pseudo Random Pattern Generator (Prpg), Linear Feedback Shift Register (Lfsr), Pre-Selected Toggling (Presto).

References: 1. B. Koenemann, “LFSR-coded test patterns for scan designs,” in Proc.Eur. Test Conf. (ETC), 1991, pp. 237–242. 2. V. Gherman, H. Wunderlich, H. Vranken, F. Hapke, M. Wittke, and M. Garbers, “Efficient pattern mapping for deterministic logic BIST,” in Proc. Int. Test Conf. (ITC), Oct. 2004, pp. 48–56. 3. A.-W. Hakmi, S. Holst, H. Wunderlich, J. Schloffel, F. Hapke, and A. Glowatz, “Restrict encoding for mixed- mode BIST,” in Proc. 27th IEEE VLSI Test Symp. (VTS), May 2009, pp. 179–184.

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4. A. S. Abu-Issa and S. F. Quigley, “Bit-swapping LFSR for low-power BIST,” Electron. Lett., vol. 44, no. 6, pp. 401–402, Mar. 2008. 5. J. Rajski, J. Tyszer, M. Kassab, and N. Mukherjee, “Embedded deterministic test,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 23, no. 5, pp. 776–792, May 2004. 6. S. Gerstendorfer and H. Wunderlich, “Minimized power consumption for scan-based BIST,” in Proc. Int. Test Conf. (ITC), 1999, pp. 77–84. 7. F. Corno, M. Rebaudengo, M. S. Reorda, G. Squillero, and M. Violante,“Low power BIST via non-linear hybrid cellular automata,” in Proc.18th IEEE Very Large Scale Integr. (VTSI) Test Symp., May 2000,pp. 29–34. 8. X. Lin and J. Rajski, “Adaptive low shift power test pattern generator for logic BIST,” in Proc. 19th IEEE Asian Test Symp. (ATS), Dec. 2010, pp. 355–360. 9. S. Wang and S. K. Gupta, “LT-RTPG: A new test-per-scan BIST TPG for low switching activity,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 25, no. 8, pp. 1565–1574, Aug. 2006. 10. S. Wang and S. K. Gupta, “DS-LFSR: A BIST TPG for low switching activity,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst.,vol. 21, no. 7, pp. 842–851, Jul. 2002. 11. Michał Filipek, Grzegorz Mrugalski, Senior Member, IEEE, Nilanjan Mukherjee, Senior Member, IEEE, Benoit Nadeau-Dostie, Senior Member, IEEE, Janusz Rajski, Fellow, IEEE, Je˛drzej Solecki, and Jerzy Tyszer, Fellow, IEEE “Low Power Programmable PRPG with Test compression capabilities”. 12. J. Rajski, J. Tyszer, G. Mrugalski, and B. Nadeau-Dostie, “Test generator with preselected toggling for low power built-in self-test,” in Proc. IEEE 30th VLSI Test Symp. (VTS), Apr. 2012, pp. 1–6. 13. Janusz Rajski , Jerzy Tyszer, “ Design of phase shifter for BIST Applications”. 14. X. Zhang and K. Roy, “Power reduction in test-per-scan BIST,” in Proc.6th IEEE Int. On-Line Test. Workshop (OLTW), Jul. 2000, pp. 133–138. 15. C. Zoellin, H. Wunderlich, N. Maeding, and J. Leenstra, “BIST power reduction using scan-chain disable in the cell processor,” in Proc. Int.Test Conf. (ITC), Oct. 2006, pp. 1–8, paper 32.3. 16. P. Girard, L. Guiller, C. Landrault, and S. Pravossoudovitch, “A test vector inhibiting technique for low energy BIST design.”

Author(s): B.Meena Preethi, P.Radha

Title of the Article: Disease Classification and Prediction using Ensemble Machine Learning Classification Algorithm

Abstract: In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge data from the healthcare industry were preprocessed., categorized and analyzed to find out and predict which patient to be treated and given priority and which hits the society the most. Ensemble machine learning's popularity in the medical industry is due to a variety of factors the Classifiers used are K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest. To reduce the manual processes in medical field automating these processes has become important. Electronic medical records and significant advances in health care have given an opportunity to make find out which patients need to be given more importance. Several methodologies and techniques were used to preprocess the data in order to meet the study' requirements. To improve the performance of machine learning algorithms, feature selections were made using Tabu search. When ensemble prediction is combined with the Random Forest algorithm as the combiner, the results are more reliable. The aim of this study is to create a system to classify Medical records whether it is diseased or not and find out which disease rate has increased. This research will help the society to an individual to get treated easily and take preventive measures to avoid diseases.

Keywords: Machine Learning, K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest.

References: 1. Simon Kocbek , Lawrence Cavedon David Martinez et al.,Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources Journal of Biomedical Informatics 64 (2016) 158–167. 2. https://downloads.healthcatalyst.com/wp-content/uploads/2014/05/Healthcare-Data-Mining.pdf 3. https://blog.statsbot.co/ensemble-learning-d1dcd548e936

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4. https://towardsdatascience.com/advanced-ensemble-classifiers-8d7372e74e40 5. https://www.healthcatalyst.com/data-mining-in-healthcare 6. https://www.usfhealthonline.com/resources/key-concepts/data-mining-in-healthcare/ 7. https://en.wikipedia.org/wiki/Text_mining 8. https://en.wikipedia.org/wiki/Machine_learning 9. https://www.geeksforgeeks.org/ensemble-classifier-data-mining/ 10. A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset, Computers and Electronics in Agriculture , Volume 124, June 2016, Pages 65-72 11. https://www.hindawi.com/journals/misy/2018/3860146/ 12. Zhihua Wei, Duoqian Miao, Jean-Hugues Chauchat, and Caiming Zhong, “Feature Selection based on Chinese Text Classification Using Character N –Grams , Lecture Notes in Computer Science, Publication Date: 2008 13. Cha Yang Jun Wen , “Text Categorization Based on a Similarity Approach”,Sruthi 14. Partalas, I., Tsoumakas, G., Hatzikos, E. V, & Vlahavas, I. (2008). Greedy regression ensemble selection : Theory and an application to water quality prediction. Information Sciences Journal, 178, 3867–3879. https://doi.org/10.1016/j.ins.2008.05.025 15. Ge, G., & Wong, G. W. (2008). Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles. BMC Bioinformatics, 9, 275. 16. An Improved k-Nearest Neighbor Algorithm for Text Categorization1, Li Baoli1, Yu Shiwen1, and Lu Qin2 https://arxiv.org/ftp/cs/papers/0306/0306099.pdf KNN https://arxiv.org/abs/cs/0306099 17. https://towardsdatascience.com/knn-k-nearest-neighbors-1-a4707b24bd1d 18. http://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/ 19. https://www.researchgate.net/publication/220980101_An_Improved_Algorithm_Finding_Nearest_Neighbor_Using _Kd-trees

Author(s): S.Reginold Jebitta, Durga Devi P R, Deva Dharshini L, Theerdham Naga Sai Harika, Vignesh K

Title of the Article: A Comprehensive Review on Protein Isolates from Legumes

Abstract: Legumes play an vital function in human body due to dietary fiber, protein, minerals and vitamins and well- balanced essential amino acid. Legume proteins have gained increasing significance because of preferred functional properties, including gelling and emulsifying properties. Legumes contains anti nutritional compounds like Trypsin inhibitor(TIs), Phytic acid(PA), Tannin, Saponin, Lectins, They are not a major concern for most people, but may become a problem during periods of malnutrition, these can be easily removed by dehulled, cooking, thermal process, germination after soaking. Protein isolates are advanced form of protein containing the greater amount of protein with greater digestibility. There are different types of protein isolates like chickpea, whey protein Pea protein, cowpea protein isolates .The extraction methods of protein isolates are Iso electric extraction and alkaline extraction, citric acid extraction. Our aim of this paper is to optimize the protein isolate for diet people and innovate research in this field to produce some protein enriched food formulations.

Keywords: Legumes ; Protein isolates ; Extraction techniques ; Protein isolates .

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10. M.S.Butt, R.Batool; Nutritional and functional properties of some promising legumes protein isolates. Pakistan Journal of Nutrition, 9(4), 373ñ379 (2010). 11. N.J.Enwere; Foods of plant origin.Afro-Orbis Publishers, Nsukka 124ñ145 (1998). 12. N.J.Enwere, P.O.Ngoddy; Effect of heat treatment on selected functional properties of cowpea flour. Trop.Sci. 26, (1986). 13. Canella M, Castriotta G, Bernardi A (1979) Functional and physicochemical properties of succinylated and acetylated sunflower proteins. Lebens Wiss. University u-Technology 12: 95. 14. Horax R, Hettiarachchy NS, Chen P, Jalaluddin M (2004) Preparation and characterization of protein isolate from cowpea (Vigna unguiculata L. Walp.). J Food Sci 69: fct114-fct118. 15. Abbey BW, Ibeh GO (1988) Functional properties of raw and heat processed cowpea (Vigna ungiculata, walp) flour. J Food Sci. 16. 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Emulsifying properties of chickpea, faba bean, lentil and pea proteins produced by isoelectric precipitation and salt extraction. Food Research International. 2011. 29. Gupta R, Dhillon S. Characterization of seed storage proteins of Lentil (Lens culinaris M.). Annals of Biology. 1993. 30. Saharan K, Khetarpaul N. Protein quality traits of vegetable and field peas: Varietal differences. Plant Foods for Human Nutrition. 1994. 31. Osborne TB. The vegetable proteins. Monographs in Biochemistry. London: Longmans, Green and Co.; 1924. 32. Oomah BD, Patras A, Rawson A, Singh N, Compos-Vega R. Chemistry of Pulses. In: Tiwari BK, Gowen A, Mckenna B, editors. Pulse Foods Processing: Quality & Nutritional Applications. 33. Hu HY, Pereira J, Xing LJ, Zhou GH and Zhang WG, Thermal gelation and microstructural properties of myofibrillar protein gel with the incorporation of regenerated cellulose. LWT - Food Sci Technology (2017). 34. Lin DQ, Zhang LT, Li RJ, Zheng BD, Rea MC and Miao S, Effect of plant protein mixtures on the microstructure and rheological properties of myofibrillar protein gel derived from red sea bream (Pagrosomus major). Food Hydrocolloids (2019). 35. Pan LH, Feng MQ, Sun J, Chen X and Xu XL, Thermal gelling properties and mechanism of porcine myofibrillar protein containing flaxseed gum at various pH values. CyTA - J Food (2016). 36. Zhao YY, Zhou GH and Zhang WG, Effects of regenerated cellulose fiber on the characteristics of myofibrillar protein gels. Carbohydrate Polym 209:276-281.(2019). 37. Siddiq M and Uebersax MA. Dry beans and pulses production and consumption—An overview. Dry Beans and Pulses Production, Processing and Nutrition(2012) 38. Paglarini CDS, Martini S and Pollonio MAR, Using emulsion gels made with sonicated soy protein isolate dispersions to replace fat in frankfurters. LWT -Food Sci Technology (2019). 39. 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41. Kaur M and Singh N, Studies on functional, thermal and pasting properties of flours from different chickpea (Cicer arietinum L.) cultivars. Food Chemistry 42. chicken meatballs during frozen storage. J Food Science Technology(2015). 43. Verma AK, Banerjee R and Sharma BD, Quality of low fat chicken nuggets: effect of sodium chloride replacement and added chickpea (Cicer arietinum L.) hull flour. Asian Australasian J Animal Science (2012). 44. Siddhuraju, P., Vijayakumari, K., & Janardhanan, K. (1996). Chemical composition and nutritional evaluation of an underexploited legume, Acacia nilotica (L.) Del. Food Chemistry, 57(3), 385–391. 45. Association of Official Analytical Chemists (1980). Official Methods of Analysis, 13th edn. Washing, DC. 46. Association of Vitamin Chemists (1966). Methods of Vitamin Assay, 3rd edn. InterScience Publishers, New York. 47. Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. (1956). Anal Chem. 48. Hussain, M. A., Akinyele, I. O. & Omololu, A. (1984). Perceptions of mothers on medical problems associated with cowpea consumption. Pro. And Abstract Worm Cowpea Research Conf. HTA, pp. 49. Kitson, R. E. & Mellon, M. G. (1944). Colorimetric determination of phosphorus as molybdivanadiphosphoric acid. Ind. and Eng. Chem., 16 50. Mehta, V., Jood, S. & Bhat, C. M. (1985). Effect of processing on flatus producing factors in legumes. J. Agric. Food Chem. 51. Vose, J. R. (1980). Production and functionality of starchs and protein isolates from legume seeds (field peas and horse beans). Cereal Chemistry.

Author(s): Fadare Oluwaseun Gbenga, Adetunmbi Adebayo Olusola, Oyinloye Oghenerukevwe Elohor

Title of the Article: Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers

Abstract: The proliferation of Malware on computer communication systems posed great security challenges to confidential data stored and other valuable substances across the globe. There have been several attempts in curbing the menace using a signature-based approach and in recent times, machine learning techniques have been extensively explored. This paper proposes a framework combining the exploit of both feature selections based on extra tree and random forest and eight ensemble techniques on five base learners- KNN, Naive Bayes, SVM, Decision Trees, and Logistic Regression. K-Nearest Neighbors returns the highest accuracy of 96.48%, 96.40%, and 87.89% on extra-tree, random forest, and without feature selection (WFS) respectively. Random forest ensemble accuracy on both Feature Selections are the highest with 98.50% and 98.16% on random forest and extra-tree respectively. The Extreme Gradient Boosting Classifier is next on random-forest FS with an accuracy of 98.37% while Voting returns the least detection accuracy of 95.80%. On extra-tree FS, Bagging is next with a detection accuracy of 98.09% while Voting returns the least accuracy of 95.54%. Random Forest has the highest all in seven evaluative measures in both extra tree and random forest feature selection techniques. The study results uncover the tree-based ensemble model is proficient and successful for malware classification.

Keywords: Extra-Tree, Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting Classifier, Random Forest Ensemble.

References: 1. AV-TEST (2019), The Independent IT-Security Institute, https://www.av-test.org/en/statistics/malware/. Accessed 2 November 2019. 2. Kaspersky Security Bulletin (2016), Overall statistics, https://securelist.com/kaspersky-security-bulletin-2016-e xecutive-summary/76858/. Accessed 12 May 2016. 3. McAfee Labs Threats Report (2017),https://www.mcafee.com/us/resources/reports/rp-quarterl y-threats- jun-2017.pdf. Accessed 2 June 2017. 4. Chandrashekar G. and Sahin F., “A survey on feature selection methods”, Computers & Electrical Engineering., vol. 40(1), 2014, pp.16-28. 5. HarshaLatha P. and Mohanasundaram R, “A New Hybrid Strategy for Malware Detection Classification with Multiple Feature Selection Methods and Ensemble Learning Methods”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249–8958., vol. 9(2), 2019, pp. 4013-4019. 6. Sharma J, Charul G, Ole-Christoffer G., and G. Morten G., “Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation”, EURASIP Journal on Information Security., vol. 15(1), 2019. pp. 15-27.

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7. Euh S., Hyunjong L, Donghoon K., and Doosung H., “Comparative Analysis of Low-Dimensional Features and Tree-Based Ensembles for Malware Detection Systems”, IEEE Access., vol. 8(2), 2020 pp. 76796-76808. 8. Tian R., Islam L, Batten L, and Versteeg S., “Differentiating malware from cleanware using behavioural analysis”, 5th international conference on malicious and unwanted software, IEEE, 2010. 9. Damodaran A., Troia F.D., Visaggio A., Austin H, .Stamp A., Comparison of static, dynamic, and hybrid analysis for malware detection, Journal of Computer Virology and HackingTechniques., vol. 13(1), 2017, pp.1- 12. 10. Fang Y., Yu B., Tang Y., Liu L., Lu Z., Wang Y, and Yang Q. “A new malware classification approach based on malware dynamic analysis”, In Australasian onference on Information Security and Privacy, Springer, Cham, 2017, pp. 173–189. 11. Zhang Y., Huang Q., Ma X, Yang Z, and Jiang J. “Using multi-features and ensemble learning method for imbalanced malware classification”, IEEE Trustcom/BigDataSE/ISPA, IEEE, 2016, pp. 965–973. 12. Bai, J. Wang, and G. Zou G., “A malware detection scheme based on mining format information, Sci. World Journal, vol. 12(4), 2014 pp. 36-48. 13. Ninite (2019). Benign data, www.ninite.com. Accessed in 29 November 2019. 14. Download (2018). Benign data, www.downloads.com. Accessed in 29 November 2019. 15. Softpedia (2019). Benign data, www.softpedia.com. Accessed in 29 November 2019. 16. Totalvirus (2019). Online file checker, www.totalvirus.com. Accessed in 29 November 2019. 17. Virushare (2019). Malware data, www.virushare.com. Accessed in 22 November 2019. 18. Virussign (2019). Malware data, www.virussign.com. Accessed in 22 November 2019. 19. Singh A, and Lakhotia A., “Game-theoretic design of an information exchange model for detecting packed malware in Malicious and Unwanted Software” (MALWARE), 6th International Conference, 2011, pp. 1–7 . 20. Li J., Cheng K., Wang S., Morstatter F., Trevino R.P., Tang J., and Liu H., “Feature selection: A data perspective”, ACM Computing Surveys (CSUR)., vol. 50(6), 2018, pp. 94-100, 21. Schultz M.G., Eskin E, Zadok F., and Stolfo S.J., “Data mining methods for detection of new malicious executables” Proc. IEEE Symp. Security, Privacy, 2001. 22. Konstantinou E. and Wolthusen S. Metamorphic Virus. Analysis and Detection, Technical Report RHUL-MA- 2008-02, Royal Holloway University of London, 2008. 23. Dreiseitl S.and Ohno-Machado L., “Logistic regression and artificial neural network classification models: a methodology review, Journal of Biomedical Informatics, vol. 35(6), 2002, pp. 352-359. 24. Breiman L., “Bagging predictors”, Machine Learning, vol. 24, 2002, pp. 123-140. 25. Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W, Q. Ye Q., and .Liu T.Y., “ LightGBM: A Highly Efficient Gradient Boosting Decision Tree”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 2017. 26. Ramnathv and Gdequeiroz (2017), “Gradient Boosting Machines”, https://github.com/ledell/useR- machinelearning-tutorial/blob/master/gradient-boosting-machines. Accessed 25 Sepember (2017). 27. Dada E.G., Bassi J.S., Hurcha Y.J. and Alkali A.H. , “Performance Evaluation of Machine Learning Algorithms for Detection and Prevention of Malware Attacks”, IOSR Journal of Computer Engineering (IOSR- JCE)., vol. 21(3),2019, pp. 18-27. 28. HarshaLatha P. and Mohanasundaram R. “Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3071, vol. 9(2), 2019, pp. 2055-2059.

Author(s): Prabavathi S, Kanmani P

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 43

Title of the Article: Plant Leaf Disease Detection and Classification using Optimized CNN Model

Abstract: Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.

Keywords: Agriculture, Classification, CNN, Image processing, Optimization

References: 1. Konstantinos P. Ferentinos, “Deep learning models for plant disease detection and diagnosis”. Elsevier Journal in Computer and Electronics in Agriculture. (pp. 311-318). Elsevier, January (2018). 2. Omkar Kulkarni, “Crop Disease Detection using Deep Learning”. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) 978-1-5386-5257-2/18. IEEE, (2018). 3. Parismita Bharali, Chandrika Bhuyan, Abhijit Boruah “Plant Disease Detection by Leaf Image Classification Using Convolutional Neural Network”. Information, Communication and Computing Technology 4th International Conference, (ICICCT) CCIS 1025, pp. 194–205, Springer, May (2019). 4. Ankur Das, Chirantana Mallick and Soumi Dutta, “Deep Learning-Based Automated Feature Engineering for Rice Leaf Disease Prediction”. Computational Intelligence in Pattern Recognition (CIPR), Advances in Intelligent Systems and Computing. ISSN 2194-5357, Springer, (2020). 5. Ashraf Darwish, Dalia Ezzat and Aboul Ella Hassanien, “An Optimized Model based on Convolutional Neural Networks and Orthogonal Learning Particle Swarm Optimization Algorithm for Plant Diseases Diagnosis” Swarm and Evolutionary Computation BASE DATA, S2210-6502(19)30546-2, Elsevier, November (2019). 6. Hyeon Park, Jee-Sook Eun, Se-Han Kim, “Image based disease diagnosing and predicting of the crops through deep learning mechanism”. ICTC 2017, pp (5090-4032) IEEE, (2017) 7. Mohammed Brahimi, Kamel Boukhalfa, and Abdelouahab Moussaoui, “Deep Learning for Tomato Diseases: Classification and Symptoms Visualization”. Applied Artificial Intelligence, vol:31no.4, pp (299-315), Taylor and Francis, May (2017). 8. Abirami Devaraj, Karunya Rathan, Sarvepalli Jaahnavi and K Indira, “Identification of Plant Disease using Image Processing Technique”. International Conference on Communication and Signal Processing, April, pp (5386-7595), IEEE, April (2019) 9. Shivani K. Tichkule, Prof. Dhanashri. H. Gawali, “Plant Diseases Detection Using Image Processing Techniques”. 2016 Online International Conference on Green Engineering and Technologies (IC-GET). pp (5090-4556), IEEE (2016). 10. Md. Selim Hossain, Rokeya Mumtahana Mou, Mohammed Mahedi Hasan, Sajib Chakraborty, M. Abdur Razzak, “Recognition and Detection of Tea Leaf’s Diseases Using Support Vector Machine ''. IEEE 14th International Colloquium on Signal Processing & its Applications (CSPA 2018), pp (5386-0389), IEEE, March (2018). 11. D. O. Shamkuwar, Gaurav Thakre, Amol R. More, Kishor S. Gajakosh, Muktanand O. Yewale, “An Expert System for Plant Disease Diagnosis by using Neural Network”. International Research Journal of Engineering and Technology (IRJET), pp (369-371), vol.5 issue.4, IRJET April (2018). 12. Yong Zhong, Ming Zhao, “Research on deep learning in apple leaf disease recognition”. Journal in Computers and Electronics in Agriculture, pp (0168-1699), Elsevier, December (2019). 13. Peng Zhang, Jun Meng, Yushi Luan, Chanjuan Liu, “Plant miRNA–lncRNA Interaction Prediction with the Ensemble of CNN and IndRNN”. Interdisciplinary Sciences: Computational Life Sciences (2020). vol.12 pp (82– 89), Springer, December (2019). 14. Qingxin Xiao, Weilu Li, Peng Chen, and Bing Wang, “Prediction of Crop Pests and Diseases in Cotton by Long Short-Term Memory Network”. ICIC 2018, LNCS 10955, pp (11–16), Springer (2018). 15. Muammer Turkoglu1, Davut Hanbay, Abdulkadir Sengur, ‘Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests’ Journal of Ambient Intelligence and Humanized Computing, Springer (2019). 16. PlantVillage Dataset – spMohanty’s GitHub Repo.

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https://github.com/spMohanty

Author(s): Nazia Tazeen, K.Sandhya Rani

Title of the Article: A Survey on Some Big Data Applications Tools and Technologies

Abstract: Big Data is a broad area that deals with enormous chunks of data sets. It is a word for enormous data sets having huge volume, more diverse structures of data originating from diverse sources are growing rapidly. Many data being generated because of fast data transmission between devices concerning different sectors like healthcare, science, media, business, entertainment and engineering. Data collection capacity and its storage is big concern. Apache Hadoop software is a store of accessible source programs to store big data and perform analytics and various other operations related to big data. Many organizations base their decisions by extracting knowledge from huge and complex data, because of this prime cause of decision making, Big Data has to be accurately classified and analyzed. In order to overcome the complex challenges encountered by Big Data, various Big Data tools and technologies have developed. Big Data Applications, tools and technologies used to handle it are briefly discussed in this paper.

Keywords: Big Data, Veracity, Hadoop, Structured Data, Unstructured Data.

References: 1. B. Furht and F. Villanustre, “Introduction to big data. In: Big Data Technoogies: Springer International Publishing, Chambers, pp 3–11, 2016. 2. 2, A. McAfee, E. Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review, pp.60–68, 2012. 3. Rajaraman, “Big data analytics,” Resonance vol.21, pp.695–716, 2016. 4. Kune, P.K. Konugurthi, P.K. Agarwal, A. Chillarige, R. Buyya, “The anatomy of big data computing,” Software: Practice and Experience 46(1), pp.79–105, 2016. 5. C.K. Emani, N. Cullot, C. Nicolle, “Understandable big data: a survey,” Computer Science Review, vol.17, pp.70– 81, 2015. 6. Gandomi, M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” IJIM, vol.35, pp.137–144, 2015. 7. Iqbal, F. Doctor, B. More, S. Mahmud, U. Yousuf, “Big Data Analytics: Computational Intelligence Techniques and Application areas,’ IJIM, 2016. 8. https://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day/ 9. Shilpa, M. Kaur, “International Journal of Advanced Research in Computer Science and Software Engineering,” vol.3(10), October, pp. 991-995, 2013. 10. Sagiroglu, D. Sinanc, “Big Data: A Review,” pp.20-24, May 2013. 11. N. Mangla, R.K. Khola, “Application Based Route Optimization,” IOSR Journal of Engineering, vo.2(8), pp.78-82, 2012. 12. https://www.edureka.co/blog/big-data-applications-revolutionizing-various-domains 13. https://www.simplilearn.com/how-big-data-powering-internet-of-things-iot-revolution-article 14. M Bishop, “Pattern Recognition,” Machine Learning, 128, 1-58, 2006. 15. https://mapr.com/products/apache-hadoop/ 16. Mehta and N. Mangla, “A survey paper on Big Data Analytics using Map Reduce and Hive on Hadoop Framework,” IJRAET Volume.4, Issue 2, NCRISTM-2016. 17. Maheshwari, “Big Data,” McGraw Hill Education India private limited, second edition. 18. J. Wang, W. Liu, S.kumar and S. Chang, “Learning to Hash for indexing Big Data A survey,” arXiv:1509.05472v1 [cs.LG] 19. https://www.qubole.com/products/qubole-data-service/apache-spark-service/ 20. M. Israd, M. Budiu, Y. Yu, A. Birrell, D. Fitterly, “Dryad: Distributed Data-parallel Programs from Sequential Building Blocks,” Proc. of 2007 Eurosys conf. 21. Gupta, “Learning Apache Mahout Classification,” Packt publication, UK, 2015. 22. M. Chen, S. Mao, Y. Liu, “Big Data: A Survey,” Springer Science Business Media New York, Mobile Network Applications pp.171-209, 2014. 23. Chambers, J.: Bell Laboratories: What is R? The R Foundation. http://www.r-project.org/. Accessed 5 Aug 2018

Author(s): Mahassine Bekkari, Abdellah El Fallahi

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 45

Title of the Article: Modelling and Analyzing the Employees’ Engagement in Workplace using Machine learning Tools

Abstract: In a new economy where immaterial capital is crucial, companies are increasingly aware of the necessity to efficiently manage human capital by optimizing its engagement in the workplace. The accession of the human capital through its engagement is an efficient leverage that leads to a real improvement of the companies’ performance. Despite the staple attention towards human resource management, and the efforts undertaken to satisfy and motivate the personnel, the issue of engagement still persists. The main objective of this paper is to study and model the relation between eight predictors and a response variable given by the employees’ engagement. We have used different models to figure out the relation between the predictors and the dependent variable after carrying out a survey of several employees from different companies. The techniques used in this paper are linear regression, ordinal logistic regression, Gradient Boosting Machine learning and neural networks. The data used in this study is the results of a questionnaire completed by 60 individuals. The results obtained show that the neural networks perform slightly the rest of models considering the training and validation error of modelling and also highlight the complex relation linking the predictors and the predicted.

Keywords: human resources management, machine learning; Neural networks, Boosting machine learning, logistic regression.

References: 1. Baayoud M. La gestion des RH au Maroc. In : YANAT, Z. et SCOUARNEC, A. (éd.), Perspectives sur la GRH au Maghreb, Algérie Maroc-Tunisie. AGRH/Vuibert, 2005. 2. LOUART P. Les champs de tension en gestion des ressources humaines. In : BRABET, J. (éd.), Repenser la GRH. Economica, 1993. 3. FRIMOUSSE S. et PERETTI, J.M. La GRH dans le contexte maghrébin : entre convergence et contingence. In : YANAT Z. et SCOUARNEC A. (éd.), Perspectives sur la GRH au Maghreb, Algérie –Maroc- Tunisie. Vuibert, 2005. 4. BARRAUD D. Contribution à l'étude du lien entre les pratiques de GRH et la performance financière de l’entreprise : le cas des pratiques de mobilisation. PhD thesis in management sciences, University of Toulouse 1. 1999. 2. PERETTI J.M, HELFER J.P, ORSONI J. Gestion ressources humaines. Edition Vuibert, 2013, N°19, pp.1-2. 3. TSUI, A.S. et MILKOVICH, G.T. (1987), Management Information Centre (1973) en ont respectivement identifié 122 et 170. In : GUERIN, G. et WILS, T. (1991) L’harmonisation des pratiques de gestion des ressources humaines au contexte stratégique : une synthèse. 4. BESSEYRE DES HORTS, C. Vers une gestion stratégique des ressources humaines. Paris : Editions d’organisation, 1988. 5. CUMMINGS, L. Compensation, Culture et motivation : A System Perspective. Organizational Dynamics, hiver 1984, pp. 33-43. 6. PORTER, M. Competitive Strategy. New-York: Free Press, 1980. 7. CHARLES-PAUVERS B, COMMEIRAS N. L’implication, le concept. In : NEVEU, J.P. Et THEVENET, M. (éd.), L’implication au travail. Paris : Vuibert, 2002. 8. COMMEIRAS N. L’implication, facteur d’implication organisationnelle, PhD thesis in management sciences, University of Montpellier 2, IAE de Montpellier, 1994. 9. THEVENET M. Impliquer les personnes dans l’entreprise. Les Éditions Liaisons, 1992. 10. ANGLE H.L, PERRY J.L. An empirical assessment of organizational commitment and organizational effectiveness. Administrative science quarterly,1981, vol. 26, n°1, pp. 1-14 11. IGALENS J, NEVEU JP. L’implication syndicale. In : Communication au 4ème Congrès de l’AGRH, HEC, Jouy- en-Josas, 17-18 novembre.1993. 12. MATHIEU J.E, ZAJAC D.M. A review and meta-analysis of the antecedents, correlates and consequences of organizational commitment. Psychological bulletin, 1990, vol.108, n°2, pp.171-194. 13. PIERCE J.L, RANDALL B.D. Organizational commitment: pre-employement propensity and initial work experiences. Journal of Management,1987, vol.13, n°1, pp.163-178. 14. RATNER, B. Statistical and Machine-Learning Data Mining, Third Edition: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition. Chapman & Hall/CRC, 3 Edition 2017.

15. Fahrmeir L, Kneib T, Lang S. Regression – Models, methods and applications. 2nd edition. Berlin, Heidelberg: Springer; 2009.

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16. McCullagh P: Regression models for ordinal data. J R Stat Soc B. 1980, V 42,109-142. 17. Anderson JA. Regression and ordered categorical variables. JRStat Soc B: V 46 1-30. 18. Lall R, Campbell MJ, Walters SJ, Morgan K. A review of ordinal regression models. Applied on helth-related quality of life assessments. Stat Methods Med Res. 2002, V11: 49-67. 19. Hosmer DW, Lemeshow S. Applied Logistic regression. 2000, New York: John Willy and soons,2. 20. Peterson B, Harrel FE. Partial proportional odds models for ordinal response variables. Appl Stat. 1990, 39: 205- 217. 21. Pongsapukdee V, Sulgumphaphan S. Goodness of fit of cumulative logit models for ordinal response categories and nominal explanatory variables with two-factors interaction. Silpakorn U Sciences & Tech J.2007, 1(2): 29-38. 22. Agresti A. Tutorial on modeling ordered categorical response data: Psych Bull, 1989, V 105: 290-301. 23. Alexey N, Alois K. Gradient boosting machine, a tutorial, Frontiers in Neurorobotics, December 2013. 24. Friedman, J. (2001). Greedy Boosting approximation: a gradient boosting machine. Ann. Stat. 29, 1189-1232. doi:10.1214/aos/1013203451. 25. Wang, J., Chen, Q., & Chen, Y. (2004, August). RBF kernel based support vector machine with universal approximation and its application. In International Symposium on Neural Networks (pp. 512-517). Springer, Berlin, Heidelberg. 26. Rojas, R. (1996), “Neural Networks A Systematic Introduction”, Springer, ISBN 3-540-60505-3, New York. 27. Rumelhart, D.E, Hinton, G.E., and Williams, R. (1986), “Learning Internal Representations by Error Propagation”, in: Rumlhart, D.E., and McClelland, J.L., (eds.) Parallel Distributed, vol. 1: Foundations, MIT Press, Cambridge, MA, pp. 18-362. 28. Werbos, P. J. (1974), “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences”, PhD thesis, Harvard University 20. 29. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol. 1. Massachusetts: MIT Press; 1986. pp. 318–362. 30. El-Fallahi, A. and Martí, R. (2003), “Tabu and Scatter Search for Artificial Neural networks”, Computational Modeling and Problem Solving in The Networked World, Interfaces in Computer Science and Operations Research, pp. 79-96. 31. Marti, Rafael & Laguna, Manuel & Glover, Fred. (2006). Principles of scatter search. European Journal of Operational Research. 169. 359-372. 10.1016/j.ejor.2004.08.004. 32. Glover, F. (1998), “A Template for Scatter Search and Path Relinking, in Artificial Evolution”, Lecture Notes in Computer Science 1363, Washington, DC, USA.AUTHORS PROFILE

Author(s): Sanniv Shome, Shushil Mhaske, K. Pathak, M S Tiwari

Title of the Article: Mine Waste as Resource: Indian Mining Scenario of Coal and Non Coal Mining Sector

Abstract: Mother Nature has bestowed India with huge resources of coal, iron ore, bauxite, manganese and limestone. India has one of the lowest per capita availability of land due to population of more than 1.3 billion. The transformation from under developed to developed economy warrants enormous increase in mineral production. This will generate additional huge quantities of waste. The industry is already facing problems related to land acquisition and environmental clearances. Sustainable development of Indian mineral industry requires reprocessing, reuse and recycling of mine waste. To achieve this, economic and innovative mineral processing methods are required which will result in least damage to ecology and environment.

Keywords: sustainable development, reprocessing, reuse, recycling.

References: 1. www.ibef.org, Metals and Mining industry in India 2. Dr. B. K. Pal, etc Problems of mining wastes management in India and its suggestive measures – case studies, 3. Maedeh Tayebi-Khorami etc, Re-thinking mining waste through an integrative approach led by circular economy aspirations, www.mdpi.com 4. Bernd G Lottermoser, Recycling, reuse and rehabilitation of mine waste, www.element magazine.org, Decemmber2011.

5. Larisa CHINDRIS, Valorization of mining waste in the construction industry, General considerations,

Souvenior of Volume - 9 Issue - 6 March 2021 Website: www.ijrte.org DOI: 10.35940/ijrte.2277-3878 47

6. Anup Kumar Gupta etc, A review on utilization of coal mine overburden dump waste as underground mine filling material: a sustainable approach of mining, International Journal of Mining and Mineral Engineering, 2015 Vol.6 7. Jeroen Spoorena etc, Near-zero-waste processing of low-grade, complex primary ores and secondary raw materials in Europe: technology development trends, Journal of Resource, conservation and recycling, May, 2020 8. Yellishetty, M., etc. “Reuse of iron ore mineral wastes in civil engineering constructions: A case study -resources”, Journal of .Conservation and .Recycling., Vol. 52, 2008 9. B C Gayana etc , A study on suitability of iron ore overburden waste rock for partial replacement of coarse aggregates in concrete pavements, 14th International Conference on Concrete Engineering and Technology, IOP publication, 2018. 10. Mishra R R, Inaugural Address of national conference, Geomine 2019, Nagpur 2019. 11. Yash Pandey etc.,Utilization of Coal Mixed Waste Aggregates available at Thermal Power Plants for GSB and Asphalt Mixtures, The 3rd International Conference on Transportation Geotechnics, Procedia Engineering, 2016. 12. Tara Sen etc, Usage of Industrial Waste Products in Village Road Construction, International Journal of Environmental Science and Development, Vol. 1, No. 2, June 2010 13. Shrikant R Lamani etc, Utilization of mine waste in the construction industry - A Critical Review, International Journal of Earth sciences and Engineering, vol 9, feb. 2016.

Author(s): M. Ambika ME, M. Madhunisha

Title of the Article: Smart Internet of Things Based Induction Motor Parameter Monitoring and Control System

Abstract: Automation is the use of various control systems for operating equipment such as machinery, processes in industries such as boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. This research paper presents advanced approaches using wireless monitoring system for induction motor based on Internet of Things (IoT) for safe and economic data communication. n the first approach, state of the art fault detection strategy is exhibited for induction motor. This research paper depends on the assessment of measured voltage, current, earth leakage, rotor status and speed. Progressed embedded strategy and utilized to isolate and survey the disappointment seriousness. In this procedure, distinctive sensors are associated with the motor, and the quantities are extracted by utilizing a microcontroller. The Graphical User Interface (GUI) with cloud server IoT is used to transmit the data from base station to remote station. This arrangement allows the client to interface with the framework. The proposed research paper based induction motor control system is validated through the simulation in Raspberry Pi 3 environment.

Keywords: Induction Machine, Zigbee Protocol, Wireless Control And Monitoring System.

References: 1. Tanmoy Maity , Partha sarathi Das , and Mithu Mukherjee, “Rescue and protection system for underground mine workers based on Zigbee ” Int. Jr. Of Advanced Computer Engineering & Architecture Vol. 2 No. 2 (June - December,12) 2. Jui-Yu Cheng and Min-Hsiung Hung, Jen-Wei Chang, “A ZigBee-Based Power Monitoring System with Direct Load Control Capabilities” Proceedings of the 2007 IEEE International Conference on TuesE04 Networking, Sensing and Control, London, UK, 15-17 April 2007 3. A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi by Jin-Shyan Lee, Yu-Wei Su, and Chung-Chou Shen, Information & Communications Research Labs, Industrial Technology Research Institute (ITRI) 4. F.L.Lewis, Wireless Sensor Networks-Chapter 4, Smart environments: Technologies, Protocols, and Applications Journal 5. Ramazan BAYINDIR, Mehmet ŞEN, “A Parameter Monitoring System for Induction Motors based on zigbee protocol”, Gazi University Journal of Science. GU J Sci 24(4):763-771 (2011). 6. Zulhani Rasin, Mohd Rizal Abdullah , “Water Quality Monitoring System Using Zigbee Based Wireless Sensor Network”, International Journal of Engineering & Technology IJET Vol: 9 No: 10 7. Shizhuang Lin1, Jingyu Liu2 and Yanjun Fang,” ZigBee Based Wireless Sensor Networks and Its Applications in Industrial”, International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China 603 8. Robles, T, Alcarria, R, Martin, D, Navarro, M, Calero, R, Iglesias, S & Lopez, M 2015, „An IoT based reference architecture for smart water management processes‟, Journal of wireless mobile networks, ubiquitous computing, and dependable applications, vol. 6, no. 1, pp. 4-23.

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9. Gubbi, J, Buyya, RK, Marusic, S & Palaniswami, M 2013, „Internet of Things (IoT): A vision, architectural elements, and future directions‟, Future Generation Computer Systems, vol. 29, pp. 1645-1660. 10. Aggarwal, C, Ashish, N & Sheth, A 2013, „The internet of things: A survey from the data-centric perspective‟, Managing and mining sensor data, Springer. Alur, R, Berger, E, Drobis, A W, Fix, L, Fu, K, Hager, G D, Lopresti, D, Nahrstedt, K, Mynatt, E, Patel, S, Rexford, J, Stankovic, J A & Zorn, B 2015, Systems computing challenges in the internet of things, Computing community consortium, Catalyst, pp. 1-15. 11. Bandyopadhyay, D & Sen, J 2011, „Internet of things – Applications and challenges in technology and standardization‟, Wireless personal communications, pp. 1-24.

Author(s): V. Saidulu

Title of the Article: Dielectric Cover Layer Thickness Effect on Circular Microstrip Antenna Parameters

Abstract: This paper studies the effect of dielectric cover layer thickness on circular microstrip patch antenna parameters such as gain, bandwidth, beam-width, radiation patterns, return loss and VSWR. The proposed antenna is designed with 2.4GHz frequency in S-Band region. This operating frequency useful in ISM band applications. Circular patch antenna is designed with cavity model analysis and simulated using HFSS simulation software (Electromagnetic simulator). The coaxial probe fed is used for antenna design. In this paper the effect of dielectric cover layer on antenna parameters studied experimentally and comparing their performance characteristics. The simulation results shows that the antenna without dielectric cover layer obtained gain is 4.11dB and antenna with dielectric cover the gain is reduced to 2.87dB to 5.88dB based on thickness of the dielectric cover layer. The antenna bandwidth obtained without dielectric cover is 3% and with dielectric cover its bandwidth is reduced from 0.012GHz to 0.052GHz based on thickness of the cover layer effect. Similarly other parameters are investigated and compared. This proposed circular patch antenna is used in wireless and Wi-Fi applications.

Keywords: Dielectric Cover Layer, Bandwidth, Radiation Patterns, Vswr Etc.

References: 1. IE3D Manual, Zeland software Inc.,Fremount, USA, 1999 2. I J Bhal and P Bhartia, “Microstrip antennas”, Artech house, 1980. 3. R.Shavit,”Dielectric cover effect on Rectangular Microstrip Antennas array”. IEEE Trans. Antennas propagat.,Vol 40,. PP.992-995,Avg.1992. 4. Inder ,Prakash and Stuchly, “Design of Microstrip Antennas covered with a Dielectric Layer. IEEE Trans. Antennas Propagate. Vol.AP-30.No.2,Mar 1992. 5. O.M.Ramahi and Y.T.LO, ”Superstrate effect on the Resonant frequency of Microstrip Antennas”, Microwave Opt.Technol. Lett. Vol.5, PP.254-257,June 1992. 6. A.Bhattacharyya and T. Tralman, “Effects of Dielectric Superstrate on patch Antennas”, Electron Lett., Vol.24,PP.356-358, Mar 1998. 7. I J Bahl P. Bhartia, S.Stuchly“ Design of microstrip antennas covered with a dielectric layer. IEEE Trans. Antennas Propogat. No. 30, PP. 314-318, Mar 1982 8. V.A. Dmitrier, J.C.W.A. Costa, “Theoritical investigation of compact microstripe resonators with stubs for patch antennas”. IEEE trans. Microwave theory Tech; 50, PP.27-29, 9. R.E. Collin , Antennas radial wave propagation. Newyork, McGraw-Hill 1985 10. V.Saidulu, “Design, simulation and experimental analysis on rectangular microstrip patch antenna with superstrates, IJEAT, vol.9, 2020

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