International Journal of Innovative Technology and Exploring Engineering
ISSN : 2278 - 3075 Website: www.ijitee.org Volume-8 Issue-8S3, JUNE 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication
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www.ijitee.org Exploring Innovation Editor-In-Chief Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE International Journal of Recent Technology and Engineering (IJRTE)
Associated Editor-In-Chief Chair Prof. MPS Chawla Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology & Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India
Dr. Vinod Kumar Singh Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India
Dr. Rachana Dubey Ph.D.(CSE), MTech(CSE), B.E(CSE) Professor & Head, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal (M.P.), India
Associated Editor-In-Chief Members Dr. Hai Shanker Hota Ph.D. (CSE), MCA, MSc (Mathematics) Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science ,Technology and Maritime Transport, Egypt
Dr. Mayank Singh PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-Natal, Durban, South Africa.
Scientific Editors Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Moinuddin Sarker Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor) Stamford, USA.
Prof. (Dr.) Nishakant Ojha Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India
Dr. Shanmugha Priya. Pon Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East Africa, Tanzania
Dr. Veronica Mc Gowan Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman, China.
Dr. Fadiya Samson Oluwaseun Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern Cyprus, Turkey.
Dr. Robert Brian Smith International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie Centre, North Ryde, New South Wales, Australia
Dr. Durgesh Mishra Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India
Executive Editor Chair Dr. Deepak Garg Professor & Head, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India
Executive Editor Members Dr. Vahid Nourani Professor, Faculty of Civil Engineering, University of Tabriz, Iran.
Dr. Saber Mohamed Abd-Allah Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.
Dr. Xiaoguang Yue Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.
Dr. Labib Francis Gergis Rofaiel Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology, Mansoura, Egypt.
Dr. Hugo A.F.A. Santos ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.
Dr. Sunandan Bhunia Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia (Bengal), India.
Dr. Awatif Mohammed Ali Elsiddieg Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan, Saudi Arabia.
Technical Program Committee Chair Dr. Mohd. Nazri Ismail Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.
Technical Program Committee Members Dr. Haw Su Cheng Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.
Dr. Hasan. A. M Al Dabbas Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.
Dr. Gabil Adilov Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.
Dr.Ch.V. Raghavendran Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.
Dr. Thanhtrung Dang Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineeering, HCMC University of Technology and Education, Hochiminh, Vietnam.
Dr. Wilson Udo Udofia Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.
Dr. Ch. Ravi Kumar Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra Pradesh), India.
Dr. Sanjay Pande MB FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR) Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University Belgaum (Karnataka), India.
Manager Chair Mr. Jitendra Kumar Sen Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India
Editorial Chair Prof. (Dr.) Rahul Malhotra Director – Principal, Department of Electronics & Communication, Swami Devi Dyal Institute of Engineering and Technology, Barwala (Haryana), India.
Editorial Members Dr. Wameedh Riyadh Abdul-Adheem Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq
Dr. T. Sheela Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Periyaseeragapadi (Tamil Nadu), India
Dr. Manavalan Ilakkuvan Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India
Dr. Shivanna S. Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India
Dr. H. Ravi Kumar Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India
Dr. Pratik Gite Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P), India
Dr. S. Murugan Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India
Dr. S. Brilly Sangeetha Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala), India
Dr. P. Malyadri Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India
Dr. K. Prabha Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India
Dr. Liladhar R. Rewatkar Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India
Dr. Raja Praveen.N Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India
Dr. Issa Atoum Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education University, Amman- Jordan
Dr. Balachander K Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi (Coimbatore), India
Dr. Sudhan M.B Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering, Anna University, (Tamilnadu), India
Dr. T. Velumani Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India
Dr. Subramanya.G.Bhagwath Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management Bhatkal (Karnataka), India
Dr. Mohan P. Thakre Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India
Dr. Umar Lawal Aliyu Lecturer, Department of Management, Texila American University Guyana USA.
S. Volume-8 Issue-8S3, June 2019, ISSN: 2278-3075 (Online) Page No Published By: Blue Eyes Intelligence Engineering & Sciences Publication No.
Authors: Divakar Harekal, Veena G.S., Ankit Goyal, Rahul Sinha Paper Title: Reaug an Implemented Augmented Reality enabled Scanner for Restaurants Abstract: In our research paper with the use of a well-executed augmented reality (AR) marker in which we are using the combined properties of QR code and AR to scan the QR code associated with the particular restaurant menu. Scanning will result in obtaining of 3-D images of all the dishes present in that restaurant along with the dish details like health constituents, servings, how a particular dish tastes like and many more details associated with that dish. In this way we are elevating the dine-in experience by removing the traditional menu ordering process. It is directed at eliminating the discomfort customers are facing due to food and language gaps. Visual representation of food will give unique experience and due to this wide range of customers will prefer to come to restaurants. So overall it will revolutionize the conventional ordering system which is followed in the restaurants.
1. Keywords: Reaug Overview, Augmented Reality, AI Scanner, Fast Scan AI Techniques. 1-4 References: 1. Zhihong Liu, and Yongtao Wang,"Halftone qr codes ACM Trans. Graph", pp. 6-10, 2004 2. Weibing Chen, Gaobo Yang, "A Simple and Efficient Image Pre-processing for QR Decoder", 2012. 3. S.Ramya and C. Sheeba Joice,"An Optimized Image and Data Embedding in Color QR Code", ACM-Gis, pp. 194203, 2005. 4. C.M. Ming, H.D. Ma, and Q.P. Zhao: “Journal of Computer-Aided Design & Computer Graphics”, [Accessed: 18- Mar- 2018]. 5. Kinjal H. Pandya, Hiren J. Galiyawala, “A Survey on QR Codes: in context of Research and Application”,[Accessed: 17- Mar-2018]. 6. Gonzalo Garateguy, Gonzalo R. Arce, “QR Images: Optimized Image Embedding in QR Codes”, [Accessed: 18- July- 2014]. 7. Omar Lopez-Rincon, Oleg Starostenko, Vicente Alarcon-Aquino, and Juan C. Galan-Hernandez, “Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments”,[Accessed: 18- August- 2016]. 8. Ways to Optimize Target Detection and Stability - Vuforia AR https://library.vuforia.com/articles/Solution/Optimizing-Target-Detection- and-Tracking-Stabilit. Authors: Deepali Singhal, Amit Doegar Paper Title: Face-Iris Multimodal Biometric System Using Feedforward Backpropagation Neural Network Abstract: Multimodal biometric systems are used to verify or identify people by utilizing information multiple biometric modality. It combines the advantages of a unimodal biometric system to address their limitations. An efficient Face-Iris multimodal Biometric system based on artificial intelligence technique is presented in this paper. The main goal of this article is to enhance the authentication performance by fusing two biometric traits such as face and iris modalities. A feature extraction algorithm Maximally Stable Extremal Regions (MSER) along with feature optimization technique Artificial Bee Colony (ABC) is used to extract the key points and optimized these key points respectively. To detect or match face and iris Feed forward back propagation neural network (FFBPNN) is used. Evaluating overall performance of the designed modal based on accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Error and Receiver Operating Characteristic (ROC) analysis suggests that the proposed multimodal biometric system achieves improved results compared to existing work.
Keywords: Multimodal biometric system, Iris-face, MSER, ABC, FFBPNN.
References: 1. Ortiz, N., Hernández, R. D., Jimenez, R., Mauledeoux, M., & Avilés, O. (2018). Survey of Biometric Pattern Recognition via Machine Learning Techniques. 2. Akhtar, Z., Hadid, A., Nixon, M., Tistarelli, M., Dugelay, J. L., & Marcel, S. (2018). Biometrics: In search of identity and security (Q & 2. A). IEEE MultiMedia. 3. Wayman, J., Jain, A., Maltoni, D., & Maio, D. (2005). An introduction to biometric 5-9 4. authentication systems. In Biometric Systems (pp. 1-20). Springer, London. 5. Jain, A. K., Dass, S. C., & Nandakumar, K. (2004). Soft biometric traits for personal recognition systems. In Biometric authentication (pp. 731-738). Springer, Berlin, Heidelberg. 6. Khoo, Y. H., Goi, B. M., Chai, T. Y., Lai, Y. L., & Jin, Z. (2018, June). Multimodal Biometrics System Using Feature-Level Fusion of Iris and Fingerprint. In Proceedings of the 2nd International Conference on Advances in Image Processing(pp. 6-10). ACM. 7. Walia, G. S., Singh, T., Singh, K., & Verma, N. (2019). Robust multimodal biometric system based on optimal score level fusion model. Expert Systems with Applications, 116, 364-376. 8. Ammour, B., Bouden, T., & Boubchir, L. (2018). Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis. IET Biometrics. 9. Bicego, M., Lagorio, A., Grosso, E., & Tistarelli, M. (2006, June). On the use of SIFT features for face authentication. In Computer Vision and Pattern Recognition Workshop, 2006. CVPRW'06. Conference on (pp. 35-35). IEEE. 10. Alvarado, M., Melin, P., Lopez, M., Mancilla, A., & Castillo, O. (2009, March). A hybrid approach with the wavelet transform, modular neural networks and fuzzy integrals for face and fingerprint recognition. In Hybrid Intelligent Models and Applications, 2009. HIMA'09. IEEE Workshop on (pp. 19-24). IEEE. 11. Chaudhary, S., & Nath, R. (2016). A robust multimodal biometric system integrating iris, face and fingerprint using multiple SVMs. International Journal of Advanced Research in Computer Science, 7(2). 12. Arulkumar, V., & Vivekanandan, P. (2018). An intelligent technique for uniquely recognising face and finger image using learning vector quantisation (LVQ)-based template key generation. International Journal of Biomedical Engineering and Technology, 26(3-4), 237-249. 13. Kaur, R., & Kaur, B. Secure Multimodal Biometric Recognition Based on Speech, Face and Fingerprint Using Feature Level Fusion Approach. Ammour, B., Bouden, T., & Boubchir, L. (2018). Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis. IET Biometrics, 7(5), 482-489. 3. Authors: D. Ratnagiri, G.Murali Paper Title: Retinal Blood Vessel Segmentation using Ant Colony System Abstract: Diabetic retinopathy infection spreading on the retina vessels along these lines and it loses blood circulation that causes the visual loss in the time, so early identification of diabetes anticipates visual impairment in over half of cases. The programmed division of corneal veins into two-grade corneal images could achieve early representation. In this paper, the ant-colony technique for programmed dividing retinal arteries is used in two improvements in the previous methodology. Finally, the second development is the application of special heuristic capabilities in the ant-colony method, completely dependent on the supposition of chance, rather than the old one recently used on Euclidean distance. In our own database is for everyone a solitary database, which has a very pathology for diabetes retinopathy and important fundus structures, which is still clarified for each image in a database that makes it attractive for planning and assessing estimations of the diabetic retinopathy by coloring fundus images, which are currently reachable and completely new for early identification.
Keywords: Diabetic retinopathy, Morphological process, SVM, Digital Image Processing, Machine learning
References: 1. K. Goatman, A. Charnley, L. Webster and S. Nussey, “Assessment of automated disease detection in diabetic retinopathy screening using twofield photography,” PLoS. One, vol. 6, no.12, pp. 275-284, 2011. 2. K. Verma, P. Deep and A.G. Ramakrishnan, “Detection and classification of diabetic retinopathy using retinal images,” Annual IEEE India Conference (INDICON), pp. 1-6, 2011, DOI: 10.1109/INDCON. 2011.6139346. 3. M. Foracchia, E. Grisan and A. Ruggeri, “Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images,” In Book abstracts of 2nd international workshop on computer assisted fundus image analysis, 2011. 4. V. Vijayakumari and N. Suriyanarayanan, “Survey on the detection methods of blood vessel in retinal images,” Eur. J. Sci. Res., vol. 68, no.1, pp. 83-92, 2012. 5. M.M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen and S.A. Barman, “Blood vessel segmentation methodologies in retinal images-a survey,” Comput. Methods Programs Biomed., vol. 108, no.1, pp.407-433, October 2012, doi: 6. 10.1016/j.cmpb.2012.03.009. 7. X. You, Q. Peng, Y. Yuan, Y Cheung and J. Lei, “Segmentation of retinal blood vessels using the radial projection and semi-supervised approach,” Pattern Recogn., vol. 441, pp. 2314–2324, 2011. 8. Ahmed. H. Asad, A. T. Azar, and A. E. Hassanien, “Integrated features based on gray-level and Hu moment invariants with ant colony 10-14 system for retinal blood vessels segmentation,” Int. J. Syst. Boil. Biomed. Tech., vol. 1, no. 4, pp. 61-74, 2012. 9. M. Dorigo and L.M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput. vol. 1, no. 1, pp. 53–66, 1997. 10. D. Marin, A. Aquino, ME. Gegundez-Arias and JM. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using grey-level and moment invariants-based features,” IEEE Trans. Med. Imaging, vol. 30, no. 1, pp. 146-158, 2011. 11. M.K. Hu, “Visual pattern Recognition by Moment Invariants,” IRE. Trans. Inform. Theory., vol. 8, no. 2, pp. 179–187, 1962. 12. M.A. Hall, “Correlation-based feature felection for discrete and numeric class machine learning,” ICML, pp. 359-366, 2000. 13. A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging , vol. 19, no. 3, pp. 203–210, Mar.2000. 14. J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever and B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501-509, 2004. 15. E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imaging, vol. 26, no. 10, pp. 1357–1365, Oct. 2007. 16. M. M. Fraz, S. A. Barman, P. Remagnino, A. Hoppe, A.Basit, B. Uyyanonvara, A. R. Rudnicka, and C.G. Owen, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Trans. Biomed. Eng., vol. 59, no. 9, pp. 1427–1435, Sep. 2012. 17. M. S. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng., vol. 58, no. 5, pp. 1183–1192, May 2011. 18. M. M. Fraz, S. A. Barman, P. Remagnino, A. Hoppe,A.Basit, B. Uyyanonvara, A. R. Rudnicka, and C.G. Owen, “An approach to localize the retinal blood vessels using bit planes and centerline detection,” Comput. Methods Programs Biomed., Sep. 2011. 19. P. Kelvin, H. Ghassan and A. Rafeef, “Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images”, in Proc. 10th Int. Conf. Med. Image Computing and Computer-Assisted Intervention, Springer-Verlag, Brisbane, Australia, 2007. 20. K.K. Delibasis, A.I. Kechriniotis, C. Tsonos and N. Assimakis, “Automatic model-based tracing algorithm for vessel segmentation and diameter estimation,” Comput. Method. Programs. Biomed., vol. 100, pp. 108–122, 2010. 21. M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput. Biol. Med., vol. 37, pp. 262–267, 2007. 22. B. Zhang, L. Zhang, L. Zhangb and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of Gaussian,” Comput. Biol. Med, vol. 40 , pp. 438–445, 2010. Authors: D. Bhavya Varma, P. Varaprasada Rao Paper Title: Sparse Representations of Blind Image Deblurring with Motion Abstract: Sparse illustration based blind picture de-blurring strategy abuses the sparsity property of normal images, by expecting that the “patches” from the characteristic images can sparsely spoken to by an over-total lexicon. By joining this prior into the de-blurring process, however reestablishing an unmistakable image from a “solitary motion-obscured image because of camera shake has for quite some time been one trying problem in 4. digital imaging. Existing blind de-blurring methods either just can evacuate basic motion blurring, or require user interactions to chip away at progressively complex cases”. In this study work examining to expel motion blurring 14-19 from a solitary image by planning the blind blurring as another joint improvement problem, which at the same time augments the sparsity of the unmistakable image under certain appropriate excess tight frame frameworks. Moreover, “the new sparsity limitations under tight frame frameworks empower the utilization of a quick calculation called linearized Bregman iteration to proficiently take care of the proposed minimization problem. The study is on both reproduced images and genuine images demonstrated that our calculations can adequately expelling complex motion blurring from nature images.
Keywords: Blind deblurring, Sparse representation, Non-Negative Matrix Approximation, Image restoration.
References: 1. Q. Shan, J. Jia, and A. Agarwala, “High-quality motion de-blurring from a single image,” ACM Transactions on Graphics, vol. 27, no. 3, pp. 73:1–73:10, Aug. 2008. 2. D. Krishnan, T. Tay, and R. Fergus, “Blind de-convolution using a normalized sparsity measure,” in IEEE CVPR, June 2011, pp. 233– 240. 3. L. Xu, S. Zheng, and J. Jia, “Unnatural l0 sparse representation for natural image de-blurring,” in IEEE CVPR, June 2013, pp. 1107– 1114. 4. W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M. H. Yang, “Image deblurring via enhanced low-rank prior,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3426–3437, July 2016. 5. Yuanchao Bai, Gene Cheung, Xianming Liu and Wen Gao, “Graph-Based Blind Image Deblurring from a Single Photograph”. 6. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” in ACM Transactions on Graphics, 2006, pp. 787–794. 7. Dejee Singh and Mr. R. K. Sahu, “A Survey on Various Image Deblurring Techniques”, International Journal of Advanced Research in Computer and Communication Engineering 8. J. Pan, Z. Hu, Z. Su, and M. H. Yang, “l0-regularized intensity and gradient prior for deblurring text images and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 2, pp. 342–355, Feb 2017. 9. Haichao Zhang and Yanning Zhang, “Sparse representation based iterative incremental image deblurring,” in ICIP, 2009. 10. Jianchao Yang, John Wright, Thomas Huang, and Yi Ma, “Image super resolution as sparse representation of raw image patches,” in CVPR, 2008. 11. Zhe Hu, Jia-Bin Huang, and Ming-Hsuan Yang, “Single image deblurring with adaptive dictionary learning,” in ICIP, 2010. 12. Mohammad Tofighi, Yuelong Li and Vishal Monga,”Blind Image Deblurring Using Row Column Sparse Representation”, IEEE Signal Processing Letters. 13. W. Zuo, D. Ren, D. Zhang, S. Gu, and L. Zhang, “Learning iterationwise generalized shrinkage-thresholding operators for blind deconvolution,” IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1751–1764, April 2016. 14. L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” in ECCV, 2010, pp. 157–170. 15. J. F. Cai, H. Ji, C. Liu, and Z. Shen, “Framelet-based blind motion deblurring from a single image,” IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 562–572, Feb 2012. 16. S. Cho and S. Lee, “Fast motion deblurring,” ACM Transactions on Graphics, vol. 28, no. 5, pp. article no. 145, 2009. 17. D. Perrone and P. Favaro, “A clearer picture of total variation blind deconvolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1041–1055, June 2016. 18. M. Tofighi, O. Yorulmaz, K. Kose, D. C. Yildirim, R. Cetin-Atalay, and A. E. Cetin, “Phase and TV based convex sets for blind deconvolution of microscopic images,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 1, pp. 81–91, Feb 2016. 19. T. F. Chan and Chiu-Kwong Wong, “Total variation blind de-convolution,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 370–375, Mar 1998. 20. S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind de-convolution using a total variation prior,” IEEE Transactions on Image Processing, vol. 18, no. 1, pp. 12–26, Jan 2009. 21. A. Ahmed, B. Recht, and J. Romberg, “Blind deconvolution using convex programming,” IEEE Transactions on Information Theory, vol. 60, no. 3, pp. 1711–1732, March 2014. 22. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. In SIGGRAPH, volume 25, pages 783–794, 2006. 23. Q. Shan, J. Jia, and A. Agarwala. High-quality motion deblurring from a single image. In SIGGRAPH, 2008. Authors: Amit Pratap Singh, Maitreyee Dutta Paper Title: Spam Detection in Social Networking Sites using Artificial Intelligence Technique Abstract: Social networks provide a way for users to remain in contact with their friends. The increasing popularity of social networks allows social site users to gather large amounts of individual information about their friends. Among numerous sites, Twitter is the fastest growing website. Its popularity has also attracted many spammers to use large amounts of spam to penetrate legitimate users' accounts. In this research work, the Spam detection system in social sites” is designed to detect the spammer by using a machine learning approach. Initially, data is collected from H-Spam14 site and then different pre-processing schemes such as to convert data into lowercase; stop word removal will be applied. After this, the data enters into the feature extraction phase, in which tokenization process is used to divide the entire sentence into a group of words and hence extract the best features from the raw data. To select an appropriate value of extracted feature set, Artificial Bee Colony (ABC) has been applied as an optimization algorithm to determine the optimal feature sets from spam as well as non-spam data. Then, the classification process has been performed using Artificial Neural network (ANN) to distinguish the spam 5. and non-spam data. At the end of the process, performance metrics and comparison will be performed between proposed and existing work to validate the proposed work. The proposed spam detection system can obtained higher accuracy precision, recall and F-measure compared to the existing classifiers such as naïve Bayes and 20-25 Support vector machine (SVM).
Keywords: Spam detection, Twitter, ABC and ANN.
References: 1. Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010, October). @ spam: the underground on 140 characters or less. In Proceedings of the 17th ACM conference on Computer and communications security (pp. 27-37). ACM. 2. Wang, A. H. (2010, June). Detecting spam bots in online social networking sites: a machine learning approach. In IFIP Annual Conference on Data and Applications Security and Privacy (pp. 335-342). Springer, Berlin, Heidelberg. 3. Wu, T., Liu, S., Zhang, J., & Xiang, Y. (2017, January). Twitter spam detection based on deep learning. In Proceedings of the Australasian Computer Science Week Multiconference (p. 3). ACM. 4. Zheng, X., Zeng, Z., Chen, Z., Yu, Y., & Rong, C. (2015). Detecting spammers on social networks. Neurocomputing, 159, 27-34. 5. Alsaleh, M., Alarifi, A., Al-Salman, A. M., Alfayez, M., & Almuhaysin, A. (2014, December). Tsd: Detecting sybil accounts in twitter. In 2014 13th International Conference on Machine Learning and Applications (pp. 463-469). IEEE. 6. Verma, M., & Sofat, S. (2014). Techniques to detect spammers in twitter-a survey. International Journal of Computer Applications, 85(10). 7. Wang, D., Irani, D., & Pu, C. (2011, September). A social-spam detection framework. In Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (pp. 46-54). ACM. 8. Cao, C., & Caverlee, J. (2015, March). Detecting spam urls in social media via behavioral analysis. In European Conference on Information Retrieval Springer, Cham, pp. 703-714. 9. Jain, G., Sharma, M., & Agarwal, B. (2018). Spam detection on social media using semantic convolutional neural network. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 12-26. 10. Ezpeleta, E., Iturbe, M., Garitano, I., de Mendizabal, I. V., & Zurutuza, U. (2018, June). A Mood Analysis on Youtube Comments and a Method for Improved Social Spam Detection. In International Conference on Hybrid Artificial Intelligence Systems Springer, Cham, pp. 514-525. 11. Dwyer, C., Hiltz, S., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. AMCIS 2007 proceedings, 339. 12. Dutta, S., Ghatak, S., Dey, R., Das, A. K., & Ghosh, S. (2018). Attribute selection for improving spam classification in online social networks: a rough set theory-based approach. Social Network Analysis and Mining, 8(1), 7. 13. Ala’M, A. Z., Faris, H., & Hassonah, M. A. (2018). Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts. Knowledge-Based Systems, 153, 91-104. 14. Aslan, Ç. B., Sağlam, R. B., & Li, S. (2018). Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example. 15. Sedhai, S., & Sun, A. (2018). Semi-supervised spam detection in the Twitter stream. IEEE Transactions on Computational Social Systems, 5(1), 169-175. 16. http://shodhganga.inflibnet.ac.in/bitstream/10603/183741/7/07_chapter%201.pdf Authors: Mohit Angurala, Manju Bala, Sukhvinder Singh Bamber Use of Energy Replenishment Model to Find Optimum Radio Propagation Model in Wireless Sensor Paper Title: Networks Abstract: Unreliability may occur during radio transmissions in Wireless Sensor Networks (WSNs) owing to the rigid constraints on battery power when wireless sensors hold low power radio transceivers. As a result, propagation patterns fluctuates because of which connections becomes unstable. Consequently, a precise radio propagation model is always pivotal for superior communication. Therefore, a Joint Energy Recharging with Load Balancing (J-ERLB) is implemented in WSNs to diversify the lifetime of the network. Moreover, this model is implemented in diverse propagation models to find optimum model for communication. Our numerical findings illustrate the comparison between Free Space Model, Shadowing Model and the Two-Ray Ground. The effectiveness of the proposed model is performed using the NS2 simulator.
Keywords: AODV, Load Balancing, Radio Propagation Model, WSNs.
References: 1. D. Kumar, “Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks,” IET Wireless Sensor Systems, 2013. 2. C. Chen, S.C. Mukhopadhyay and C. Chuang, “Efficient Coverage and Connectivity Preservation with Load Balance for Wireless Sensor Networks”, IEEE Sensors Journal, 2014. 3. M. Zhao, J. Li, and Yuanyuan Yang, “A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks,” IEEE Transactions on Mobile Computing, vol. 13, No. 12, December 2014. 4. M. A. Abd, S. F. Majed, B. K. Singh, K. E. Tepe and R. Benlamri, “Extending Wireless Sensor Network Lifetime with Global Energy Balance,” IEEE Sensors Journal, 2015. 5. V. Pal, G. Singh and R. Yadav, “Balanced Cluster Size Solution to Extend Lifetime of Wireless Sensor Networks,” IEEE Internet of Things Journal, 2015. 6. 6. C. Tunca, S. Isik, M. Donmez and C. Ersoy, “Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink,” IEEE Transactions on Mobile Computing, 2015. 7. P. Kumar, A. Chaturvedi, “Spatio-temporal Probabilistic Query Generation Model and Sink Attributes for Energy Efficient Wireless 26-32 Sensor Networks,” IET Networks, 2016. 8. J. Lee and T. Kao, “ An Improved Three-Layer Low-Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks,” IEEE Internet of Things Journal, 2016. 9. R. Labisha and E. Baburaj, “Efficient approach to maximize WSN lifetime using weighted optimum storage-node placement, efficient and energetic wireless recharging, efficient rule-based node rotation and critical-state-data-passing methods,” IET Networks, IEEE, 2016. 10. I. Azam1, N. Javaid1, and A. Ahmad, “Balanced Load Distribution with Energy Hole Avoidance in Underwater WSNs,” IEEE ACCESS, IEEE, 2017. 11. Punyasha Chatterjee, Sasthi C. Ghosh and Nabanita Das, “Load Balanced Coverage with Graded Node Deployment in Wireless Sensor Networks,” IEEE Transactions on Multi-Scale Computing Systems, 2017. 12. N. Jan, N. Javaid, Q. Javaid, and N. Alrajeh., “A balanced energy consuming and hole alleviating algorithm for wireless sensor networks,” IEEE ACESS, 2017. 13. X. Liu, N. Javaid, and P. Zhang, “Data Drainage: A Novel Load Balancing Strategy for Wireless Sensor Networks,”IEEE Communications Letters, 2017. 14. X. Rao_, P. Yang, Y. Yan, H. Zhouy, X. Wuz., “Optimal Recharging with Practical Considerations in Wireless Rechargeable Sensor Network,”IEEE ACESS, 2017. 15. D. Arivudainambi, and S. Balaji., “Optimal Placement of Wireless Chargers in Rechargeable Sensor Networks,”IEEE Sensors Journal, 2018. 16. M. Dhurgadevi, and P. Meenakshi Devi, “An Analysis of Energy Efficiency Improvement Through Wireless Energy Transfer in Wireless Sensor Network, “Wireless Personal Communications, Volume 98, Issue 4, pp 3377–3391, 2018. 17. M. Bakshi, A. Ray, and D. De, “Maximizing lifetime and coverage for minimum energy wireless sensor network using corona based sensor deployment,” CSI Transactions on ICT, Volume 5, Issue 1, pp 17–25, 2017. 18. S. Chauhan, and M. M. Gore, “Balancing energy consumption across network for maximizing lifetime in cluster-based wireless sensor network,” CSI Transactions on ICT, Volume 3, Issue 2–4, pp 83–90, 2015. 19. S. Raj Barath, and C. Kezi Selva Vijla, “Mitigating Packet Drop and Energy Consumption in Wireless Sensor Networks Using Network Level Optimized Relay Election Protocol,” Wireless Personal Communications, Volume 94, Issue 4, pp 2783–2796, 2017. 20. R. Arya, and S. C. Sharma, “Energy optimization of energy aware routing protocol and bandwidth assessment for wireless sensor network,” International Journal of System Assurance Engineering and Management, Volume 9, Issue 3, pp 612–619, 2018. 21. S. Cui, Y. Cao, G. Sun, and S. Bin, “A new energy-aware wireless sensor network evolution model based on complex network,” EURASIP Journal on Wireless Communications and Networking, 2018. 22. K. Hiraga, K. Sakamoto, M. Arai, T. Seki, H. Toshinaga, T. Nakagawa, and Kazuhiro Uehara, “Dependency on Beamwidth in an SD Method Utilizing Two-Ray Fading Characteristics, “IEEE antennas and wireless propagation letters, VOL. 14, 2015. 23. M. Chiou, and J. Kiang, “Simulation of X-band Signals in a Sand and Dust Storm with Parabolic Wave Equation Method and Two-Ray Model, “IEEE antennas and wireless propagation letters, VOL. 14, 2016. 24. T. Olasupo, C. Otero, K. Olasupo, Ivica Kostanic, “Empirical Path Loss Models for Wireless Sensor Network Deployments in Short and Tall Natural Grass Environments, “IEEE Transactions on Antennas & Propagation, 2016. 25. Hsueh-Wen Tseng, Member, IEEE, Ruei-Yu Wu, and Yi-Zhang Wu, “An Efficient Cross-Layer Reliable Retransmission Scheme for the Human Body Shadowing in IEEE 802.15.6-Based Wireless Body Area Networks,” IEEE Sensors Journal, 2016. 26. M. Cheffena, and M. Mohamed, “Empirical Path Loss Models for Wireless Sensor Network Deployment in Snowy Environments,” IEEE antennas and wireless propagation letters, VOL. 16, 2017. 27. S. Kurt And B. Tavli, “Path-Loss Modeling For Wireless Sensor Networks,” IEEE Antennas Propagation Magazine, 2017. 28. H. Wu, L. Zhang, and Y. Miao,” The Propagation Characteristics of Radio Frequency Signals for Wireless Sensor Networks in Large- Scale Farmland,” Wireless Personal Communications, Volume 95, Issue 4, pp 3653–3670, 2017. 29. V. Gupta, and Brahmjit Singh, “Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing,” International Journal of Information Technology, 2018. 30. M. Uddin, “Throughput analysis of a CSMA based WLAN with successive interference cancellation under Rayleigh fading and shadowing,” Wireless Networks, Volume 22, Issue 4, pp 1285–1298, 2016. Authors: Vivek Deshpande, Vladimir Poulkov, Dattatray Waghole Paper Title: Adaptive Range Control Scheme to improve QoS for WSNs Abstract: In wireless sensor networks (WSNs), economical power utilization is crucial analysis drawback since from last twenty years. The WSNs are cluster of energy strained and little sensing element nodes. Since from the emerge of WSN mechanism, researchers conferred numerous ways to enhance the network period of time supported completely different layers like routing protocol, mac (Medium Access Control) protocols etc. except for this the transmission range parameter that is by default fixed in WSNs also can facilitate to reduce the overall power consumption. In this paper, we proposed Adaptively Transmission Range & Minimization of Energy (ATREM) algorithm utilization of the energy resources so as to extend the network period of time of WSNs. The planned algorithm designed to estimate the minimum transmission power for current link for data transmissions. In ATREM, the transmission range is computed at every interval for every sensing node and thus the transmission range of each sensing node is completely different because it is computed based on the 30 nodes network topology. We have a tendency to exploited the network connection parameter for dynamically adjust the transmission range of sensing nodes. The simulation results shows that proposed solution minimizing the energy consumptions and improvement of the network QoS (Quality of Service) performances.
Keywords: Adaptive power control, Energy consumption, Network lifetime, QoS, Transmission Range, Sensor nodes.
References: 1. N. M. Khan, Z. Khalid, G. Ahmed, and M. Yasin, “A robust routing strategy for wireless sensor networks,” in Proc. IEEE International Conference on Electrical Engg. (ICEE), Lahore, Pakistan, April 2007, pp. 1–5. 2. M. D. F. C. Alippi, G. Anastasi and M. Roveri, “Energy management in wireless sensor networks with energy-hungry sensors,” IEEE Instru- mentation and Measurement Magazine, 2009. 3. Cagalj, M., Hubaux, J.-P., & Enz, C. C. (2005). “Energy-efficient broadcasting in all-wireless networks”. Wireless Networks, 11(1/2), 7. 177–188. 4. Polastre, J., Szewczyk, R., & Culler, D. (2005). Telos: Enabling ultra-low power wireless research. In Proceedings of international symposium on information processing in sensor networks (pp. 364–369). 33-37 5. Chen, Y. P., Wang, D.,& Zhang, J. (2006). Variable-base tacitcommunication: a new energy efficient communication scheme for sensor networks. IEEE. 6. S. Lin, J. Zhang, G. Zhou, L. Gu, J. A. Stankovic, and T. He, “Atpc: adaptive transmission power control for wireless sensor networks,” in Proc. ACM Sensys. Boulder, Colorado: ACM, November 2006. 7. O. Chipara, Z. He, G. Xing, Q. Chen, X. Wang, C. Lu, J. Stankovic, and T. Abdelzaher, “Real-time power aware routing in wireless sensor networks,” Tech. Rep. WUSEAS-2005-31, July 2005. 8. Ghufran Ahmed , Noor M Khan , Mirza M Yasir Masood, “A Dynamic Transmission Power Control Routing Protocol to Avoid Network Partitioning in Wireless Sensor Networks”, 2011 International Conference on Information and Communication Technologies. 9. Delia Ciullo; Guner D. Celik; Eytan Modiano, “Minimizing Transmission Energy in Sensor Networks via Trajectory Control”, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks 10. Michele Chincoli,* ID and Antonio Liotta, “Self-Learning Power Control in Wireless Sensor Networks”, Sensors, MDPI, 2018. 11. Le, T.T.T.; Moh, S. An Energy-Efficient Topology Control Algorithm Based on Reinforcement Learning for Wireless Sensor Networks. Int. J. Control Autom. 2017, 10, 233–244. 12. Yau, K.L.A.; Goh, H.G.; Chieng, D.; Kwong, K.H. Application of reinforcement learning to wireless sensor networks: Models and algorithms. Computing 2015, 97, 1045–1075. 13. Chincoli, M.; Syed, A.A.; Exarchakos, G.; Liotta, A. Power Control inWireless Sensor Networks with Variable Interference. Mob. Inf. Syst. 2016, 2016, 1–10. 14. Xiaoping Yang, Xueying Chen, Riting Xia and Zhihong Qian, “Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID”, Sensors, MDPI, 2018. 15. Yau, K.L.A.; Goh, H.G.; Chieng, D.; Kwong, K.H. Application of reinforcement learning to wireless sensor networks: Models and algorithms. Computing 2015, 97, 1045–1075. 16. Chincoli, M.; Syed, A.A.; Exarchakos, G.; Liotta, A. Power Control inWireless Sensor Networks with Variable Interference. Mob. Inf. Syst. 2016, 2016, 1–10.s 17. Zahra Rezaei , Shima Mobininejad ,” Energy Saving in Wireless Sensor Networks ”, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.1, February 2012. 18. M. A. Ameen, S.M.R. Islam, and K. Kwak. Energy Saving mechanisms for mac protocols in wireless Sensor networks. International Journal of Distributed Sensor Networks, 2010, 2010. 19. J.Sinha and S.Barman, “Energy efficient routing mechanism in wireless sensor network”, In Recent Advances in Information Technology (RAIT), 2012 1st International Conference on, pages 300 –305, march 2012. 20. R.Soua and P.Minet, “A survey on energy efficient techniques in wireless sensor networks”, In Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP, pages 1 –9, oct. 2011. 21. Ajinkya Nanavati, Vivek S Deshpande, “Performance Analysis of wireless sensor network for propogation range”, IEEE International Conference on Pervasive Computing (ICPC), pp 19-24, Pune, 2015 1. S.B.G.Tilak Babu, K.H.K.Prasad, Jyothirmai Gandeti, Devi Bhavani Kadali, V.Satyanarayana, Authors: K.Pavani Paper Title: Image Fusion using Eigen Features and Stationary Wavelet Transform Abstract: Image fusion is a technique of fusing multiple images for better information and more accurate image compared source images. The applications of image fusion in modern military, multi-focus image integration, pattern recognition, remote sensing, biomedical imaging etc.In this paper discussed, pros and cons of various newly arrived existing techniques in spatial and transform domain image fusion techniques. The individual advantages of Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) is become great advantage to the proposed method.Standard dataset is used to evaluate the performance of proposed method, the obtained results are compared with exiting methodologies and shows robustness in terms of entropy, standard deviation and Peak Signal to Noise Ratio (PSNR).
Keywords: Fusion, multi-focus image integration, SWT, PCA, PSNR, standard deviation.
References: 1. P. K. Varshney, "Multisensor data fusion," in Electronics & Communication Engineering Journal, vol. 9, no. 6, pp. 245-253, Dec 1997. 2. Deepak Kumar Sahu, M.P.Parsai, “Different Image Fusion Techniques –A Critical Review”International Journal of Modern Engineering Research (IJMER), Vol. 2, Issue 5,pp 4298-4301, Sep-Oct 2012. 3. Shrivsubramani, Krishnamoorthy, K P Soman,“ Implementation and Comparative Study of Image Fusion Algorithms” .International Journal of Computer Applications (0975 – 8887) Volume 9– No.2, November 2010. 4. V.P.S. Naidu, J.R. Raol, “Pixel-level Image Fusion using Wavelets and Principal Component Analysis”. Defence Science Journal, Vol. 58, No. 3, May 2008, pp. 338 -352, 2008. 8. 5. Jiya ma, Chen Chen, Chang Le, Jun Huang, “Infrared and visible image fusion via gradient transfer and total variation minimization” Information Fusion, Elsevier, pp. 100–109, 2016. 6. Yanfei Chen, Nong Sang , “Attention-based hierarchical fusion of visible and infrared images”, Volume 126, Issue 23, PP 4243–4248, 38-40 August 2015. 7. Xiaosong Li , Huafeng Li , Zhengtao Yu , Yingchun Kong, “Multifocus image fusion scheme based on the multiscale curvature in nonsubsampledcontourlet transform domain”, Volume 54, Issue 7, Imaging Components, Systems, and Processing, Jul 30, 2015. 8. Jun Lang,ZhengchaoHao, “Image fusion method based on adaptive pulse coupled neural network in the discrete fractional random transform domain”,International Journal for Light and Electron Optics, Elesiver, Volume 126, Issue 23, Pages 3644–365, December 2015. 9. Keith A. Johnson, J. Alex Becker, “ http://www.med.harvard.edu/aanlib/home.html”, The whole brain atlas data set, image database1. 10. SlavicaSavic, “http://dsp.etfbl.net/mif/”, Image database2. 11. Q.Guihong, Z. Dali, and Y.Pingfan, “Medical image fusion by wavelet transform modulus maxima,” Opt. Express, vol. 9, pp. 184– 190,2001. 12. Y.Yang, D. S. Park, S.Huang, and N. Rao, “Medical image fusion via an effective wavelet based approach,” EURASIP J. Adv. Signal Process., pp. 44-1–44-13, 2010. 13. S.B.G.Tilak Babu, V.Satyanarayana, Ch.Srinivasarao, “Shift Invarient And Eigen Feature Based Image Fusion,”International Journal on Cybernetics & Informatics (IJCI) Vol. 5, No. 4, August 2016. 14. J. Wang, D. Xu, C. lang, B. Li, “Exposure fusion based on shift-Invariant discrete wavelet transform”, Journal of information Science and engineering, vol. 27, pp. 197-211, 2011. 15. Y. Zhou, K. Gao, Z. Dou, Z. Hua and H. Wang, "Target-Aware Fusion of Infrared and Visible Images," in IEEE Access, vol. 6, pp. 79039-79049, 2018. doi: 10.1109/ACCESS.2018.2870393. 16. X. Han et al., "An Adaptive Two-Scale Image Fusion of Visible and Infrared Images," in IEEE Access, vol. 7, pp. 56341-56352, 2019. doi: 10.1109/ACCESS.2019.2913289. 17. Z. Pan and H. Shen, "Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration," in IEEE Transactions on Image Processing, vol. 28, no.4,pp.1783-1797,April 2019.doi: 10.1109/TIP.2018.2881911. Authors: Charu Wahi Secured AODV to prevent Single and Collaborative Black Hole Attack in MANETs Paper Title: Abstract: This paper addresses one of the most important security attacks against routing in Mobile Ad Hoc Networks – Black hole attack. We propose an algorithm called Secured AODV (SAODV) to reduce the susceptibility of AODV routing protocol against Black hole attack. The proposed algorithm combats the effect of both single and collaborative Black hole attack. Essentially it requires the intermediate node to validate the Sequence Numbers and Speed of node (which replies with a route to the destination) based on well-defined 9. thresholds prior to instantiating a communication with destination to send data packets; without imposing any additional overhead on source and destination nodes. A very small number of solutions exist that can collectively prevent both single and collaborative black hole 41-48 attacks. Moreover, those who do, have high amount of overhead and delay. Simulation using NS-2 shows that SAODV provides better security against single and collaborative blackhole attacks and also better performance in terms of Packet delivery ratio and Throughput against original AODV in presence of Blackhole nodes. In comparison to AODV, there is an improvement in PDR by 60-80% and throughput increases by 70-80% when malicious nodes are present in the network; with a marginal increase in delay and overhead.
Keywords: MANET, AODV, SAODV, Black-Hole, Single, Collaborative
References: 1. Nital Mistry, Devesh C Jinwala and Mukesh Zaveri, “Improving AODV Protocol against Blackhole attacks”, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 2, March 2010, Hong Kong. 2. C. E. Perkins, E. M. B. Royer and S. R. Das, “Ad-hoc On-Demand Distance Vector (AODV) Routing,” Mobile Ad-hoc Networking Working Group, Internet Draft, draft-ietf-manetaodv- 00.txt, Feb. 2003. 3. C. Wahi, S.K. Sonbhadra, S. Chakraverty and V. Bhattacharya, “Effect of scalability and mobility on On-Demand routing protocols in a Mobile Ad-Hoc network”, International Conference on Software and Computing Technology (ICSCT 2012), 21-22nd Dec’2012, Malaysia. 4. Yaser khamayseh, Abdulraheem Bader, Wail Mardini, and Muneer BaniYasein, “A New Protocol for Detecting Black Hole Nodes in Ad Hoc Networks”, International Journal of Communication Networks and Information Security, Vol.3, No.1, pp-36-47, 2011. 5. H. Deng, W. Li, and D. P. Agrawal, “Routing security in wireless ad hoc networks”, IEEE Communications Magazine, 40(10), pp. 70-75. doi:10.1109/MCOM.2002.1039859, 2002. 6. M. Al-Shurman, S-M. Yoo, and S. Park, “BlackHole Attack in Mobile Ad Hoc Networks,” ACM Southeast Regional Conf. 2004. 7. Tamilselvan, Latha and Sankaranarayanan, V. (2007). “Prevention of Blackhole Attack in MANET”, The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (Aus Wireless 2007) India, 2007 IEEE. 8. Sanjay Ramaswamy, Huirong Fu, and Kendall E. Nygard, “Simulation Study of Multiple Black Holes Attack on Mobile Ad Hoc Networks,” International Conference on Wireless Networks (ICWN’ 05), Las Vegas, Nevada, Jun. 2005. 9. Seryvuth Tan and Keecheon Kim, “Secure Route Discovery for Preventing Black Hole Attacks on AODV-base MANETs”, ICTC 2013. 10. Tamilselvan, Latha and Sankaranarayanan, V. (2008). ”Prevention of cooperative black hole attack in MANET”, in Journal of Networks, Vol. 3, NO. 5, MAY 2008. 11. Sanjay K. Dhurandher, Isaac Woungang, Raveena Mathur and Prashant Khurana, “GAODV: A Modified AODV against single and collaborative Black Hole attacks in MANETs”, 27th International Conference on Advanced Information Networking and Applications Workshops, 2013. 12. Issariyakul, T., Hossain, E.: “Introduction to network simulation ns2,” July 2008. Authors: Ruchi Singh Total Factor Productivity in Manufacturing Sector of Bihar: With a Special Reference to tobacco Paper Title: Industry Abstract: India has an impressive and progressive profile in the global tobacco industry and it is an important commercial crop grown here. India is the second-largest tobacco producer and exporter in the world. Total exports of manufactured and unmanufactured tobacco stood at US$ 934.23 million in 2017-18 and US$ 564.28 million between Apr-Oct 2018. Indian tobacco is exported to about 100 countries. The development of manufacturing sector of an economy is an indicator of the economic strength of that country by raising productivity, employment generation along with supporting various other sectors of the economy. This study covers the duration of 1998-99 to 2012-13. Depending upon the availability of data the latest years have been added. This study is proposed to cover the Tobacco industry in state Bihar indicated by Annual Survey of Industries. Malmquist, Tornquist and Solow Index technique have been used to calculate TFP in tobacco industry of state Bihar, India. The productivity trend of tobacco industry in state Bihar had shown a steady growth pattern. TFP was maximum in year 2007-08 and was lowest in year 2006-07. TFP growth rate had shown a steady growth pattern from 2001-02 to 2005-06 and 2008-09 to 2011 with 2 major downfalls in TFP during 2006-07 and 2012-13.
Keywords: Tobacco Industry, Manufacturing.
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Saikia, Dilip (2014), “Total Factor Productivity in Agriculture: A Review of Measurement Issues in The Indian Context”, Romanian Journal of Regional Science, Vol.8 No. 2, Winter. 17. Manonmani, M. (2014), “Total Factor Productivity of Indian Corporate Manufacturing Sector”, The Indian, Journal of Industrial Relations, Vol. 49, No. 3, January. 18. Kumar, Sandeep & Kavita (2012), “Productivity and Growth in Indian Manufacturing Sector Since 1984-85 To 2004-05s: An Analysis of Southern Region States” Zenith International Journal of Business Economics & Management Research Vol.2, Issue 4, April, ISSN 2249 8826 Online Available at Http://Zenithresearch.Org.In/ 19. Annual Survey of Industries, Government of India, Ministry of Statistics and Programme, Implementation Central Statistics Office (Industrial Statistics Wing) Kolkata. 20. Office of the Economic Advisor, Ministry of Commerce & Industry, Government of India. 21. National Industrial Classification [All Economic Activities (2008), Central Statistical Organisation Ministry of Statistics and Programme Implementation Government of India New Delhi India. 22. https://www.ibef.org/states/bihar-presentation 23. https://www.ibef.org/states/Bihar.aspx 24. https://economictimes.indiatimes.com/news/politics-and-nation/bihar-economic-survey-report-tabled-in-the- house/articleshow/51141272.cms 25. Nandi, Arindam, Ashvin Ashok, G Emmanuel Guindon, Frank J Chaloupka and Prabhat Jha “Estimates of the economic contributions of the bidi manufacturing industry in India” Research paper Nandi A, et al. Tob Control 2014;0:1–8. doi:10.1136/tobaccocontrol-2013- 051404 26. Investment climate in Bihar, IBEF, An initiative of the Ministry of Commerce & Industry, Government of India 27. Department of Commercial Taxes, GOB. 28. Ahluwalia, I.J. (1985), “Industrial Growth in India - Stagnation since the Mid-Sixties." Oxford University Press, Delhi. 29. Ahluwalia, I.J. (1991), "Productivity and Growth in Indian Manufacturing." Oxford University Press, New Delhi. 30. Annual Report (2015-2016), Government of India Ministry of Commerce and Industry Department of Industrial Policy & Promotion. 31. Annual Survey of Industries, Government of India, Ministry of Statistics and Programme, Implementation. 32. Bairagya, Indrajit (2011), “Distinction between informal and unorganized sector: A study of total factor productivity growth for manufacturing sector in India”, journal of economics and behavioural studies, vol3 no.5, pp 296-310, (ISSN: 2220-6140) 33. Balakrishnan, P. & Pushpangada (1995), “Total Factor Productivity Growth in Manufacturing Industry” Economic and Political Weekly March 4. 34. Bulet unel (2003), "Productivity trends in India's manufacturing sectors in the last two decades", IMF Working paper, Asia and Pacific Department. 35. Christos J., Pantzios Giannis Karagiannis and Vangelis Tzouvelekas (2010), "Parametric decomposition of the input-oriented Malmquist productivity index: with an application to Greek aquaculture", Published online: 25 Deceniber, Springer. 36. Coelli, T.J., D.P. Rao, C.J. O'Donnell and G.E. Battese (2005.), "An Introduction to Efficiency and Productivity Analysis", Springer. 37. Central Statistics Office (Industrial Statistics Wing) Kolkata. 38. Crafts, Nicholas (2008), “Solow and Growth Accounting: A Perspective from Quantitative Economic History” (University of Warwick) Robert Solow and the Development of Growth Economics, Duke University 39. Das, D.K. (2001), "Trade Liberalization and Industrial Productivity: An Assessment of Developing Country Experiences". April, Working Paper 77, CRIER, New Delhi Diewert, W. Erwin & Alice O. Nakamura (2004),” Concepts and Measures of Productivity: An Introduction” Chapter 2 In Lipsey and Nakamura (Eds.), Services Industries and the Knowledge Based Economy, University of Calgary Press. 40. Goldar, Bishwanath., Suresh Aggarwal, Deb Kusum Das, Abdul A Erumban and Pilu Chandra Da (2016), “Productivity Growth and Levels - A comparison of Formal and Informal Manufacturing in India” Paper presented at the Fourth World KLEMS Conference to be held at the BBVA Foundation, Madrid, Spain, on May 23-24. 41. Handbook of Industrial Policy and Statistics 2008-09. 42. Industrial Development Report (2016), The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development, United Nations Industrial Development Organization. 43. Ibef (A Trust Established by the Department of Commerce, Ministry of Commerce and Industry, Government of India.) India Brand Equity Foundation Report on Indian Manufacturing: Overview and Prospects. 44. John RM, Sung HY, Max W. Economic cost of tobacco use in India, 2004. Tobacco Control, 2009;18:138-143. 45. Kaur S. Tobacco cultivation in India: time to search for alternatives. In: Efroymson D, FitzGerald S, eds. Tobacco and Poverty: Observations from India and Bangladesh. PATH Canada; 2002:15-20. 46. Office of the Economic Advisor, Ministry of Commerce & Industry, Government of India. 47. Reddy, S.K. & Gupta P.C. “ Report on Tobacco Control in India”, Ministry of Health and Family Welfare. New Delhi: Government of India;2004. 48. Report presented at Parliament of India, Rajya Sabha, (2016), Report 130 49. UNIDO “Industrial Development in the era of Globalization: Competitiveness and South- South Cooperation”, G-77 and China South – South conference on Trade, Investment and Finance, San Jose, Costa Rica, 13-15 Jan 1997. 50. https://www.ibef.org/states/Bihar.aspx Authors: Rudra Malali, Naman Jangid, Pranjali Satish Deshmukh, Halgaonkar Prasad S. Paper Title: Bluetooth Automatic Attendance Management using Android Application Abstract: The day-to-day Attendance and its maintenance has become a big problem statement that needs to be solved with an effective but still affordable and portable system. The attendance system currently present in institutes is based on manual methods or RFID, Wi-Fi, Face recognition, etc which has proven to be either time consuming or expensive with complex implementation. While the same is achieved in this paper using Bluetooth system auto attendance management. Hereby proposed method marks attendance by authenticated Bluetooth addresses and also checks for the false attendance, making it reliable. 11.
Keywords: Bluetooth, Attendance, SQLite, Bluetooth Adapter . 54-56
References: 1. Ignace T. Toudjeu and Prosper Z. Sotenga “Design and Implementation of an RFID Based Smart Attendance Register “, J IEEE Africon 2017 Proceedings, pp. 748–751. 2. Shouhao Geng, Guangming Li*, Wei Liu “Design and Implement of Attendance Management System Based on Contactless Smart IC Card”, 2012 International Conference on Computer Science and Electronics Engineering IEEE computer society, pp. 290 - 294. 3. Agus Bejo, Ricky Winata, Sri Suning Kusumawardani, “Prototyping of Class-Attendance System Using Mifare 1K Smart Card and Raspberry Pi 3”, IEEE 2018, pp. 1- 4 4. Zhongyun Jiang, “Analysis of Student Activities Trajectory and Design of attendance management based on Internet of Things”, ICALIP 2016, pp. 600–603. 5. Benfano Soewito, Ford Lumban Gaol, Echo Simanjuntak, Fergyanto E. Gunawan, “Smart Mobile Attendance System Using Voice Recognition and Fingerprint on Smartphone’, 2016 International Seminar on Intelligent Technology and Its Application, pp. 175–180. 6. Refik Samet, Muhammed Tanriverdi, “Face Recognition-Based Mobile Automatic Classroom Attendance Management System”, 2017 International Conference on Cyberworlds, pp. 253 - 256. 7. Shubhobrata Bhattacharya, Gowtham Sandeep Nainala, Prosenjit Das and Aurobinda Routray, “Smart Attendance Monitoring System (SAMS): A Face Recognition based Attendance System for Classroom Environment”, 2018 IEEE 18th International Conference on Advanced Learning Technologies, pp. 358- 360. 8. Prasad S Halgaonkar, Atul B Kathole, Jubber S Nadaf, K P Tambe, “Providing Security in Vehicular Adhoc Network using Cloud Computing by secure key Method”, 2018 International Conference on Information, Communication, Engineering and Technology (ICICET),IEEE. 9. Jubber S Nadaf, Prasad S Halgaonkar, Atul B Kathole, “Study and Implementation of Routing Protocols by using Security Method”, 2018 International Conference on Information, Communication, Engineering and Technology (ICICET),IEEE. 10. A B Kathole , “Optimization of Vehicular Adhoc Network Using Cloud Computing”, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing,IEEE. 11. Mr.Atul B.Kathole, Prof.Yogdhar Pande,“Performance analysis of sybil attack”, IJRISE, A National Conference on Recent Trends and developments in Engineering & Technology. Techno-Xtreme 16 Date of Conference 29th March 2016. Poonam M Bhagat, Prasad S Halgaonkar, Vijay M Wadhai, Comparison of LTE and WiMAX on the Basis of Qualities, International Journal of P2P Network Trends and Technology,2011. Authors: N. B. Totla, C. L. Prabhune, N. K. Sane CFD Simulation of In Cylinder Gases of Multi-cylinder Diesel Engine for Estimation of Liner Paper Title: Temperature from Gas Side Abstract: The simulation /estimation of cylinder temperature for power stroke of internal combustion engine along with liner temperature from gas side is pertinent/significant/essential to investigate the thermal distortion of a liner at different points along its longitudinal direction from coolant side. The present research/study includes estimation/finding of approximate temperature of in cylinder gases during power stroke and thereby liner temperature from gas side using water, and ethylene glycol as a coolant for present diesel engine. The present study includes numerical simulations based on Inbuilt ICE Combustion model in ANSYS 15.0 version where dynamic/time transient meshing of combustion space above piston during power stroke is used. Appropriate averaged boundary conditions were set on different surfaces for the combustion model. The variation of temperature of cylinder combustion gases and temperature of liner along gas side at different crank angle is reported. Observations are done that the highest combustion gas temperature occurred during power stroke was about 2150 K and minimum temperature is found to be 800 K. Also the maximum temperature on liner from gas side along stroke was found to be 470K during power stroke. It has been also found that the maximum temperature of in cylinder gases and liner from gas side persists only during early power stroke.
Keywords: Combustion, liner, grid, dynamic mesh, simulation.
References: 1. Carmen C. Barrios, Aida Domínguez-Sáez, DévoraHormigo, Influence of hydrogen addition on combustion characteristics and particle number and size distribution emissions of a TDI Diesel Engine Fuel, 199 (2017) 162 – 168. 12. 2. Taguchi optimization method”Materials and Design vol 27, March 2005 pp 853-861Fanos Christodoulou, Athanasios Megaritis, Experimental investigation of the effects of simultaneous hydrogen and nitrogen addition on the emissions and combustion of a diesel engine, International Journal of Hydrogen Energy. 39 (2014), 2692 – 2702. 57-66 3. W. B. Santosoa, R. A. Bakara, A. Nurb, Combustion characteristics of diesel-hydrogen dual fuel engine at low load, International Conference on Sustainable Energy Engineering and Application, Energy Procedia 32 (2013) 3 – 10. 4. Constantin Pana, Christian Nutu, Experimental aspects of the hydrogen use at diesel engine, 10th International Conference Interdisciplinary in Engineering, 2017, 649 – 657. ghn 5. Rachan D. Shekar, H. R. Purushothama, Hydrogen induction to diesel engine working on Biodiesel: A Review. Global Challenges, Policy framework and Sustainable Development for Mining of Minerals and Fossile Energy Resources, Earth and Planatory science 2015, 385 – 392. 6. A.P.Singh, A.K.Agarwal, An Experimental Investigation of Combustion, Emissions and Performance of a Diesel Fuelled HCCI Engine. 2012 -28-0005 SAE International. 7. U.V.Kongre, V.K.Sunnapwar, CFD Modeling and Experimental Validation of Combustion in Direct Ignition Engine Fueled with Diesel, International Journal of Applied Engineering Research, Dindigul, Volume 1, No 3, 2010, 508 – 517. 8. Dr.M.J.Lewis, J.B.Moss, P.A.Rubini, CFD Modelling of Combustion and Heat Transfer in Compartment Fires, International Symposium on Fire Safety Science, Melbourne, 1997, 463 – 474. 9. MA.Siddique, S.A.Azeez, R.Mohammed, Simulation And CFD Analysis of Various Combustion Chamber Geometry of A C.I Engine Using CFX, International Refereed Journal of Engineering and Science (IRJES) Volume 5, Issue 8 (August 2016), 33 – 39. 10. Rajesh Bisane, DhananjayKatpatal, Experimental Investigation & CFD Analysis of an Single Cylinder Four Stroke C.I. Engine Exhaust System, International Journal of Research in Engineering and Technology, 50 – 55. 11. H.Sushma and Jagadeesha.K.B, CFD modelling of the in-cylinder flow in direct injection Diesel engine, International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013, 1 – 6. 12. DivyanshuPurohit, Pragya Mishra, VishwanathBanskar, Flow Simulation of an I.C. Engine in FLUENT, ANSYS 14.0. International Conference on Emerging Trends in Mechanical and Electrical Engineering (ICETMEE- 13th-14th March 2014), 252 – 255. 13. Z.F. Tian, J. Abraham, Development of a two-dimensional internal combustion engines model using CFD for education purpose. 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013, 1575 – 1581. 1. Authors: Ranjana Jadhav, Shubhangi Ovhal, Priyanka Mutyal, Aishwarya Damale, Sriya Nagannawar Paper Title: Automating Security Vulnerabilities using Scanning and Exploiting 13. Abstract: Cyber security is gaining tremendous importance due to the increased reliance on the internet, computer systems and wireless networks like Wi-Fi and Bluetooth. Explosive growth of internet has invited in 67-70 new advancements, but these technical advancements have a dark side: Attackers. To sway away the cyber attacks caused by the attackers, it is a vital job to provide a tough security to the arrangement. This research explores the characteristics of our security tool “Autoploit”. Autoploit is used for scanning and exploiting. It is applied in checking the security of nodes in LAN and Web application. The process starts with scanning. For scanning of nodes in LAN, IP address is taken as the input and for website, URL of the website is taken as the input. After scanning process is carried out, all the vulnerable open ports are discovered. Banner grabbing gives the services of the vulnerable ports. Later in exploiting, Autoploit has its own exploits. Exploiting is done by version wise attack. It prevents the system from crashing. If the exploiting is successful then it is concluded that the system is not secure enough. Thus Autoploit is used to give the security efficiency report of the tested system.
Keywords: scanning, exploit, reconnaissance, banner grabbing, attack.
References: 1. Regner Sabillon, Victor Cavaller, Jeimy Cano, Jordi Serra- Ruiz.(2016) “Cybercriminals, Cyberattacks and Cybercrime Privacy, security and control” IEEE International Conference on Cybercrime and Computer Forensic (ICCCF). 2. Brijesh Kumar Pandey,Alok Singh,Lovely lakhmani balani.(2015)”ETHICAL HACKING TOOLS(Tools,Techniques and Approaches)” Conference 3. Brad Arkin, Scott Stender, Gary McGraw: “Software Penetration Testing”[J]. IEEE Security & Privacy, 2005, 3(1): 84-87. 4. Gordon “Fyodor” Lyon,” Nmap Network Scanning,”2009. 5. Xia Yi-min, etc. “Security Vulnerability Detection Study Based on Static Analysis”. Computer Science, 2006.33(10). 6. Gurpreet K. Juneja1,(2013)” ETHICAL HACKING: A TECHNIQUE TO ENHANCE INFORMATION SECURITY’ International Journal of Innovative Research in Science, Engineering and Technology 7. Ashiqur Rahman,Kantibhusan Roy,Atik Ahmed Sourav,Al- Amin Gaji.(2016)”Advanced Network Scanning”American Jornal of Engineering Research(AJER) (for scanning) 8. Hannes Holm,Teodor Sommestad. (2016) ”SWED:Scanning,vulnerabilities,exploits and Detection” IEEE Authors: V.G.Umale, S.Wadhankar, A.A.Deosant, M.Ahmed
Paper Title: Available Transfer Capability (ATC) based Nodal Pricing Abstract: The transmission pricing depends on generator, load levels and transmission line constraints. The transmission system places an importance on the intensive use interconnected network reliably, which requires knowledge of the complex capability. Available Transfer Capability (ATC) is a compute of the remaining power transfer capability of the transmission network for further transactions. Available transfer capability in the transmission system has become essential quantity to be declared well in advance for its commercial use in a competitive electricity marketplace. The proposed approach of ATC using AC Power transfer distribution factors (AC PTDFs) based approach has been used for single and concurrent transactions using power transfer distribution factor and ATC based Locational Marginal Pricing methodology is used to decide the energy price for transacted power and to handle the network congestion and marginal losses. Simulation is carried out on IEEE30 bus system.
Keywords: Locational Marginal Pricing, Optimal Power Flow, transmission pricing, Available Transfer Capability (ATC), AC Power Transfer Distribution Factor (ACPTDF)
14. References: 1. V.Sarkar, S.A.Khaparde, “Optimal LMP decomposition for the ACOPF calculation” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1714– 1723, Aug. 2011. 71-73 2. Q.Zhou, L.Tesfatsion,C.C.Liu, “Short term congestion forecasting in wholesale power” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2185–2196, Nov. 2011. 3. E. Litvinov,” Design and operation of the locational marginal prices-based electricity markets” IET Gener. Transm. Distrib., 2010, Vol. 4, Iss. 2, pp. 315–323 4. J.C.Peng,H.Jiaang, G,Xu ,A.Luo & C.Huang “Independent marginal losses with application to localtional marginal price calculaton IET Gener. Transm. Distrib., 2009, Vol. 3, Iss. 7, pp. 679–689 5. M.S.Kumari, M.Murali “LMP based electricity market simulation using genetic algorithm” 7th IEEE conference on Industrial Electronics and Applications(ICIEA)2012 6. F.Li, R.Bo,”DCOPF based LMP simulation :Algorithm comparison with ACOPF and sensitivity”, IEEE Trans. Power Syst., vol. 22, no. 4, pp. 14751485–2196, Nov. 2007. 7. Dr. ParamasivamVenkatesh, RamachandranGnanadass and Dr.Narayana Prasad Padhy, "Available Transfer Capability Determinations International Journal of Emerging Electric Power Systems, vol. I, Issue 2, 2004. 8. G.C. Ejebe, 1. Tong, J.G. WaIgh!. etc., "Available TransferCapability Calculations,"IEEE Transactions on Power Systems, vol.13, No4, , pp.1521-1527,November 1998. 9. HfsPeter W. Sauer,"Technical Challenges of Computing AvailableTransfer Capability (ATC) in Electric Power Systems,"Proceedings,30th Annual Hawaii International Conference on System Sciences,Jan. 7-10, 1997. 10. Yan Ou and Chanansingh, "Assessment of Available TransferCapability and Margins," IEEE Transactions on Power Systems, vol.17,No 2, 2002. Authors: Neha Verma, Vinay Sharma An Experimental Research in Sustainability Analysis in Industries based on Lean Green and Six Sigma Paper Title: using AHP and Fuzzy AHP Abstract: Sustainability is a matter of utmost importance in the industries of all the sectors in the current 15. scenario. Thus a study based on the different factors responsible for the sustainability is done. Hence three factors which affect sustainability are considered they are Green (environmental factors), Lean manufacturing(waste 74-81 minimization) and Six Sigma (zero defects). Sub factors to all this factors are also selected. These factors and their sub factors are than prioritized by the proportion or percentage to which they affect the sustainability of the organization. These factors are given ratings from the industries and these ratings are further utilized to determine the priority ratios. The prioritization is done by using Fuzzy and Fuzzy AHP processes.
Keywords: Fuzzy, AHP, Sustainability, Lean manufacturing, Green manufacturing, Six Sigma.
References: 1. Saaty, Thomas L. "How to make a decision: the analytic hierarchy process." European journal of operational research 48, no. 1 (1990): 9- 26. 2. Acharya, Vikas, Somesh Kumar Sharma, and Sunand Kumar Gupta. "Analyzing the factors in industrial automation using analytic hierarchy process." Computers & Electrical Engineering (2017). 3. Thanki, Shashank, KannanGovindan, and Jitesh Thakkar. "An investigation on lean-green implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach." Journal of Cleaner Production 135 (2016): 284-298. 4. Maletič, Damjan, MatjažMaletič, Viktor Lovrenčić, Basim Al-Najjar, and BoštjanGomišček. "An application of analytic hierarchy process (AHP) and sensitivity analysis for maintenance policy selection." Organizacija 47, no. 3 (2014): 177-188. 5. Kłos, Sławomir, and Peter Trebiina. "Using the AHP method to select an ERP system for an SME manufacturing company." Management and Production Engineering Review 5, no. 3 (2014) 6. Madadian, Edris, Leila Amiri, and Mohammad Ali Abdoli. "Application of analytic hierarchy process and multicriteria decision analysis on waste management: a case study in Iran." Environmental Progress & Sustainable Energy 32, no. 3 (2013): 810-817. 7. Caputo, Antonio C., Pacifico M. Pelagagge, and Paolo Salini. "AHP-based methodology for selecting safety devices of industrial machinery." Safety science 53 (2013): 202-218.. 8. Samah, MohdArmi Abu, Latifah Abdul Manaf, and N. I. M. Zukki. "Application of AHP model for evaluation of solid waste treatment technology." Int J Eng Tech 1, no. 1 (2010): 35e40. 9. Ho, Fu Haw, SalwaHanim Abdul-Rashid, and Raja Ariffin Raja Ghazilla. "Analytic hierarchy process-based analysis to determine the barriers to implementing a material efficiency strategy: Electrical and electronics’ companies in the Malaysian context." Sustainability 8, no. 10 (2016): 1035. 10. Jayawickrama, H. M. M. M., A. K. Kulatunga, and S. Mathavan. "Fuzzy AHP based Plant Sustainability Evaluation Method." Procedia Manufacturing 8 (2017): 571-578. Authors: Jawahar Gawade, Latha Parthiban Paper Title: An Experimental Analysis on Opinion Mining Feature Identification for Product Analysis Abstract: Opinion feature mining is also known as aspect mining used to take out users opinions, and attitudes towards a specific product, services and their characteristics. The most of the existing approaches to opinion feature extraction on mining patterns is only by using a single review corpus. This paper presents the new method to discover the opinion features from online reviews by taking out the difference in opinion feature statistics across two different corpora, one domain specific corpus and another is domain independent corpus (i.e. the contrasting corpus). Domain relevance is the measure which is used to capture the disparity. The domain relevance characterizes the relevant term from the text collection. Firstly, the sentences are extracted from the reviews. Then the POS Tagger is applied to separate out the nouns, noun phrases and adjectives. Next the candidate features are extracted by applying the syntactic rules designed for Standard English. For every candidate feature ,the Intrinsic Domain Relevance (IDR) and Extrinsic Domain Relevance (EDR) scores are calculated by using Domain dependent and domain independent corpus respectively. a The interval threshold approach, called as IEDR Criteria is applied to confirm the final Opinion Feature in which the candidate feature having IDR score greater than IDR threshold, and EDR scores less than EDR threshold is checked .
Keywords: Opinion mining, Domain relevance, part-of-speech tagging, Opinion Feature 16.
References: 82-86 1. Hai, Kuiyu Chang, Jung-Jae Kim, and Christopher C.Yang,“Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance”, IEEE Transaction on knowledge and data engineering, vol. 26, no. 3, March 2014. 2. V. Hatzivassiloglou and J.M. Wiebe, “Effects of Adjective Orientation and Gradability on Sentence Subjectivity”, Proc 18th Conf. Computational Linguistics, pp. 299-305, 2000. 3. B. Pang, L. Lee, and S. Vaithyanathan, “up?: Sentiment Classification Using Machine Learning Techniques,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 79-86,2002. 4. A. Yessenalina and C. Cardie, “Matrix-Space Models for Sentiment Analysis,”. Conf. Empiricalb Methods in Natural Language Processing, pp. 172-182, 2011. 5. Z. Hai, K. Chang, Q. Song, and J.-J. Kim, “Statistical Nlp Approach for Feature and Sentiment Identification from Chinese Reviews,”. CIPS-SIGHAN Joint Conf. Chinese Language Processing, pp. 105-112, 2010 6. B. Pang and L. Lee, “Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,”. 42nd Ann. Meeting on Assoc. for Computational Linguistics, 2004. 7. R. Mcdonald, K. Hannan, T. Neylon, M. Wells, and J. Reynar, “Structured Models for Fine-to-Coarse Sentiment Analysis,,”. 45th Ann. Meeting of the Assoc. of Computational Linguistics, pp. 432-439, 2007. 8. L. Qu, G. Ifrim, and G. Weikum, “Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns,” Proc 23rd Intl Conf. Computational Linguistics, pp. 913-921, 2010. 9. W. Jin and H.H. Ho, “Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining,” Proc. 26th Ann. Intl Conf. Machine Learning, pp. 465-472, 2009. 10. D.M. Blei, A.Y. Ng, and M.I. Jordan, “Dirichlet Allocation,”. Machine Learning Research, vol. 3, pp. 993-1022, Mar. 2003. Authors: G. Ilankumaran, V. Darling Selvi Paper Title: Customer Purview of Cashless Payment System in the Digital Economy of India Abstract: Indian payments industry is largely dominated by cash-based transactions. In digital payments, payer 17. and payee both use digital modes to send and receive money. It is also called electronic payment. The reduced transaction charges and the degree of ease of cash transfers associated with the electronic fund transfers and 87-93 mobile banking will further drive the growth of digital payment systems in India. This paper highlights the growth of digital payment infrastructure in India and the problems faced by the sample respondents in connection with the operation of digital payment services. The researcher has collected data on Worldwide Mobile and Broadband Subscriptions and Government e-Payments Rankings along with the growth percentages as secondary source of data and primary data were collected from a sample of 100 respondents from Tirunelveli town of Tamilnadu. The survey results were interpreted with the help of Reliability Analysis, percentage analysis, Reliability analysis, Factor analysis and structural Equation Modelling through SPSS and AMOS software. The study reveals that though the sample respondents use digital payment services in various forms and various purposes, they too come across with the problems of infrastructure, awareness and operation. The structural equation model fits the purpose. If the penetration of digital payment services is entered into every nook and corner of the villages by the extension of internet facilities, it will become a user friendly source of operation to everyone across India irrespective of the rural and urban villages. The ultimate focus should be to keep the momentum going with more support from the government and innovations, safety and convenience from the players.
Keywords: Awareness, Digital, e-Payments, Infrastructure, Payment System
References: 1. Chakraborty, Rajesh (2006), The Financial Sector in India: Emerging Issues, Oxford University Press. 2. Committee on Payment and Settlement Systems of the central banks of the Group of Ten countries (March 1997). Real-Time Gross Settlement Systems Bank for International Settlements. p. 14. 3. Daddihal V.S. Kulkarni P.K. (1998), “Technology in Banks A Case Study of HDFC Bank”, Professional Bankers, Vol-VIII Issue-4, p-82. 4. Kalita, Basanta (2004), “Post-1991 Banking Sector Reforms in India: Policies and Impact", http://ssrn.com/abstract=1089020. 5. Mohan, R. (2006), “Reforms, Productivity and Efficiency in Banking: The Indian Experience”, RBI Monthly Bulletin, March, pp.279- 293. 6. RBI, Statistical Tables relating to Banks in India, Various issues (1997-98 to 2007-08). 7. Report on Trend and Progress of Banking in India 8. Reserve Bank of India. (1984), Report of the Committee on Mechanisation in Banking Industry. 9. RBI (1998), Report of the committee on Banking Sector Reforms (The Narasimhan committee) Mumbai: Reserve Bank of India. 10. Singh, B., and Malhotra, P. (2004), Adoption of Internet banking: An empirical investigation of Indian banking, Sector, Journal of Internet Banking and Commerce, Vol. 9, No. 2. 11. ChandrawatiNirala, Dr. BB Pandey(2017), “Role of E-Banking services towards Digital India”, International Journal of Commerce and Management Research, April 2017, Volume 3, Issue 4, pp. 67-71 12. Das, Ashish& Singh, Rakhi. (2019). Cashless Payment System in India-A Roadmap, Technical Report 2010, Indian Institute of Technology Bombay 13. Sujith T S, Julie C D (2017), “Opportunities and Challenges of E- Payment System in India”, International Journal of Scientific Research and Management (IJSRM), Volume 5, Issue 9, pp. 6935-6943 14. Suma vally. K, HemaDivya. K (2018)“A study on Digital payments in India with perspective of consumers adoption”, International Journal of Pure and Applied Mathematics, Volume 118, No. 24 15. www.rbi.org.in 16. www.ibef.org Authors: N. Suresh, T. Antony Alphonnse Ligori, Shad Ahmad Khan, Prabha Thoudam Paper Title: Predicting Financial Distress of Bhutan Telecom Limited Abstract: In the country of Gross National Happiness, the telecom industry is showing a momentous and a stable growth. The cellular network is gaining momentum across the country to connect people in the remotest part of Bhutan so that the people receive the benefits of digitalization. The present research examines the three renowned accounting based prediction models for analysing the healthiness of Bhutan Telecom limited (BTL). The applications of models under study are Altman’s Z-score, Springate and Zmijewski. The findings of the study reveal that BTL under the three well-known models found to be healthy.
Keywords: Financial Healthiness, Altman Z-score, Springate S-score, Zmijewski X-score
References: 1. Agarwal, A., and Patni, I. (2019). Bankruptcy Prediction Models: An Empirical Comparison. International journal of Innovative Technology and Exploring Engineering, 8(6S2), 131-139. 2. Apoorva, D., Curpod, S, P., and Namratha, M. (2019). Application of Altman Z Score Model on Selected Indian Companies to Predict 18. Bankruptcy. International Journal of Business and Management Invention, 8(1), 77-82. 3. AlAli, M. (2018). Predicting Financial Distress for Mobile Telecommunication Companies Listed in Kuwait Stock Exchange using Altman’s Model. Journal of Economics, Finance and Accounting, 5(3), 242-248. 94-99 4. AlAli, M. S, Bash, A.Y., AlForaih, E.O., AlSabah, A.M., and AlSalem, A.S. (2018). The Adaptation of Zmijewski Model in Appraising the Financial Distress of Mobile Telecommunications Companies listed at Boursa Kuwait. International Academic Journal of Accounting and Financial management, 5(4), 129-136. 5. Bhutan Telecom Limited. (2009-2018). Annual Reports. Thimphu, Bhutan. 6. Chouhan, V., Chandra, B., and Goswami, S. (2014). Predicting Financial Stability of Select BSE Companies Revisiting Altaman Z Score. International Letters of Social and Humanistic Sciences, 26, 92-105. 7. Januri, Sari, E.N., and Diyanti, A. (2017). The Analysis of the Bankruptcy Potential Comparative by Altman Z-Score, Springate and Zmijewski Methods at Cement Companies Listed in Indonesia Stock Exchange. Journal of Business and Management, 19(10), 80-87. 8. Khoury, E., Rim, Beaino, A., and Roy.(2014). Classifying Manufacturing Firms in Lebanon: An Application of Altman’s Model. Procedia – Social and Behavioral Sciences 109, 11-18. 9. Lagkas, D.T., and Papadopoulos, D. (2014). Financial Analysis Considering Distress Prediction Models of Telecommunications Companies Listed in Athens Stock Exchange: Hellenic Telecommunications Organization, Forthnet, Hellas Online. International Journal of Decision Sciences, Risk and Management (IJDSRM), 5(4), p.376. ISSN 1753-7169. 10. Narender, S., and Rajendar, K. (2016). Debt Management Practices in Telecom Sector in India – A Study of Select Companies. Indian Journal of Commerce & Management Studies, 7, 2(1), 46-50. 11. National Revenue Report FY 2016-17, Department of Revenue & Customs, Ministry of Finance, Bhutan, p.56. 12. Primasari, N.S. (2017). Analysis Altman Z-Score, Grover Score, Springate and Zmijewski As Financial Distress Sginaling. Accounting and Management Journal, 1(1), 23-42. 13. Ramachandran, N., and Kelkar, A. S (2019). Financial Performance of Telecom Industry in Sultanate of Oman. Shanlax International Journal of Management, 6(3), 43-51. 14. Sinarti., and Sembiring, T. M. (2015). Bankruptcy Prediction Analysis of Manufacturing Companies Listed in Indonesia Stock Exchange. International Journal of Economics and Financial Issues, 5(Special Issue), 354-359. 15. Syamni, G., Majid, M.S.A., &Siregar, W.F. (2018). Bankruptcy Prediction Models and Stock Prices of The Coal Mining Industry in Indonesia. Etikonomi: JurnalEkonomi. Vol. 17 (1): 57 – 68. doi: http//dx.doi.org/10.15408/etk.v17i1.6559. 16. Verma, A., and Pandit, J. (2019). An Analysis of Financial Distress of Selected Public Sector Enterprises of India Using Zmijewski X- Score Model. International Journal of Engineering Development and Research, 7(1), 362-366. 17. Zainuddin, Z., Tapa., A., and Rahim, A.I.A., (2016). Examine The Financial Health of the Listed Technology Companies in Malaysia Using Altman’s Z-Score Test. Proceedings of the 3rd International Conference on Applied Science and Technology, 1-7. Authors: Nitai Chandra Debnath, B.C.M.Patnaik, Ipseeta Satpathy By Appearance and/or by Amount: The Impact of Presence and Size of Audit Committees on Real Paper Title: Earnings Management in Bangladesh Abstract: In this study, we analyze how the presence and size of audit committees are interrelated to real earnings management empirically in the context of Bangladesh. To accomplishour study, we utilize a sample of 2191 firm year observations listed on the Dhaka Stock Exchange throughout the period of 2000-2017. Our study ascertains that the size of audit committeesisnegatively associated with real earnings management. Results also demonstrate that all three composite measures of real earnings management are negatively associated with the size of audit committees. More specifically, large audit committee are more capable of restraining managers from real earnings management practices through changing discretionary expenses. On the other hand, presence of audit committees is also negatively associated with real earnings management Though this association is not statistically significant. It indicates that the presence of audit committees will not improve the situation considerably; rather, experience, diversification, size, and independence may offer opportunities for positive changes. Regulators may be aware of this and change the provision of audit committee formation accordingly.
Keywords: Audit committee, Audit committee size, Real earnings management, Corporate governance
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The users of the software expect the early delivery of the software, so the demand to decrease the delivery time of software is increasing day by day. As the demand for early delivery of software in increasing day by day, so guaranteeing the quality of software is becoming critical. While designing and building the software there may be some errors 20. which are commonly known as software bugs. About one third of the total cost is due to the software bugs. So it advantageous to use some intelligent technique for software bugs detection. The data of the software bug is 108-113 contained in the repository, called the software bug repository. As the bug repository contains the huge amount of data, different types of data mining techniques may be applied to extract the hidden information from the software bug repository. Software bugs are classified using data mining techniques on the basis of the different parameters like accuracy precision, recall and F-measures. Different types of bug classification techniques using data mining have been studied in this paper and the results compared.
Index Terms: Bug Tracking, Classification Algorithms, Data Mining, Software Bugs
References:
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Authors: Shivangi Dutta Bhavna Arora Paper Title: Preprocessing for Parts of Speech (POS)Tagging in Dogri Language Abstract: Natural language processing (NLP) is viewed among the most crucial fields of computer science, information retrieval and artificial intelligence. One such challenging feature in NLP is Parts of speech (POS) tagging. It is the process of labelling the words present in the corpus as the parts of speech. According to English grammar there are eight major parts of speech which are: noun, pronoun, verb, adjective, adverb, preposition, conjunction, interjection. Over the past few years, various researchers have compassed considerable amount of work using various pursues to closely supervised tagging and unmonitored tagging. These methods of labelling are further divided into rules-based, stochastic and hybrid approaches. The language that has been taken for research work is Dogri Language which is based on Devanagari script. The paper presents the related work in the languages having same script as Dogri. The study helps in the selection of appropriate technique to be used for POS tagging for Dogri language. The paper also presents grammatical and inflectional analysis of Dogri language along with few rules for designing POS tagger. A section of the paper also demonstrates the results of preprocessing i.e. tokenization and stemming of Dogri text, which are considered as the initial steps in POS tagging.
21. Index Terms: Dogri language, Parts of speech tagging, stemming, tokenization. References: 114-120 1. S. Kumar, “Developing POS Tagset for Dogri,” Language in India www.languageinindia.com, vol.18, no. 1, 2018. 2. S. Bhatta, K. Parmara and M. Patelb, “Sanskrit Tag-sets and Part-Of-Speech Tagging Methods- A Survey,” International Journal of Innovative and Emerging Research in Engineering (IJIERE), vol. 2, 2015 3. S. Rathod and S. Govilkar, “Survey of various POS tagging techniques for Indian regional languages,” International Journal of Computer Science and Information Technologies (IJCSIT), vol. 6, pp. 2525-2529,2015 4. N. Joshi, H. Darbari and I. Mathur, “HMM BASED POS TAGGER FOR HINDI,” The Second International conference on Parallel, Distributed Computing technologies and Applications (PDCTA), pp. 341-349,2013 5. A. Ekbal, R. Haque, and S. Bandyopadhyay, “Maximum Entropy Based Bengali Part of Speech Tagging,” Advances in Natural Language Processing and Application, Research in Computer Science (RCS)Journal (33), pp.67-78,2008. 6. J. Lafferty, A. McCallum and F. C.N. Pereira, “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), pp. 282-289, 2001. 7. S. Singh, K. Gupta, M. Shrivastava and P. Bhattacharyya, “Morphological Richness Offsets Resource Demand- Experiences in Constructing a POS Tagger for Hindi,” Proceedings of the COLING/ACL -Association for Computational Linguistics, pp. 779–786, July 2006. 8. N. Mishra and A. Mishra, “Part of Speech Tagging for Hindi Corpus,” Int. Conf. Commun. Syst. Netw. Technol., pp. 554–558, 2011. 9. N. Garg, V. Goyal, and S. Preet, “Rule Based Hindi Part of Speech Tagger.,” COLING (Demos), vol. 2, no. December, pp. 163–174, 2012. 10. A. Dalal, K. Nagaraj, U. Sawant, and S. Shelke, “Hindi Part-Of-Speech Tagging and Chunking: A Maximum Entropy Approach,” Proceeding NLPAI Mach. Learn. Compet., pp. 1–4, 2006. 11. R. Kumar and S. S. Shekhawat, “PARTS OF SPEECH TAGGING FOR HINDI LANGUAGES USING HMM,” Int. J. Sci. Res., vol. 7, no. 4, pp. 42–44, 2018. 12. K. Mohnot, N. Bansal, S. . Singh, and A. Kumar, “Hybrid approach for Part of Speech Tagger for Hindi language,” Int. J. Comput. Technol. Electron. Eng., vol. 4, no. 1, pp. 25–30, 2014. 13. P. K. Dwivedi and P. K. Malakar, “Hybrid Approach Based POS Tagger for Hindi Language,” Int. J. Res. Stud. Comput. Sci. Eng., vol. 4840, no. August, pp. 63–68, 2015. 14. V. Khicha and M. Manna, “Part-of-Speech Tagging of Hindi Language Using Hybrid Approach,” Int. J. Eng. Technol. Sci. Res., vol. 4, no. 8, pp. 737–741, 2017. 15. P. Bagul, A. Mishra, P. Mahajan, M. Kulkarni, G. Dhopavkar, and M. T. Scholar, “Rule Based POS Tagger for Marathi Text,” vol. 5, no. 2, pp. 1322–1326, 2014. 16. S. Rathod, S. Govilkar, and S. Kulkarni, “Part of Speech TAGGER for MARATHI Language,” Sixth International Conference on Computational Intelligence and Information Technology(CIIT), 2016, pp. 131–138. 17. J. Singh, N. Joshi, and I. Mathur, “Development of Marathi part of speech tagger using statistical approach,” in Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, 2013, pp. 1554–1559. 18. Nita V. Patil, “POS Tagging for Marathi Language using Hidden Markov Model,” International Journal of Computer Sciences and Engineering IJCSE, vol.6, no. 1, pp. 2347-2693, 2018. 19. N. Tapaswi, B. Santorini, and M. A. Marcinkiewicz, “Treebank Based Deep Grammar Acquisition and Part- Of-Speech Tagging for Sanskrit Sentences.” 20. A. Yajnik, “Part of Speech Tagging Using Statistical Approach for Nepali Text,” Int. J. Cogn. Lang. Sci., vol. 11, no. 1, pp. 76–79, 2017. 21. P. Dubey, “The Hindi to Dogri machine translation system : grammatical perspective,” Int. J. Inf. Technol., 2018. 22. V. Gupta, “Hindi Rule Based Stemmer for Nouns,” Int. J. Adv. Res. Comput. Softw. Eng., vol. 4, no. 1, pp. 62–65, 2014. 23. B. P. Pande, P. Tamta, and H. S. Dhami, “A Devanagari Script based Stemmer,” Int. J. Comput. Linguist. Res., vol. 5, no. 4, pp. 119– 130, 2014. 24. A. Pimpalshende and A.R. Mahajan, “Extraction of Root Words Using Morphological Analyzer for Hindi Text,” International Journal of Soft Computing, vol.13, no. 5, pp 134-138, 2018. Authors: Neha Verma, Devanand, Bhavna Arora Paper Title: Experimental Analysis of Recommendation System in e-commerce Abstract:Recommendation System (RS) are generally used in e-commerce industry to solve the complication of information overloading. Large amount of information is generating now, days due to which user face the difficulty in finding the relevant information of product and services matching to their taste and preferences. Data mining (DM) is the process of mining and extracting useful knowledge from large datasets. The tasks of DM are to do description and prediction of data to retrieve the information. RS is a subfield of information retrieval (IR) and IR is subfield of DM. Recommendation engines basically are data filtering and IR tool that make use of algorithms and data to recommend the most relevant item to particular user. The various technique and approaches used by RS are content-based (CB) filtering, Collaborative Filtering (CF) and hybrid filtering techniques. This paper illustrates the role of Data Mining in Recommendation System and proposes a workflow of RS. Also describes the review of techniques, challenges of RS & compares recommendation systems of various e-commerce websites
Keywords:Data Mining, Recommendation System, Recommendation Technique, e-commerce
References:
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Niu, “A research of job recommendation system based on collaborative filtering,” Proc. - 2014 7th Int. Symp. Comput. Intell. Des. Isc. 2014, vol. 1, no. 1, pp. 533–538, 2015. 46. Y. G.Patel and V. P.Patel, “A Survey on Various Techniques of Personalized News Recommendation System,” Int. J. Eng. Dev. Res., vol. 3, no. 4, pp. 696–700, 2015. 47. C. Rana, “Survey Paper on Recommendation System,” Int. J. Sci. Res., vol. 3, no. 2, pp. 3460–3462, 2012. 48. D. V. Attarde, “Survey on Recommendation System using Data Mining and Clustering Techniques,” Int. J. Res. Eng. Appl. Manag., vol. 03, no. 09, pp. 1–5, 2017. 49. R. Prabhu, P. Shetty, D. R. Shwetha, and R. Hegde, “A review : Recommender System using Collaborative Filtering and Gray Sheep Problem,” Int. J. Eng. Dev. Res., vol. 6, no. 2, pp. 440–443, 2018. 50. P. S. S. Srinivasa G, Archana M, “Survey Paper on Recommendation System using Data Mining Techniques,” Int. J. Eng. Comput. Sci., vol. 6, no. 4, pp. 2454–4698, 2016. 51. S. V Hovale and P. G, “Survey Paper on Recommendation System using Data Mining Techniques,” Int. J. Eng. Comput. Sci., vol. 5, no. 5, pp. 16697–16699, 2016. 52. G. Linden, B. Smith, J. York “Amazon.com Recommendations Item-to-Item Collaborative Filtering,” IEEE Comput. Soc., vol. 5, no. 4, pp. 339–346, 2003. 53. R. Tamvada, “Recommendations — The Machine-Learnt Way! – Flipkart Tech Blog,” Flipkart Tech Blog, 2018. [Online]. Available: https://tech.flipkart.com/e-commerce-recommendations-using-machine-learning-5002526e531a. [Accessed: 14-Feb-2019]. 54. X.Amatriain and J. Basilico, “Netflix Recommendations: Beyond the 5 stars (Part 1),” Netflix Technology Blog, 2012. [Online]. Available: https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429. [Accessed: 14-Feb-2019]. 55. “How Netflix’s Recommendations System Works.” [Online]. Available: https://help.netflix.com/en/node/100639. [Accessed: 14-Feb- 2019]. 56. P. Covington, J. Adams, and E. Sargin, “Deep Neural Networks for YouTube Recommendations,” ACM, 2016. M. KABILJO and A. ILIC, “Recommending items to more than a billion people - Facebook Code,” 2015. [Online]. Available: https://code.fb.com/core-data/recommending-items-to-more-than-a-billion-people/. [Accessed: 14-Feb-2019]. Authors: Anju Bala, Priti, Paper Title: An Experimental Analysis of Meta Heuristic Techniques on Unimodal and Multimodal Functions Abstract:The advancement in the technology leads to the increase in the complexity of the problems. The traditional heuristic algorithms are not suitable for the optimized results of such complex problems. This leads to generation of Meta heuristic techniques which incorporate the exploration as well as the exploitation search. This paper studies different state of art Meta heuristic techniques like ant colony optimization, particle swarm optimization, differential evolution and genetic algorithm. This paper also covers different stable modified version 23. of these techniques and implements the same to analyze the performance on different unimodal and the multimodal functions. The analysis clearly signifies the use of Meta heuristic techniques based on application.
Index Terms: Exploration, Exploitation, Meta-heuristic, Multimodal and Unimodal
References:
[1] R. Rajakumar, P. Dhavachelvan, and T. Vengattaraman, “A survey on nature inspired meta-heuristic algorithms with its domain specifications,” Proc. Int. Conf. Commun. Electron. Syst. ICCES 2016, 2016. [2] M. G. Torres, “Metaheuristics in Data Mining,” no. August, 2008. [3] R. Rao Kurada, K. K. Pavan, and A. D. Rao, “A Preliminary Survey on Optimized Multiobjective Metaheuristic Methods for Data Clustering Using Evolutionary Approaches,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 5, pp. 57–77, 2013. [4] X. S. Yang, S. Fong, X. He, S. Deb, and Y. Zhao, “Swarm Intelligence: Today and Tomorrow,” Proc. - 2016 3rd Int. Conf. Soft Comput. Mach. Intell. ISCMI 2016, pp. 219–223, 2017. 128-135 [5] S. C. Chu, H. C. Huang, J. F. Roddick, and J. S. Pan, “Overview of algorithms for swarm intelligence,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6922 LNAI, no. PART 1, pp. 28–41, 2011. [6] C. Blum, “{A}nt colony optimization: {I}ntroduction and recent trends,” Phys. Life Rev., vol. 2, no. 4, pp. 353–373, 2005. [7] M. Dorigo and C. Blum, “Ant colony optimization theory: A survey,” Theor. Comput. Sci., vol. 344, no. 2–3, pp. 243–278, 2005. [8] H. Ali and A. K. Kar, “Discriminant Analysis using Ant Colony Optimization - An Intra-Algorithm Exploration,” Procedia Comput. Sci., vol. 132, pp. 880–889, 2018. [9] I. M. Anwar, K. M. Salama, and A. M. Abdelbar, “Instance Selection with Ant Colony Optimization,” Procedia - Procedia Comput. Sci., vol. 53, pp. 248–256, 2015. [10] R. C. Eberhart, “AltaVista - Babel Fish Translation,” pp. 81–86, 1995. [11] D. P. Rini and S. M. Shamsuddin, “Particle Swarm Optimization: Technique, System and Challenges,” Int. J. Appl. Inf. Syst., vol. 1, no. 1, pp. 33–45, 2011. [12] C. F. Wang and K. Liu, “An improved particle swarm optimization algorithm based on comparative judgment,” Nat. Comput., vol. 17, no. 3, pp. 641–661, 2018. [13] Y. Liu, G. Wang, H. Chen, H. Dong, X. Zhu, and S. Wang, “An improved particle swarm optimization for feature selection,” J. Bionic Eng., vol. 8, no. 2, pp. 191–200, 2011. [14] Y. Lu, M. Liang, Z. Ye, and L. Cao, “Improved particle swarm optimization algorithm and its application in text feature selection,” Appl. Soft Comput. J., vol. 35, pp. 629–636, 2015. [15] J. McCall, “Genetic algorithms for modelling and optimisation,” J. Comput. Appl. Math., vol. 184, no. 1, pp. 205–222, 2005. [16] R. Malhotra, N. Singh, and Y. Singh, “Genetic Algorithms : Concepts , Design for Optimization of Process Controllers,” Comput. Inf. Sci., vol. 4, no. 2, pp. 39–54, 2011. [17] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002. [18] Y. Yusoff, M. S. Ngadiman, and A. M. Zain, “Procedia Engineering Overview of NSGA-II for Optimizing Machining Process Parameters,” Rg, vol. 00, pp. 3978–3983, 2011. [19] D. Karaboǧa and S. Ökdem, “A simple and global optimization algorithm for engineering problems: Differential evolution algorithm,” Turkish J. Electr. Eng. Comput. Sci., vol. 12, no. 1, pp. 53–60, 2004. [20] A. Youyun and C. Hongqin, “Experimental study on differential evolution strategies,” Proc. 2009 WRI Glob. Congr. Intell. Syst. GCIS 2009, vol. 2, pp. 19–24, 2009. [21] T. Vivekanandan and N. C. Sriman Narayana Iyengar, “Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease,” Comput. Biol. Med., vol. 90, no. April, pp. 125–136, 2017. Authors: Kamlesh Kumari, Sanjeev Rana Paper Title: Off-Line Persian Signature Verification: An Empirical Evaluation Abstract:Signature verification is a frequent task in forensic document investigation. In this paper, an empirical study of off-line Persian signature verification system has been proposed. Persian signatures are different from other signature types because citizens generally do not use text in it and they draw a outline as their signature. Initially scanned signature images undergoes suitable preprocessing steps. After preprocessing, features based on Euler number, average object area, mean and area are extracted. Finally offline signatures are verified using SVM, KNN and Boosted Tree. Publically available database UTSIG is used. Paper also compares the result for different signers and different samples size
Index Terms: Support Vector Machine (SVM), UTSig (University of Tehran Persian Signature)
References:
1. A.Soleimani, K.Fouladi and B.N.Araabi, "UTSig: A Persian offline signature dataset," IET Biometrics 6.1, 2016. 24. 2. M.Diaz, M.A. Ferrer, D.Impedovo, M. I. Malik, G.Pirlo, and R Plamondon, “A Perspective Analysis of Handwritten Signature Technology,” ACM Computing Surveys (CSUR), 2019. 3. K.Kumari, V.K. Shrivastava, “ A Review of Automatic Signature Verification,” ICTCS,2016 136-138 4. Elias N. Zois et al,. "Representation and Verification of Offline Signatures with Dictionary Learning and Parsimonious Coding," arXiv preprint arXiv: 1807.05039, 2018. 5. Snehal K. Jadhav and M. K. Chavan, "Symbolic Representation Model for Off-Line Signature Verification," 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018. 6. B.S Thakare, and Hemant R. Deshmukh, "Optimized Classification Approach for Offline Signature Verification System," 3rd International Conference for Convergence in Technology (I2CT). IEEE, 2018. 7. P. Maergner, N. Howe, K. Riesen, R. Ingold and A. Fischer, "Offline Signature Verification Via Structural Methods: Graph Edit Distance and Inkball Models," 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, NY, 2018, pp. 163-168 8. S. Shariatmadari, S. Al-maadeed, Y. Akbari, I. Rida and S. Emadi, "Off-line Persian Signature Verification using Wavelet-based Fractal Dimension and One-class Gaussian Process," 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Edinburgh, 2018, pp. 168-173 9. K.Kumari, S.Rana, “Offline Signature Verification using Intelligent Algorithm” International Journal of Engineering & Technology, 2018, pp. 69-72. 10. L. G. Hafemann, R. Sabourin and L. Oliveira, "Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification," in IEEE Transactions on Information Forensics and Security. 2019. 11. K.Kumari, V.K. Shrivastava, “Factors Affecting the Accuracy of Automatic Signature Verification,” IEEE, 2016. 12. S.Rana, A.Kumar, K.Kumari, “Performance Analysis of off-line signature verification,” International Conference on Innovative Computing and Communication, ICICC, 2019. Randeep Singh, Dr. Amit Bindal, Dr. Ashok Kumar Authors: 25. Paper Title: Reducing Maintenance Efforts of Developers by Prioritizing Different Code Smells Abstract:An architectural problem associated with a software system constantly affects the evolving system. These architectural problems are symptoms of different code smells that must be removed using refactoring. The refactoring efforts are directly associated with the maintenance cost of the software system. This cost can be minimized or optimized by prioritizing different code smells. Prioritization helps tackle only a subset of code smells and hence minimize the maintenance cost. This makes code smells ranking and prioritization an important research area and is tackled in this paper. This paper proposes a new approach that is capable of ranking the existing code smells. This prioritization is based on the newly proposed metric based on three identified key criteria. Firstly, the severity of a code smells based on the change-history of a software system. Secondly, the association of the code smells with the improvement in the understandability of the software system. Thirdly, the importance of a developer’s feedback for a given code smells associated with a class in the software system. The feasibility of the proposed approach is tested and evaluated on an open-source Java software system.
Index Terms: Code Smell, Identification, Prioritization, Change-History, Cognitive Complexity, User Feedback. References:
1. R. Arcoverde, E. Guimaraes, I. Macia, A. Garcia, and Y. Cai, “Prioritization of code anomalies based on architecture sensitiveness,” Proceedings of the 27th Brazilian Symposium on Software Engineering (SBES’13), pp.69–78, 2013. 2. L. Erlikh, “Leveraging legacy system dollars for e-business,” IT Professional, vol. 02, no.3, pp. 17–23, 2000. 3. F.A. Fontana, V. Ferme, M. Zanoni, and R. Roveda, “Towards a prioritization of code debt: A code smell intensity index,” Proceedings of the IEEE Seventh International Workshop on Managing Technical Debt (MTD’15), pp.16–24, 2015. 4. M. Fowler, K. Beck, J. Brant, W. Opdyke, &D. Roberts, “Refactoring: Improving the design of existing code (1st ed.),” Reading, MA: Addison-Wesley, 1999. 139-144 5. F. Khomh, M. Di Penta, Y.-G. Gueheneuc, and G. Antoniol, “An exploratory study of the impact of antipatterns on class change-and fault proneness,” Emp. Softw. Eng., vol. 17, no. 3, pp. 243–275, 2012. 6. R. Marinescu, “Assessing technical debt by identifying design flaws in software systems,” IBM Journal of Research and Development, vol.56, no.5, pp.9:1–9:13, 2012. 7. S. Misra, A. Adewumi, L. Fernandez-Sanzand R. Damasevicius, "A Suite of Object Oriented Cognitive Complexity Metrics," in IEEE Access, vol. 6, pp. 8782-8796, 2018. 8. M.J. Munro,“Product metrics for automatic identification of bad smell: design problems in java source-code,” In F. Lanubile, & C. Seaman (Eds.), Proceedings of the 11th international software metrics symposium. IEEE Computer Society Press, 2005. 9. S.M. Olbrich, D.S. Cruzes, & D.I.K. Sjoberg, “Are all code smells harmful? A study of god classes and brain classes in the evolution of three open source systems,” In Software maintenance, ICSM 2010, pp. 1–10, Timisoara, 2010. 10. T. Paiva, A. Damasceno, E. Figueiredo. et al., “On the evaluation of code smells and detection tools,”J Softw Eng Res Dev, vol. 5,no. 7,2017. 11. F. Palomba, G. Bavota, M. Di Penta, R. Oliveto, and A. De Lucia, “Do they really smell bad? A study on developers’ perception of bad code smells,” in Proc ICSME, pp. 101–110, 2014. 12. F.Palomba,A.Panichella,A.DeLucia,R. Oliveto andA.Zaidman, “A textual-based technique for smell detection,” Proceedings of the 24th IEEE International Conference on Program Comprehension (ICPC’16), pp.1–10, 2016. 13. A. Rani and J. K. Chhabra, "Prioritization of smelly classes: A two phase approach (Reducing refactoring efforts)," 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, pp. 1-6, 2017. 14. N. Sae-Lim, S. Hayashi, and M. Saeki, “Context-based approach to prioritize code smells for prefactoring,” Journal of Software: Evolution and Process, 2017. 15. R. Sehgal,D. Mehrotra&M. Bala, “Prioritizing the refactoring need for critical component using combined approach,” Decision Science Letters, vol. 7, no. 3, pp. 257-272, 2018. 16. S.A. Vidal, C. Marcos, and J.A. D´ıaz-Pace, “An approach to prioritize code smells for refactoring,” Automated Software Engineering, vol.23, no.3, pp.501–532, 2016. 17. A. Yamashita and L. Moonen, “Exploring the impact of inter-smell relations on software maintainability: An empirical study,” in Proc. ICSE, pp. 682–691, 2013. Priya Oberoi, Sumit Mittal, Rajneesh Kumar Gujral Authors:
Paper Title: Multilevel Cloud Security Policy (MCSP) for Cloud-Based Environments Abstract:In the present period Cloud computing (CC) generally addresses the issues of quick arrangement and on-request versatility. Despite the fact that Cloud computing gives countless benefits to the clients like adaptability, versatility and so on however, it additionally brings new security challenges. In this paper, authors have proposed a Multilevel Cloud Security Policy (MCSP) to provide security in Cloud- based environment from the malicious insider attacks. This policy comprises of two levels viz. Cloud Chinese Wall Security Policy (CCWSP), and Cloud Clark Wilson Policy (CCWP). At the outer level of MCSP, CCWSP operates, while at the inner level operates CCWP. Whenever a client endeavors to access services provided by the Cloud or administrations then its demand is either allowed or 26. dismissed by the CCWSP. Now, if the client gains admittance to the asked for administration or services, then within that service or domain the authorization is done using the CCWP. The MCSP is proposed to mitigate the 145-152 malicious insider attacks at IaaS in Cloud-based environments. Being a multi-level policy it is capable to detect as well as prevent the malicious insiders within the tenants of the clouds as well as within the organizations. Keywords: Cloud Security, Malicious Insider Attacks, Chinese Wall Policy, Clark Wilson Model. References:
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Singhal, “Information flow control in cloud computing,” in Proceedings of the 6th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing, 2012, pp. 1–7. [27] Q. Shen, X. Yang, X. Yu, P. Sun, Y. Yang, and Z. Wu, “Towards Data Isolation & Collaboration in Storage Cloud,” 2011. [28] S. Pramanik, V. Sankaranarayanan, and S. Upadhyaya, “Security policies to mitigate insider threat in the document control domain,” Proc. - Annu. Comput. Secur. Appl. Conf. ACSAC, pp. 304–313, 2004. [29] A. Sharifi and M. V. Tripunitara, “Least-restrictive enforcement of the Chinese wall security policy,” Proc. 18th ACM Symp. Access Control Model. Technol. - SACMAT ’13, p. 61, 2013. Authors: Kelly Steer, Lalit Garg, Vijay Prakash, Vipul Gupta Paper Title: SWOT Analysis of e-Marketing for e-Business Abstract e-businesses are becoming more and more popular with time, mainly due to the range of opportunities offered by the internet and other technologies. These new technologies have provided marketers with new ways of promoting their products or services to the public. This paper compares the traditional marketing media with the new ones and looks at marketing tools which can be used to create a successful e-marketing strategy. The paper also analyses different e-marketing mediums using Porter’s 5 forces and SWOT (strengths, weaknesses, opportunities and threats) analysis. Keywords—e-business, e-marketing, strategy, digital marketing, social media, Porter’s 5 forces, SWOT analysis
References:
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Index Terms: Clustering Techniques, K-Medoids, Packet Loss, Support Vector Machines
References:
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Sison and R.P.Medina. “An Improved Overlapping Clustering Algorithm to Detect Outlier.”Indonesian Journal of Electrical Engineering and Informatics (IJEEI),vol. 6, no.4, pp. 401-409,2018. 6. A.Abuarqoub, M.Hammoudeh, B.Adebisi, S.Jabbar, A. Bounceur and H.Al-Bashar. “Dynamic clustering and management of mobile wireless sensor networks.” Computer Networks,vol. 117, pp.62-75,2017. 7. A. Mohanty, S. Mahapatra and U. Bhanja. “Traffic congestion detection in a city using clustering techniques in VANETs.” Indonesian Journal of Electrical Engineering and Computer Science, vol. 13, no.3, pp. 884-891, 2019. 8. S. H. Yoon, J. W. Park, J. S. Park, Y. S. Oh, M. S. Kim. “Internet application traffic classification using fixed IP-port.” In Asia-Pacific Network Operations and Management Symposium, pp. 21-30, Springer, Berlin, Heidelberg. 2009. 9. A. W. Moore. and K. Papagiannaki. "Toward the Accurate Identification of Network Applications." In PAM, vol. 5, pp. 41-54, 2005. 10. F. Dehghani, N. Movahhedinia, M. R. Khayyambashi, and S. Kianian. "Real-time traffic classification based on statistical and payload content features." In Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on, pp. 1-4. IEEE, 2010. 11. T. T. Nguyen and G. Armitage. "A survey of techniques for internet traffic classification using machine learning." IEEE Communications Surveys & Tutorials 10, no. 4 (2008), pp. 56-76, 2008. 12. J. MacQueen. "Some methods for classification and analysis of multivariate observations." In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281-297, 1967. 13. S. Lloyd. "Least squares quantization in PCM." IEEE transactions on information theory, vol. 28, no. 2, pp. 129-137, 1982. 14. M. Hirvonen and J. P. Laulajainen. "Two-phased network traffic classification method for quality of service management." In Consumer Electronics, 2009. ISCE'09. IEEE 13th International Symposium on, pp. 962-966. IEEE, 2009. 15. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu. “An efficient k-means clustering algorithm: Analysis and implementation.” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 1, no.7, pp. 881-92, 2002. 16. J. Z. Xiao and L. Xiao. “Analysis and improvement for K-Means Algorithm.” In Applied Mechanics and Materials, vol. 52, pp. 1976- 1980, Trans Tech Publications, 2011. 17. P. Luarn, H. W. Lin, Y. P. Chiu, Y. L. Lee and P. C. Shyu. “The Categorising Characteristics of Facebook Pages: Using the K-Means Grouping Method.” International Journal of Business and Management, vol. 11, no.2 pp. 60, 2016 18. F. Hajikarami, M. Berenjkoub and M.H. Manshaei. “A modular two-layer system for accurate and fast traffic classification.” In 2014 11th International ISC Conference on Information Security and Cryptology, pp. 149-154, 2014. 19. S. Zander, T. Nguyen, and G. Armitage. "Automated traffic classification and application identification using machine learning." In Local Computer Networks, 2005, 30th Anniversary, The IEEE Conference on, pp. 250-257, 2005. 20. A. McGregor, M. Hall, P. Lorier and J. Brunskill. "Flow clustering using machine learning techniques." Passive and Active Network Measurement, pp. 205-214, 2004. 21. P. Cheeseman and J. Stutz. "Bayesian classification (autoclass): Theory and results in advances in knowledge discovery and data mining eds." Articles FALL, pp. 51, 1996. 22. J. Erman, A. Mahanti and M. Arlitt.“Byte me: a case for byte accuracy in traffic classification.”In Proceedingsof the 3rd annual ACM workshop on Mining network data, pp 35-38, 2007. 23. J. Erman, A. Mahanti, M. Arlitt, I. Cohen and C. Williamson. "Offline/realtime traffic classification using semi-supervised learning." Performance Evaluation, vol. 64, no. 9, pp. 1194-1213, 2007. 24. Y. Wang, Y. Xiang, J. Zhang, W. Zhou, G. Wei and L. T. Yang. "Internet traffic classification using constrained clustering." IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 11, pp. 2932-2943, 2014. 25. P. Wang, S. C. Lin and M. Luo. "A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs." In Services Computing (SCC), 2016 IEEE International Conference on, pp. 760-765. IEEE, 2016. 26. D. Achunala, M Sathiyanarayanan & B. Abubakar. “Traffic classification analysis using omnet++.” In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, pp. 417-422. Springer, Singapore 2018. 27. T. Karagiannis, A. Broido and M. Faloutsos. “Transport layer identification of P2P traffic.” In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, pp. 121-134, ACM, October 2004. 28. T. Karagiannis, K. Papagiannaki and M. Faloutsos. “BLINC: multilevel traffic classification in the dark.” In ACM SIGCOMM computer communication review, vol. 35, no. 4, pp. 229-240, ACM, August 2005. 29. P. Wang, S. C. Lin and M. Luo. "A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs." In Services Computing (SCC), 2016 IEEE International Conference on, pp. 760-765. IEEE, 2016. 30. K. Xu, Z. L. Zhang and S. Bhattacharyya. “Profiling internet backbone traffic: behavior models and applications.” In ACM SIGCOMM Computer Communication Review, vol. 35, no. 4, pp. 169-180, ACM, August 2005. 31. W. Zai-jian, Y.N. Dong, H. X. Shi, Y. Lingyun and T. Pingping. “Internet video traffic classification using QoS features.” In 2016 International Conference on Computing, Networking and Communications (ICNC), pp. 1-5, 2016. 32. L. Bin and T. Hao. “P2P Traffic Classification Using Semi-Supervised Learning.” In 2010 International Conference on Artificial Intelligence and Computational Intelligence, vol. 1, pp. 408-412, 2010. 33. D. Achunala, M Sathiyanarayanan and B. Abubakar. “Traffic classification analysis using omnet++.” In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, pp. 417-422. Springer, Singapore 2018. 34. J. Yan, X. Yun, Z. Wu, H. Luo, S. Zhang, S. Jin and Z. Zhang. “Online traffic classification based on co-training method.” In 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 391-397, 2012. 35. D. B. Shukla and G. S. Chandel. “An approach for classification of network traffic on semi-supervised data using clustering techniques”.In 2013 Nirma University International Conference on Engineering (NUiCONE, pp. 1-6. 2013. 36. H. S. Park and C. H. Jun. “A simple and fast algorithm for K-medoids clustering.” Expert systems with applications, vol. 36, no. 2, pp. 3336-41, 2009. 37. L. Wang. Support vector machines: theory and applications. Springer Science & Business Media; ed. 2005, vol. 177, ch. 1. 38. V. Vapnik, I. Guyon and T. Hastie. “Support vector machines.” Machine. Learning, vol.20, no. 3 pp. 273-97, 1995. 39. R. Bar-Yanai, M. Langberg, D. Peleg and L. Roditty. “Realtime classification for encrypted traffic.” In International Symposium on Experimental Algorithm, pp. 373-385, Springer, Berlin, Heidelberg, 2010. 40. T. M. Cover and P. E. Hart. “Nearest neighbor pattern classification.” IEEE transactions on information theory, vol. 13 no.1, pp. 21-7, 1967. Authors: Amandeep Bagga, Shiv Preet Squeeze Pack and Transfer Algorithm: A new over the top Compression Application for Seamless data Paper Title: Transfer over Wireless Network Abstract:Mobile data transfer is the backbone of Information Technology industry. It is now becoming the daily bread and butter earning means for most of the third world and developed countries. This paper proposed a text based compression algorithm which can be used with existing algorithms as an OTT ( over the top) service and improves their functionality. It can take advantage of increasing processing power of new generation CPUs like Snapdragon and Mediatech as well as bandwidth efficiency of modern mobile infrastructure. This algorithm is based on .Net Assemblies of Microsoft Visual Studio 2017 (Community Edition) and VB.Net (Visual Basic.Net) language. This algorithm can help in improving data transfer speed over wireless and mobile networks. It can also secure data transfer for vulnerable attacks (Man in the Middle Attack, DDOS Attack etc.). It will also help in reducing network congestion as it can pack more data per packet as compared to traditional algorithms. Keywords OTT, C#, .Net, Polymorphism, IPTV, Compression, Decompression, Key Pool, Encryption, Decryption, Lossless Compression, Lossy Compression, Winzip, Winrar, DDOS.
References:
30. [1] Ignacio Capurro et. el, "Efficient Sequential Compression of Multichannel Biomedical Signals", IEEE, pp 914-916, 2016 [2] Weigang Li,Yu Yao, "Accelerate Data Compression in File System",IEEE, pp 615 - 615, 2016 175-179 [3] Cornel Constantinescu ; Gero Schmidt, "Fast and Efficient Compression of Next Generation Sequencing Data", IEEE, pp 402-402, 2018 [4] Jin Zhou, Chiman Kwan, "A Hybrid Approach for Wind Tunnel Data Compression", IEEE, pp 435-435, 2018 [5] Weigang Li, "Optimize Genomics Data Compression with Hardware Accelerator", IEEE, pp 446-446, 2017 [6] Jie Chen et. el, "OCT: A Novel Opportunistic Compression and Transmission Approach for Private Car Trajectory Data", IEEE, pp 401- 401, 2018 [7] Ping Wang et. el, "A TR069 WAN management protocol for WIA-PA Wireless sensor Networks", IEEE, pp 1-4, 2016 [8] Sreekrishna Pandi et. el, "Reliable low latency wireless mesh networks — From Myth to reality", IEEE, pp 1-2, 2018 [9] G. Kalfas et. el, "Network planning for 802.11ad and MT-MAC 60 GHz fiber-wireless gigabit wireless local area networks over passive optical networks", IEEE, pp 206-220, 2016 [10] Sven Zehl et. el, "Hotspot slicer: Slicing virtualized home Wi-Fi networks for air-time guarantee and traffic isolation", IEEE, pp 1-3, 2017 [11] Mohamed Labraoui et. el, Opportunistic SDN-controlled wireless mesh network for mobile traffic offloading",IEEE, pp 1-7, 2017 [12] Bhaskar Prasad Rimai et. el, " Mobile data offloading in FiWi enhanced LTE-A heterogeneous networks",IEEE, pp 601-615, 2017 [13] Mate Akos TUNDIK et.el, " Access-independent Cloud-based Real-Time Translation Service for Voice Calls in Mobile Networks ",IEEE, pp 1-6, 2018 [14] Van-Giang Nguyen et. el, " SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey ",IEEE, pp 1567-1602, 2017 [15] Ricardo Martínez et. el, " Integrated SDN/NFV orchestration for the dynamic deployment of mobile virtual backhaul networks over a multilayer (packet/optical) aggregation infrastructure",IEEE, pp A135-A142, 2017 [16] Dawson Ladislaus Msongaleli, Kerem Kucuk " Reliability and cost-aware network upgrade for the next generation mobile networks ",IEEE, pp 496-501, 2017 [17] Siyu Zhou et. el, " Low-latency high-efficiency mobile fronthaul with TDM-PON (mobile-PON) ",IEEE, pp A20-A26, 2018 Authors: Manish Jha, Tarun Gulati, Vikas Mittal Paper Title: Proposing Classification Technique for Plant Disease Detection in Image Processing Abstract:The technique used for the processing of digital data obtained from pictures is identified as image processing. Plants and crops are ruining because of the excessive use of fertilizers and insecticides. The experts observe the plant disease with their naked eye and identify and detect the type of diseases plant is suffering from. In order to identify infections from input pictures, plant disease detection approach is implemented. An image processing approach is implemented in this research study. This approach is relied on the extraction of textural feature, segmentation and classification. The textural features are extracted from the picture with the help of GLCM algorithm. The input picture is segmented with the help of k-mean clustering algorithm. For classification, the KNN classification is used in this research. This leads to improve accuracy of detection and also leads to classify data into multiple classes. The results of the proposed algorithm are analyzed in terms of various 31. parameters accuracy, precision, recall and execution time. The accuracy of proposed algorithm is increased upto 180-183 10 to 15 percent.
Index Terms: GLCM , K-mean, KNN, SVM References:
1. Sanjay B. Patil “Leaf Disease Severity Measurement Using Image Processing”, International Journal of Engineering and Technology Vol.3 (5), 2011, pp. 297-301. 2. B. Bhanu, J. Peng, “Adaptive integrated image segmentation and object recognition”, IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol. 30, November 2000, pp. 427–441. 3. Keri Woods. ”Genetic Algorithms: Colour Image Segmentation Literature Review”, Vol. 81 (18), July 24, 2007. 4. S.Beucher, F. Meyer. “The morphological approach to segmentation: The watershed transform”, Mathematical Morphology Image Processing, E. R. Dougherty, Ed. New York Marcel Dekker, Vol. 12, January 1993, pp. 433–481. 5. Vijai Singh, Varsha, A. K. Misra, “Detection of unhealthy region of plant leaves using Image Processing and Genetic Algorithm” International Conference on Advances in Computer Engineering and Applications, March 2015. 6. Mrunalini R. Badnakhe and Prashant R. Deshmukh, “An Application of K-Means Clustering and Artificial Intelligence in Pattern Recognition for Crop Diseases”, International Conference on Advancements in Information Technology, Vol.20, 201. 7. Anand.H.Kulkarni, Ashwin Patil R. K., “Applying image processing technique to detect plant diseases”, International Journal of Modern Engineering Research, Vol.2(5), Sep-Oct. 2012, pp-3661-3664. 8. Anand K. Hase, Priyanka S. Aher, Sudeep K. Hase, “Detection, Categorization and suggestion to cure infected plants of Tomato and Grapes by using OpenCV framework for Andriod Environment,” 2nd International Conference for Convergence in Technology (I2CT), April 2017. 9. Kawaljit Kaur and Chetan Marwaha, “Analysis of Diseases in Fruits using Image Processing Techniques,” International Conference on Trends in Electronics and Informatics ICEI, May 2017. 10. Ranjith, Saheer Anas, Ibrahim Badhusha, Zaheema OT, Faseela K, Minnuja Shelly, “Cloud Based Automated Irrigation And Plant Leaf Disease Detection System Using An Android Application,” International Conference on Electronics, Communication and Aerospace Technology ICECA, April 2017. 11. Zia Ullah Khan, Tallha Akram, Syed Rameez Naqvi, Sajjad Ali Haider, Muhammad Kamran, Nazeer Muhammad, “Automatic Detection of Plant Diseases; Utilizing an Unsupervised Cascaded Design,” International conference of Electronics, Communication and Aerospace Technology (ICECA), April 2018. 12. S. Das, Tarun Gulati and Vikas Mittal “Histogram equalization techniques for contrast enhancement: A review,” International Journal of Computer application, 2015. 13. Chaitali G. Dhaware and K.H. Wanjale, “A Modern Approach for Plant Leaf Disease Classification which Depends on Leaf Image Processing,” International Conference on Computer Communication and Informatics (ICCCI-2017), Jan. 2017. 14. Davoud Ashourloo, Hossein Aghighi, Ali Akbar Matkan, Mohammad Reza Mobasheri, and Amir Moeini Rad, “An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, August 2016. Authors: Bharatkumar Shamrao Patil , Laxman M. Waghmare, M. D. Uplane Implementation of SMC Control Action with Pi Sliding Surface for Non Linear Plant Along with Paper Title: Changing Set Point
Abstract:A discrete time sliding mode controller (DSMC) is proposed for higher solicitation not withstanding defer time (HOPDT) frames. As portion of structure states and botch, a sliding mode surface is selected and the tuning parameters of the sliding mode controller are resolved using overpowering post circumstance scheme. The control object for "ball in a barrel" is to handle the velocity of a fan blowing air into a chamber to keep a ball suspended in the barrel at a certain predestined position. The DSMC is attempted to coordinate the ball's position subsequently. But skillfully clear, this is a troublesome control issue due to the non-direct ramifications for the ball and the confounding material science regulating its lead. The DSMC is attempted to coordinate the ball's position
subsequently. But skillfully clear, this is a troublesome control issue due to the non-direct ramifications for the ball and the confounding material science regulating its lead. The generation and experimentation results exhibit that the proposed methodology ensures needed after components. The flawlessness of current proposed framework is it
stipends following of advancement continuously set point. This device to likely investigate a standard PID
controller and DSMC controller. The results indicate notable separations in controllers ' implementation
characteristics. Index Terms: PID controller, SMC, DSMC, HOPDT
References:
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2. Levant, Sliding order and sliding accuracy in sliding mode control, International Journal of Control 58 (6) (1993) 1247–1263. 3. Edwards, S. Spurgeon, Sliding Mode Control: Theory and Applications, CRC Press, Boca Raton, FL, 1998.
4. Levant, Universal single-input-single-output sliding-mode controllers with finite-time convergence, IEEE Transactions on Automatic Control 46 (9) (2001).J.S. Orr, Y.B. Shtessel / Journal of the Franklin Institute 349 (2012) 476–492. 5. Klumpp, Apollo Lunar Descent Guidance, NASA R-695, 1971.
6. F. Bennett, Apollo Experience Report—Mission Planning For Lunar Module Descent and Ascent, NASA TN D-6846, 1972. 7. F. Dodge, H. Abramson (Eds.), Analytical Representation of Lateral Sloshing by Equivalent Mechanical Models, The Dynamic Behavior of Liquids in Moving Containers, NASA SP-106, 1966, pp. 199–223. 8. W. Widnall, Lunar Module Digital Autopilot, Journal of Spacecraft 8 (1) (1971) 56–62.
9. E. Kubiak, Phase Plane Logic Design Principles, NASA Memorandum EH2-86M-149, May 1986. 10. Hall, Y. Shtessel, Sliding mode observer-based control for a reusable launch vehicle, Journal of Guidance, Control, and Dynamics 29 (6) (2006) 1315–1328.
11. J. Orr, Y. Shtessel, Robust control of lunar spacecraft powered descent using a second-order sliding mode technique, in: Proceedings of the 2008 AIAA Guidance, Navigation, and Control Conference, Honolulu, HI.
12. H. Khalil, in: Nonlinear Systems, third ed, Prentice-Hall, Upper Saddle River, NJ, 2002. 13. Y. Shtessel, J. Moreno, F. Plestan, L. Fridman, A. Poznyak, Super-twisting adaptive sliding mode control: a Lyapunov design, in: Proceedings of the Conference on Decision and Control, Atlanta, GA, December, 2010. 14. F. Plestan, Y. Shtessel, V. Bregeault, A. Poznyak, New methodologies for adaptive sliding mode control, International Journal of Control 32. 83 (9) (2010) 1907–1919. 15. J. Orr, Y. Shtessel, Robust lunar spacecraft autopilot design using high-order sliding mode control, in: Proceedings of the 2009 AIAA 184-188 Guidance, Navigation, and Control Conference, Chicago, IL.
Nor Hafizah Abdullah, Mohammad Rezal Hamzah, SuffianHadiAyub, Sharipah Nur Mursalina Syed 33. Authors: Azmy, Zanirah Wahab, Hishamuddin Salim, Wan Abdul Hayyi Wan Omar An Experimental Analysis on the Corporate Identity of Institutes of Higher Learning in the Malaysian Paper Title: East Coast Region Vis-À-Vis Market Conditions in Empowering Self-Sustainability Abstract: The liberalization of the education industry has exposed the institutes of higher learning (IHL) in Malaysia to financial challenges. Without good financial standing, public institutions will rely on government funding. Ostensibly, this contradicts with the government’s aspiration to make universities self-sufficient. With stiff competition from private institutes of higher learning, IHL need to be prepared at the forefront level. The corporate identity itself is the entrance to the world of higher learning and it is in this uniqueness, it will be able to distinguish itself from competitors. Effective corporate identity representation of the IHL is very important for the sustainability of the institution. This study employed in-depth interview with key personnel and decision makers of the IHL. The IHL in the east coast region of Malaysia has been chosen as the location for the research due to its rising prominence as an education hub especially with the establishment of East Coast Economic Region (ECER). The market conditions elements in the Corporate Identity Model developed by Melewar and Jenkins in 2002 has been used as the primary research framework. The result highlights the readiness of each IHL in the east coast region in competing with other well established IHL all over Malaysia despite the increase of financial challenges. Some of the strategies used to promote and establish their corporate identities are proven to be efficient and cost effective which could be emulated especially by new IHL.
Keywords:Communication, corporate identity, market conditions, IHL 189-193
References: 1. Abdullah, Z., Shahrina, Nordin, M., & Abdul Aziz, Y. (2013). Building a unique online corporate identity. Marketing Intelligence & Planning, 31(5), 451-471. 2. Balmer, J.M.T. (1995) Corporate Branding And Connoisseurship, Journal Of General Management, Vol. 21 No. 1, Pp.24-46. 3. Fombrun, C. and Shanley, M (1990), What’s In A Name? Reputation Building and Corporate Strategy, Academy Of Management Journal, Vol. 33 No. 2, pp. 233-58. 4. Ayub, S. H., Manaf, N. A., & Hamzah, M. R. (2014). Leadership: Communicating strategically in the 21st century. Procedia-Social and Behavioral Sciences, 155, 502-506. 5. Melewar, T.C., and Jenkins, E. (2002), Defining The Corporate Identity Concept, Corporate Reputation Review, Vol. 1 No. 1, Pp 76-94. 6. Melewar, T.C., and Karaosmanoglu, Elif (2005), Seven Dimensions Of Corporate Identity: A Categorisation From The Practitioners’ Perspectives. European Journal of Marketing, Vol. 40, No. 7/8, 2006, pp. 846-869. 7. Mohamad, B. (2007), Relationship Between Corporate Identity and Corporate Reputation: A Case of a Malaysian Higher Education Sector, JurnalManajemenPemasaran, Vol. 2, No. 2, pp.81-89. 8. Schmidt, K. (Ed.) (1995), The Quest For Identity: Strategies, Methods, And Examples, Cassell,London. 9. van Riel, C.B.M. and Balmer, J.M.T (1997), Corporate Identity: The Concept, Its Measurement And Management, European Journal Of Marketing, Vol. 31 No. 5/6, Pp.40-55. Authors: NiracharapaTongdhamachart, Loni Berry Paper Title: An Experimental Analysis of the Royal Project on Highland Abstract: Theaimsofthisresearchweretoexamineandstudy DoiBo Highland Agricultural Development Station under the Royal Initiative Project of Her Majesty QueenSirikit of Thailand. The project was located on highland in Chiangrai province, northern Thailand. Doi Bo where “Lahu ethnic hill-tribe” has been living for a long time. The area in the past faced slash and burnt farming, drug trafficking and poverty. Therefore, toachievethe objectives of the study, acasestudywasutilized. Qualitative study was employed anddescriptiveanalysiswasusedto describe the result ofthestudy. Sufficiencyeconomy theorycreatedby the late KingRamaIXof Thailand and SWOT were used to analyze the study.Primary and secondary data werecollectedbased onsite observations,and extensive literaturereviews. In-depthinterviews, combinedwithquestionnaires,werealsoconductedtoobtain opinions. ThefindingsrevealedthatDoi Bo communityhadbetter socio-economic communityduetobodies of knowledgeandskill enhancementfrom the support of the agricultural development station. Strong communityengagement and related government agencieswerekeydrivershelpingaccomplish the Royalproject.However, disruptive technology such as augmented reality and virtual reality were recommended to further promote the area as an agricultural demonstration plant and ecotourism.
34. Keywords: the Royal project, augmented reality, virtual reality, sufficiency economy, disruptive technology 194-198
References: 1. I. and Isabella R.(2016). E Toursim for Socio-Economic Development. Symphonya Emerging Issues in Management, (1)75. 2. Department of National Parks, Wildlife and Plant conservation.(2017). Annual report. Chiangrai, Thailand. 3. Samira H. &Alireza E.(2011). Digital economy and tourism impacts, influences and challenges. Elsevier, Procedia, Social & Behavioral Sciences, 19)2011), pp.308-316. 4. NiracharapaT.(2016). Challenges of Indonesian animation at the global market. Actual Problems of Economics Journal, 7(181), pp 53-58. 5. NiracharapaT.(2017).Identity of Yafu community. Bangkok:National Research Council of Thailand. 6. The Nong Hoi Station.(2012). Retrived September 5, 2018 from https://www.theblondtravels.com/mon-cham-nong-hoi-project-chiang- mai. 7. Pengpinit, T et al.(2011). Success indicators of sufficient framing of local philosophers and multilateral in the Northeast. SDU research Journal of Humanities and Social Sciences, 7(2), 91-102. 8. Piboonsarawut, P.(2006). Sufficiency economy philosophy according to His Majesty’s initiative. Ramkhamhaeng University, Thailand. 9. The Royal Agricultural Station Inthanon.(2015). Retrieved August 8,2018 from 10. http://www.royal-inthanon.com. 11. Royal Agricultural station Angkhang.(2016). Retrieved August 25,2018 fromhttp://www.angkhangstation.com. 12. SripenDabphet.(2016). The Key stakeholders in the Implementation of sustainable Royal projects in two rural towns of Thailand. 13. SunatChutinitaranond(2017).The foundation for the promotion of supplementary occupation and related techniques of her Majesty Queen Sirikit and the enhancement of human security in Northeast Thailand during the 1970s-1980s. Thailand, Chulalongkorn University Press. 14. SupathanishT.(2015). OTOP product champion marketing strategy model which are selected the best OPC 5 start product approach of Chaing Mai Province: The fabric and apparel community, Thailand. Review of Integrative Business & Economics Research, 4)4), pp. 259- 276. 15. Suwanee Khamman.(2016).Overviews of Social Protection: Lesson Learned from Thailand. Bangkok, Thailand:NESDB 16. SuvitMaesincee. (2016), Thailand 4.0”. Thriving in the 21st Century through security, prosperity and sustainability conference, 20 August 2016 17. Thailand Sustainable Development Foundation(2016). The Royal Projects. Retrieved August 5, 2018 from www.tsdf.or.th) 18. ThanawutPimki.(2014). The Practical use of the royal sufficiency economy with community enterprise in Chanthaburi Province. SDU researchJournal of Humanitiesand Social Sciences, 10(11), pp. 1-21. 19. Thai Embassy (2010). The Queen and Some Examples of Her Royal Projects. Retrieved August 10, 2018 from https://www.thaiembassy.sg 20. Thailand Sustainable Development Foundation.(2016). Royal Projects of the King. Newsletter, Bangkok. 21. VipadaSitabutr.(2017).Thai entrepreneur and community-based enterprises’OTOP branded handicraft export performance: a SEM analysis. SAGE Journal, DOI 10.1177/2,pp.1-15. Authors: Fathiyyah Abu Bakar, Zakiyah Sharif, Zaimah Abdullah Paper Title: Managing University-Community Engagement (UCE): The Case of UUM Abstract: Nowadays, the debate on university community engagement (UCE) has received a huge attention by many parties and can be considered as high profile issue over the academic world. The UCE is considered as one of the university’s mechanisms in discharging its social responsibility to its nearby community. University is highly recommended to embed community engagement responsibility into the university’s policy as part of theitscommitment to improve the well-being of society surrounding the campus. The objectives of this study are to describe the process of managing UCE activities at Universiti Utara Malaysia (UUM) and to identifyfactors, challenges and benefits of engaging in the activities. To achieve these objectives, twelve respondents have been interviewed and a number of UCE documents have been reviewed. The findings of this study reveal that the social activities at UUM excel beyond the philanthropic activities and have been progressed from short term program towards the long term engagement programs. The UCE activities have shown a good progress in term of its planning and implementation. However, UUM may need to take a consideration on how to overcome the challenges they may face such as lack of financial resources and inefficiency in managing UCE As such, the findings of this study provide a new insight of the management process for a highereducation institution to engage with the good UCE programs. However, one size does not fit all. Therefore, a future study could be conducted using multiple case studies that may help to gain more understanding of the university's commitment towards UCE programs.
Keywords: university-community engagement, case study, university, management process
References:
1. About APUCEN.( 2013). APUCEN Bulletin, 7. 2. Abu Bakar, F., & Md Yusof, M. A. (2014). CSR practices at Bank Islam Malaysia Berhad (BIMB): Managing CSR fund. Issues in Social 35. & Environmental Accounting, 8(1), 1-22. 3. Creswell, J. W. (2007). Qualitative inquiry & research design: Choosing among five approaches (2nd ed.). Thousand Oaks: Sage Publications, Inc. 199-205 4. Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation: concepts, evidence, and implication. Academy of Management Journal, 20(1), 65-91. 5. Eisenhardt, K. M. (1989). Building Theories from Case Study Research.Academy of Management Review, 14(4), 532-550. 6. Eligibility Procedures and Accreditation Standards for Business Accreditation.(2016). Association to Advance Collegiate Schools of Business (AACSB). 7. Freeman, R. E. (1984). Strategic Management : A Stakeholder Approach. Boston: Pitman Publishing Inc. 8. Hollister, R. M., Pollock, J. P., Gearan, M., Reid, J., Stroud, S., & Babcock, E. (2012). The Talloires network: A global coalition of engaged universities. JOurnal of Higher Education and Engagement, 16(4), 81-103. 9. Maon, F., Lindgreen, A., & Swaen, V. (2009). Designing and implementing corporate social responsibility: An integrative framework grounded in theory and practice. Journal of Business Ethics, 87, 71-89. 10. Merriam, S. B. (1988). Case study research in education. San Francisco: Jossey-Bass Inc. 11. Miles, M. B., & Huberman, A. M. (1994).Qualitative data analysis: an expanded sourcebook (2nd ed.). Thousand Oaks: Sage Publications, Inc. 12. Mtawa, N. N., Fongwa, S. N., & Wangenge-Ouma, G. (2016). The scholarship of university-community engagement: Interrogating Boyer's model. International Journal of Educational Development, 49, 126-133. 13. Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park: Sage Publications, Inc. 14. Shannon, J., & Wang, T. R. (2010). A model for university-community engagement: Continuing education's role as convener. The Journal of Continuing Higher Education, 58(2), 108-112. 15. Shiel, C., Leal Filho, W., do Paco, A., & Brandli, L. (2016).Evaluating the engagement of universities in capacity building for sustainable development in local communities.Evaluation & Program Planning, 54, 123-134. 16. University-community engagement program: Rural single mother entrepreneurship program (RusMEP). (2015, December).APUCEN Bulletin, 18-19. 17. University-community engagement programme: Rural single mothers entrepreneurship programme (RusMEP). (2015). APUCEN Bulletin, 18-19. 18. Weerts, D. J., & Sandmann, L. R. (2008). Building a two-way street: Challenges and opportunities for community engagement at research universities.The Review of Higher Education, 32(1), 73-106. 19. Winter, A., Wiseman, J., & Muirhead, B. (2006). University-community engagement in Australia: Practice, policy and public good. Education, Citizenship and Social Justice, 1(3), 211-230. Authors: Syazwani Mahsal Khan,Norsiah Abdul Hamid, Sabrina Mohd Rashid The Information Processing on Persuasion Towards Young Consumer Decision Making In Music Paper Title: Television Advertising: Experts View Abstract: Advertising should be persuasive in nature as to make sales from consumers. It becomes increasingly popular as more people who are working on their own business and companies turn to advertising as a platform for getting their products or services known by consumers. The aim of this study is to discover how information processing of music in advertisement content affect young consumers decision making towards the advertised product or services. The Elaboration Likelihood Model (ELM) is chosen as a base to explain persuasive information processing of advertisement towards young consumer decision making. The methodology employed to carry out the study was through an in-depth interviewwith experts based on snowball sampling. The experts consists of academicians, advertising practitioners and musicians. The interview was carried out by using semi- structured question.Thematic analysis reveals two themes emerged from this study, which is consumers judgement and consumers stay updated behaviour. The findings in this study showed that information processing of young consumers towards advertisement content through the mixture of music can affect their decision making mood related products or services advertisement. This study contributed to our understanding of how young consumer view, hear and process the information of the advertisement content is important in terms of making them engage with upcoming products or services in the market as well as it helps the advertiser and marketer to gain their profit effectively.
Keywords: Advertising,Information Processing, ELM, Music, Consumer Decision Making
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Interviewing as a Data Collection Method: A Critical Review. English Linguistics Research, 3(1), 39–45. https://doi.org/10.5430/elr.v3n1p39 36. 8. Bloemer, J., Brijs, K., & Kasper, H. (2009). The CoO-ELM model: A theoretical framework for the cognitive processes underlying country of origin-effects. European Journal of Marketing, 43(1/2), 62–89. 206-213 9. Bögel, P. M. (2015). Processing of CSR communication: insights from the ELM. Corporate Communications: An International Journal, 20(2), 128–143. 10. Bracken, C. C. (2006). Perceived source credibility of local television news: The impact of television form and presence. Journal of Broadcasting and Electronic Media, 50(4),723–741. https://doi.org/10.1207/s15506878jobem5004_9 11. Broekemier, G., Marquardt, R., Gentry, J. W., Broekemier, G., & Marquardt, R. (2008). An exploration of happy / sad and liked / disliked music effects on shopping intentions in a women ’ s clothing store service setting. 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Theory and Practice in Language Studies, 3(2), 254–262. https://doi.org/10.4304/tpls.3.2.254-262 Authors: Bakri Mat, Siti Darwinda Mohamed Pero, Ratnaria Wahid, Babayo Sule Paper Title: Cybersecurity and Digital Economy in Malaysia: Trusted Law for Customer and Enterprise Protection Abstract: Cybersecurity is one of the recent areas of concern for national and global security in the 21st century. It is a required segment of security at individual, enterprise, national and international level both at public and private sector. The current economy in emerging economies is shifting towards digital activities and Malaysia is one of these economies. For a digital economy to flourish, there is a need for a secured cyberspace which is the essence of cybersecurity. This work examined the impact of a secured cyberspace or the role of cybersecurity in promoting digital economy in Malaysia through a trusted law for customers and enterprise. The issue of concern is the risk of vulnerability in cyberspace which means there is an existence of threats to cybersecurity. The work used a qualitative method of data collection and analysis. Data were collected from both primary and secondary sources and were analysed using content analysis where thematic analytical interpretations was used. The paper discovered that, the cybersecurity in Malaysia is less vulnerable and is satisfactory but still there exist threats and vulnerabilities which can affect digital trust and digital business. Therefore, it is recommended that the laws on digital trust and cybersecurity should be consolidated and public awareness should be intensified to minimise the risk and to prepare for future unforeseen.
Keywords: Cybersecurity, Digital Economy, Enterprise, Law, Risk.
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Authors: Siti Farhanah Hasnan, Razamin Ramli, Mohd Noor Abdul Hamid, Maznah Mat Kasim Paper Title: Implication of National Culture in Firms’ Innovative Capabilities From Malaysian Perspective 38. Abstract: Innovation is the key characteristic of any developed nations. From an organizational perspective, 221-228 innovation allows firms to remain relevant and stay competitive in the market. Hence, fostering innovation has become the main agenda for many organizations all over the world. With globalization and technological advancement, more organizations are becoming multinational and conducting their businesses across borders. The understanding of local culture is essential for these firms in their quests for innovation. This paper examines the implications of national culture on firms’ innovation capabilities in the context of Malaysia. The discussion adopts Hofstede dimension of national culture and focuses mainly on the leadership and effective strategic communication in innovation development. A framework is developed to explain the context, enablers and barriers to innovation.
Keywords: National culture, innovation, strategic communication, Leadership, Hofstede, Malaysia
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The Relationship between Gender and Ethnicity upon Hofstede’s Cultural Dimensions among Sabah Ethnicities. IOSR Journal of Business and Management, 10(6). 76. Kaasa, A. (2013). Culture as a Possible Factor of Innovation: Evidence from the European Union and Neighbouring Countries. 77. Ismail, M., & Lu, H. S. (2014). Cultural values and career goals of the millennial generation: An integrated conceptual framework. Journal of International Management Studies, 9(1), 38-49. Authors: Muhammad Asir, Rahim Darma, Muhammad Arsyad, Mahyuddin An Experimental Analysis of the Role of Stakeholders in the Cocoa Commodity Supply Chain in West Paper Title: Sulawesi, Indonesia Abstract: The quality and continuity of cocoa seed supply is determined by stakeholders in the cocoa supply 39. chain. In general, the activity of cocoa bean production has not been efficient enough to compete as raw material for domestic industry and export of seeds. So that cocoa beans in West Sulawesi Province can compete and 229-234 increase the income of cocoa farmers, it is necessary to increase the role of stakeholders in the cocoa supply chain network, especially those that support the improvement of cocoa bean productivity. The research objective is to analyze the role of stakeholders in cocoa seed supply chain through survey method by identifying the supply chain of cocoa beans. The results showed that farmer groups, marketing institutions (large traders, and exporters) still lacked a role in the supply chain of cocoa commodities. The expected role of marketing institutions was partnerships, especially price guarantees and support for increasing the productivity of farmers' gardens. Collector traders are considered to be very instrumental because the cooperation in the form of loans and ready to buy cocoa beans from farmers although the amount is small, but the prices received by farmers from the collector tends to be low. Formal institutions or stakeholders at the farm level in the form of farmer groups have not functioned optimally. Partnership between farmers with institutions or stakeholders in supply chain networks such as wholesalers and exporters or industries has not yet been running, so farmers as key stakeholders do not get price and capital certainty to maximize the production.
Keywords:Cocoa, Stakeholders, farmers
References: 1. Bud L S, Maa’rifM S, Sailah I, Raharja 2009 Strategi Pemilihan Model Kelembagaan dan Kelayakan Finansial Agroindustri Wijen (Strategies for Selection of Sesame Agro-industrial Financial Appropriateness and Model) Jurnal Teknologi Industri Pertanian19(2) pp. 56- 63 2. Nasution M 2002 Pengembangan Kelembagaan Koperasi Pedesaan Untuk Agroindustri (Institutional Development of Rural Cooperatives for Agro-industries), Bogor: IPB Press 3. Dentoni D, Bitzer V and Pascucci S 2016 Cross-Sector Partnerships and the Co-creation of Dynamic Capabilities for Stakeholder Orientation. Management Studies Group, Wageningen University, hollandseweg, Netherlands 135 35-53 4. Chopra S and Meidl P 2007 Supply Chain Management: Strategy, Planning and Operations 3rd edition Pearson Education. International, Upper Saddle River, NY. Prentice-Hall 5. Christopher M 2005 Logistic and Supply Chain Managemen, Creating Value-Adding Networks Prentice Hall, Harlow 6. Hadiguna R A 2010 Perancangan Sistem Penunjang Keputusan Rantai Pasok dan Penilaian Risiko Mutu pada Agroindustri Minyak Sawit Kasar. Disertasi, IPB, Bogor 7. Hidayat S 2012 Model Penyeimbang Nilai Tambah Berdasarkan Tingkat Risiko pada Rantai Pasok Minyak Sawit. IPB, Bogor 8. Eriyatno 2003 Ilmu Sistem: Meningkatkan Mutu dan Efektifitas Manajemen. Bogor: IPB Press 9. Astuti R 2012 Pengembangan Rantai Pasok Buah Manggis. Disertasi.IPB, Bogor 10. Murwito I S and Mulyati S2013 Needs Assesment of Cocoa Business Development Using The Value Chain Approach &National Movement of Cocoa Production and Quality Improvement (GERNAS KAKAO) Case study on Sikka Regency,East Nusa Tenggara. [Internet] [cited 2018 Nov 17] Available from: https://media.neliti.com/media/publications/255-EN-needs-assesment-of-cocoa-business- development-using-the-value-chain-approach-nat.pdf 11. Carlos A, Doyle B, Andrew W S, Chakib J and Sergio M 2009 Agro-Industries For Development. The Food and Agriculture Organization of the United Nations and The United Nations Industrial Development Organization by arrangement with CAB International. [Internet] [cited 2018 Oct 30] Available from:http://www.fao.org/3/a-i0157e.pdf 12. Aini H, Syamsun M and Setiawan A 2015 Risiko rantai pasok kakao di Indonesia dengan metode analytic network process dan failure mode effect analysis terintegrasi. Jurnal Manajemen & Agribisnis11(3) 209–219 13. Primadita R and Krisnamurthi B 2014 Analisis rantai pasok Biji Kakao di Kecamatan Kalukku Kabupaten Mamuju (kasus : petani program Nestle Cocoa Plan PISAgro). Undergraduate thesis, Departemen Agribisnis, Fakultas Ekonomi Dan Manajemen, Institut Pertanian Bogor. [Internet] [cited 2018 Dec 27] Available from: https://repository.ipb.ac.id/bitstream/handle/123456789/71049/H14rpr.pdf?sequence=1&isAllowed=y 14. Arsyad M 2010 The Dynamis of Cocoa Smallholders In Indonesia: An Application of Path Analysis for Poverty Reduction. Ph.D. Thesis, Ryukoku University, Kyoto. 15. Hasibuan M 2015 Peran organisasi petani dalam mengoptimalkan kinerja rantai pasok dan pembentukan nilai tambah kakao. Balai penelitian tanaman industri, Indonesia 2(1) 1-12 16. Herawati R A and Tinaprillan N 2015 Ferpormance and Efficiency of Cocoa Beans Supply Chain in Pasaman, West Sumatera 17. Rafael C, Angel G and Xavier F-S 2008An Experts Survey on Sustainability Across Twenty-Seven Extensive European Systems of Grassland ManagementEnvironmental Management42190–199 18. Sugiyono 2013 Metode Penelitian Pendidikan (Pendekatan kualitatif, Kuantitatif, dan R&D). (Bandung: Alfabeta, 2013), p.117 Authors: Sonal Patil, K. N. Jariwala Paper Title: Adaptive Keypoint Selection for Detection of Tampering in Images and Videos Abstract:Tampering with images and videos for duplicating content and copyright infringement has become a very common problem for original content producers. The main issue with duplication and forgery is that, due to the advancement of forging techniques, it is being increasingly difficult in terms of both computational power and algorithmic complexity to detect and trace the forgeries with good level of accuracy. In this paper, we propose an adaptive keypoint based approach to detect the presence of forgery in images. Our approach is independent of the input dataset, and provides good level of accuracy for forgery detection. The system is tested on REWIND dataset, and an accuracy of more than 85% was observed. Our approach can be further extended to incorporate machine learning in order to improve the accuracy.
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Keywords:Critical ,event, knowledge, mining, sentiment,tweet
References:
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Authors: Vedanti Chintawar, Jignyasa Sanghavi Paper Title: Improving Feature Selection Capabilities in Skin Disease Detection System Abstract:Feature extraction is the process of description of the input imagery into a fixed set of values. For a good feature extraction algorithm these values are sufficient in order to describe the entire properties of the image under test. There are many kind of features which can be extracted from the image, these features vary from color features, shape features, to morphological and texture features. Feature extraction is usually application dependent, and allows the application designers to incorporate various kinds of descriptors for the image under test. An optimum feature extraction system is the one which can accurately and uniquely identify each image separately via uniqueness in the feature vector. In this paper, we analyze various feature extraction techniques and identify the best features suited for the application of skin disease detection systems, and also provide some acute observations on how these techniques can be improved to further optimize the accuracy of identification.
Keywords:Accuracy, color, feature, morphological, shape,texture, uniquely
References:
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Suraj Saklani, Shubhangi Neware Authors: Paper Title: An Experimental Analysis of Churn Prediction Techniques on Real Time Datasets Abstract:Churn prediction is an indicative of the loyalty with which the customer is attached to a particular
provider. Usually churn or customer churn is a value in percentage, and can be used by various service providers to make sure that the customer stays with them for a longer duration. Based on this value, companies device
customer specific plans for higher churning customers, and plans for the customers which are about to opt for another service provider. In this paper, we review and study multiple techniques for customer churn prediction and their application areas, in order to evaluate the techniques and form a basis on which techniques can be used for
which particular type of application. Machine learning approaches are generally preferred over traditional ones, as
they allow the service providers to learn about the customer behaviour pattern over a long span of customer service
usage. We conclude the paper which some suggestions on how churn prediction can be improved for better
optimization of the developed system.
Keywords:Churn, prediction, loyalty, machine, learning
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This area has developed new techniques like Internet-of-Thing, computer vision and many more. Raspberry Pi 3 is the first 64bit version and they have inbuilt features of Bluetooth and Wi-Fi and the size is like a debit card was used at this system and the camera is connected by the Raspberry Pi 3. Basically, In IOT the system is connected and controlled the gadgets to a central hub or the “gateways”. The system is controlled by the User interface by the medium of Tablets, Desktop computer, the mobile application either a web Interface. In this study, blend of IOT with Computer vision for detecting the people that are visible to the cameras and it is helpful for us to identify that specific person. Afterward, human identity in the image they detected the face and captured it as an image and sent the image that contains the face and appearance time to the mobile or tablets by using web gateways in the form of TEXT message. At that time the user can check the image and the details verifying the person and they have the control to permit or denied there entrains. Keywords: Internet-of-Thing (IOT), Computer Vision, Face Detection, Raspberry PI 3, Web Gateways.
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[20] Mrs. Paul Jasmin Rani et al, “Voice Controlled Home Automation System Using Natural Language Processing (NLP) And Internet of Things (IoT),” International Conference on Science Technology Engineering & Management (ICONSTEM), IEEE, Vol.3rd, pp.368-373, 2017. [21] Devendra Sakharkar et al, “A Study on Various Face Detection Techniques in Real Time Video Environment,” International Conference on Pervasive Computing (ICPC), IEEE, 2015. [22] Aryuanto Soetedjo et al, “Implementation of Face Detection and Tracking on A Low Cost Embedded System Using Fusion Technique,” International Conference on Computer Science & Education (ICCSE 2016), Nagoya University, Japan, IEEE, Vol.11th, pp.209-213, August 23-25, 2016. [23] Neethu A et al, “People Count Estimation Using Hybrid Face Detection Method,” International Conference on Information Science (ICIS), IEEE, pp.144-148, 2016. [24] Yassin Kortli et al, “A novel face detection approach using local binary pattern histogram and support vector machine,” in Proc. IEEE, 2018. Authors: Palash tiwari, Ramchand Hablani Paper Title: Techniques for Outfit Composition for Fashion Trend Analysis Abstract:Outfit composition is a trending area in fashion industry these days due to the fact that the system can provide intelligent suggestions on which kind of clothing to wear, and analyse trends based on the user's interests. In this paper, we analyse various methods which are used for outfit composition detection and provide a significant review about these methods in order to help researchers to aggregatively study and analyse the trends followed by these methods for prediction of the outfit composition. Standard datasets are also described in this text, which will be helpful in many fields like analysis of outfits, colour-based predictions, and others. We conclude this text with some interesting observations in the field and suggest some further enhancements as well based on our study.
Keywords:Composition, Outfit, prediction, trend analysis.
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The utilization of mmr, decent variety basedr eranking for reordering archives and delivering outlines. In ACM SIGIR, 1998. Authors: Devika Radhakrishnan, Shubhangi Neware An Experimental Analysis of Various Algorithms for Classification in Educational Data Mining with Paper Title: the help of LMS Abstract: Data in educational institutions are developing continuously and rapidly along these lines there is a need of advancement, that this large and excessive amount of data is to be converted into helpful data and need of implementing data mining technique. Educational data mining is concerned with the application of various statistical analysis, data mining technique, machine learning which will be helpful for school, colleges and universities Educational data mining is the zone of science where various kinds of techniques are being created for analysis, looking and investigating data and this will be valuable for better comprehension for the further studies and the settings they learned. As the data is predefined, the classification of object in the view is data mining and information management procedure is utilized as a part of comparable data questions altogether. Decision Tree is a very valuable and well-known classification method that helps in decision making based on the possible consequences, that can be event outcomes, resource costs or utility. It contains conditional control statements. One explanation behind its noticeability comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification.One application behind its noticeability comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification. We will likewise be thinking about how these decision tree calculations are done and are made appropriate and valuable for educational data mining and which one isideal.
Keywords:Educational data mining (EDM), Classification, Dropout Prediction, decision tree, learning management system.
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Authors: Manasvi Gurnaney, Shubhangi Neware Paper Title: Improving Obsolescence Detection Accuracy using Recurrent Neural Networks Abstract: Forecasting a product's obsolescence depends on a multitude of factors which can be both technical and non-technical aspects of the product under study. The predictions are usually an approximate of the obsolescence and might not reflect the true nature of the product. Thus, researchers from various fields including market 47. 277-281 research, technology, public perception and others unite together in order to device a model which can be used for efficient obsolescence detection of products. In this paper, we propose an algorithm for effective obsolescence detection with the help of integrated datasets and a recurrent neural network (RNN). The RNN is used so that the effectiveness of prediction can be improved, and it is found that RNN is better when compared with other standard prediction classifiers..
Keywords: Obsolescence, recurrent neural network, perception, prediction
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Index Terms: ANFIS, Forest cover, fuzzy, neural, prediction..
References:
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Keywords:delay, energy, machine learning, P2P, QoS.
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Abstract:: In our previous work, we have presented a pioneering step in designing Bi-Gram based decoder for 50. SMS Lingo. In last few years, a significant increment in both the computational power, storage capacity of computers, and the availability of large amount of bilingual data, have made possible for Statistical Machine Translation (SMT) to become a feasible and practical technology. Natural Language Processing is capable of converting almost every machine to a human being by applying artificial intelligence and smart decision making features. In our previous work we employed Bi-Gram Language Model (LM) with a SMT decoder through which a sentence written with short forms in an SMS is translated into long form sentence. This helps users to combine multiple languages with larger vocabulary and is a useful tool for small devices like mobile phones. Since then technology have moved forward with rapid pace and NLP has become the inseparable tool for human machine interface. The IOT platform is taking the world to a place of humans as living machines and appliances as just non living smart machines. In this paper we are discussing some state-of-the-art N-Gram based decoding techniques for text to emotion extraction. The proposed work in the paper is based on outcomes, methods and generated results from various algorithms suggested in different research papers. We have taken the research work one step further and integrated the results with electronic systems on IOT platform which can be controlled and manipulated with human emotions. There are four basic methods to detect emotions from text: Keyword based detection, learning-based detection, lexical affinity method, hybrid detection. From the extracted messages we plan to develop an emotion corpus and then use the time stamped information of mood swing to control devices and digital environment as per expected emotions or mood. So, we are suggesting N-Gram based Smart Living Machines on IOT platform. The emotional intensity of an individual for given circumstances varies from person to person and even time to time for the same individual, hence personalized time stamped corpus for every individual is anticipated. Proposed idea is an IOT controlled system where devices are controlled according to individual’s mood.
Index Terms: NLP, N-Gram, Smart Living Machines (SLM), Emotion Extraction, Human Machine Interface. References:
1. Rina Damdoo; Shrawankar, U.; (2012). Probabilistic N-Gram Language Model for SMS Lingo. IEEE RACSS, Chennai, India, 113-117. 2. Data-Intensive Information Processing Applications; Jordan Boyd-Graber University of Maryland; (2011). This work is licensed under a Creative Commons Attribution-Non commercial-Share Alike 3.0 United 3. Working with n-grams in SRILM Linguistics 165, Professor Roger Levy 13 February 2015 Linguistics 165 n-grams in SRILM lecture notes, page 1 Roger Levy, Winter 2015 4. Sharon Goldwater; (2017). Accelerated Natural Language Processing Lecture 5 N-gram models, entropy 293-300 5. N-grams L545 Dept. of Linguistics, Indiana University Spring 2013 6. Emotion Extraction Model from Text with Natural Language Processing By Sonu A. Thombare, Sumit A.Tifane Computer Science & Engineering, Dhamangaon Education Society College of Engineering & Technology, INDIA 2017 IJEDR | Volume 5, Issue 2 | ISSN: 2321-9939 7. Text Based Emotion Recognition: A Survey By Chetan R. Chopade Pune Institute of Computer Technology, Pune, Maharashtra, India Volume 4 Issue 6, June 2015 Paper ID: SUB155271 8. EmoTxt: A Toolkit for Emotion Recognition from Text By Fabio Calefato, Filippo Lanubile, Nicole Novielli University of Bari “Aldo Moro” https://stackoverflow.com/ Last accessed: July 2017. 9. Emotion Extraction using Rule based and SVM-KNN Algorithm By Mohini Chaudhari Department of Computer Engineering PIIT, New Panvel, India and Sharvari Govilkar Department of Computer Engineering PIIT, New Panvel, India International Journal of Computer Applications (0975 – 8887) Volume 125 – No.11, September 2015 10. Detecting Emotion in Text Kaitlyn Mulcrone University of Minnesota, Morris UMM CSci Senior Seminar Conference, April 2012 Morris, MN. 11. Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles, Jasleen Kaur, Jatinderkumar R. Saini. International Journal of Computer Applications (0975 – 8887) Volume 101– No.9, September 2014 12. EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis Jasy Liew Suet Yan, Howard R. Turtle, Elizabeth D. Liddy School of Information Studies, Syracuse University Syracuse, New York, USA 13. Probabilistic language model for template messaging based on Bi-gram, Rina Damdoo, Urmila Shrawankar, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012), Nagapattinam, Tamil Nadu, India, 30-31 March 2012. 14. Bi-Gram based Probabilistic Language Model for Template Messaging, Rina Damdoo, International Journal of Computer Applications (0975–8887), Volume 66, No.18, March 2013 15. Machine Translation using Multiplexed PDT for Chatting Slang, Rina Damdoo, Asian Journal of Information Technology (1682-3915), Medwell Journals 2013 16. Emotion Detection from Text, V V Ramalingam, A Pandian, Abhijeet Jaiswal, Nikhar Bhatia, National Conference on Mathematical Techniques and its Applications (NCMTA 18) IOP Publishing IOP Conf. Series: Journal of Physics: Conf. Series 1000 (2018) 012027 doi :10.1088/1742-6596/1000/1/012027 17. Emotion Detection through Text: Survey, Kashif khan, Sher Hayat , Muhammad Ejaz khan, Research Journal of Science & IT Management (ISSN 2251 1563) RJSITM: Volume: 05, Number: 07, May -2016 pg 8-16 18. Emotion Recognition From Text-a Survey, Ms. Pallavi D. Phalke , Dr. Emmanuel M., Essay in Computer Science,2018 19. Toward Emotional Internet of Things for Smart Industry, David Antonio Gomez Jauregui, SMART 2017 : The Sixth International Conference on Smart Cities, Systems, Devices and Technologies, 2017 20. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition, Leandro Y. Mano, Bruno S. Faical, Luis H.V. Nakamura, Pedro H. Gomes, Giampaolo L. Libralon, Rodolfo I. Meneguete, Geraldo P.R. Filho, Gabriel T. Giancristofaro, Gustavo Pessin, Bhaskar Krishnamachari, Jo Ueyama, Computer Communications (2016), doi: 10.1016/j.comcom.2016.03.010 21. Extractive Technique for Text Summarization based on Ranking Scheme, A.A.Shrivastava, A.S.Bagora, R.Damdoo, IJCSE, 2018,pg 369- 373
Authors: Ruchita S. Chaudhari, Ravindra M. Moharil Paper Title: An Experimental Analysis of Solar PV System Under Shaded Condition Using P&O Method Abstract: Photo Voltaic (PV) systems are currently in demand as the world is moving from conventional energy utilization to harnessing the solar power provided by nature. Lot of efficient electronic converters are invented, to transform the solar energy into human usable form. But the main drawback of these systems is the dependency on optimum lighting conditions in order to produce effective outputs. Optimal lighting conditions are generally not available in real time solar systems, thus in this paper analysis of the effects of non-optimum lighting conditions (or shading) on the PV systems which are controlled by Perturb & Observe (P&O) method of Maximum Power Point Tracking (MPPT) controllers. This P&O MPPT scheme is easy to implement and efficiency of PV panel is improved. The system consider in this paper is tested in MATLAB/SIMULINK software to demonstrate the simulation results.
Index Terms: MPPT, partial shading, P&O, solar photovoltaic
References: [1]B. Subudhi and R. Pradhan, “A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems”, IEEE Trans. Sustainable Energy, vol. 4, no. 1, pp. 89-98, Jan. 2013. [2]H. Patel and V. Agarwal, “Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions”, IEEE Transactions on Industrial Electronics, vol. 55, no. 4, pp. 1689-1698, April 2008. [3]A. Chikh and A. Chandra, “An Optimal Maximum Power Point Tracking Algorithm for PV Systems With Climatic Parameters Estimation”, IEEE Trans. Sustainable Energy, vol. 6, no. 2, pp. 644-652, April 2015. [4]Shubhajit Roy Chowdhury and Hiranmay Saha, “Maximum power point tracking of partially shaded solar 51. 301-306 photovoltaic arrays”, Solar Energy Materials and Solar Cells, Vol. 94, pp. 1441-1447, Jan. 2010. [5]M. G. Villalva, J. R. Gazoli and E. R. Filho, “Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays”, IEEE Trans. on Power Electronics, vol. 24, no. 5, pp. 1198-1208, May 2009. [6]M. Miyatake, M. Veerachary, F. Toriumi, N. Fujii and H. Ko, “Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach,” IEEE Trans. Aerospace and Electronic Systems, vol. 47, no. 1, pp. 367- 380, January 2011 [7]Murari Lal Azad a, Soumya Dasb$, Pradip Kumar Sadhu c (Member, IEEE), Biplab Satpati b (Member, IEEE), Anagh Guptab, P. Arvindb “P&O algorithm based MPPT technique for solar PV System under different weather conditions”, International Conference on circuits Power and Computing Technologies [ICCPCT], 2017. [8]S. K. Kollimalla and M. K. Mishra, “Variable Perturbation Size Adaptive P&O MPPT Algorithm for Sudden Changes in Irradiance”, IEEE Trans. Sustainable Energy, vol. 5, no. 3, pp. 718-728, July 2014. [9]Nishant Kumar, Ikhlaq Hussain, Bhim Singh, Bijaya Ketan Panigrahi, “MPPT in Dynamic Condition of Partially Shaded PV System by using WODE Technique”, IEEE Trans. Sustainable Energy, DOI 10.1109/TSTE.2017.2669525. [10]Ravindra M. Moharil and Prakash S. Kulkarni “A case study of solar photovoltaic power system at Sagardeep Island, India”, Renewable and Sustainable Energy Reviews, vol 13 (2009), pp 673-681. [11]Ravindra M. Moharil and Prakash S. Kulkarni “Reliability analysis of solar photovoltaic system using hourly mean solar radiation data”, Solar Energy 84 (2010) 691-702. Authors: Poonam Shende, B. Y. Bagde, B. S. Umre Paper Title: Estimation of Available Transfer Capability For bilateral trading transactions Abstract: With increasing electricity demand, existing transmission network is constrained to supply economic and reliable power to the consumers. In open access environment, the capacity of the network, therefore must be analysed carefully before a transaction between utilities is executed. The available transfer capability (ATC) is the measure which compute a amount of power that can be transmitted over transmission network within the permissible limits. In the proposed work, ATC for a given power system is calculated using PTDF method for each source-sink pair and Iranian load curve. The simulation is carried out on IEEE 6 bus system in MATLAB programing environment. Index Terms: Available transfer capability (ATC), Power transfer distribution factor (PTDF), DC power flow, Continua-ton power flow (CPF), AC power flow
References: S. Deke, “Available transfer capability calculations considering outages,” International Conference on power and embedded drive control, 307-311 52. Chennai, India, 16-18 march 2017. 2. M. Bhaskar, J, Jimoh, “Available transfer capability calculation using PTDF and implementation of optimal power flow in power marketing,” 5th International Conference on renewable energy research and applications, Birmingham, UK, 20-23 Nov 2016. 3. M. Patil, A. Girgis, “New iterative method for available transfer capability calculation,” IEEE Conference on power and energy society general meeting, Detroit, USA, 24-29 July 2011. 4. P. Sauer, “Technical challenges of computing available transfer capability (ATC) in electrical power system,” IEEE Conference on Proceedings of the thirtieth hawaii International conference on system sciences, Wailea, HI, USA, USA, 7-10 Jan 1997. 5. E. Dehnavi, H. Abdi, “Determine optimal buses for implementing demand response as an effective congestion management method,” IEEE Transaction on power system, vol.32 no.2, March 2017. 6. P. Venkatesh, R. Ganadass, N. Padhy, “Available transfer capability determination using power transfer distribution factors,” International journal of emerging electric power system, vol.1, issue 2, pp 1-14 Jan 2004. 7. R. Zimmerman, C. Murillo-Sanchez, R. Thomas, “Matpower: Steady-state operations, planning and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12-19, Feb. 2011. 8. W. Li, P. Wang, “Determination of optimal total transfer capability using a probabilistic approach,” IEEE Transactions on power system, vol. 21, no. 2, May 2006. 9. G. Hamoud, “Assessment of available transfer capability of transmis-sion system,” IEEE Transactions on power system, vol. 15, no. 1, Feb 2000. 10. North-American electric reliability councils report-Available transfer capability defination and determination, June 11. G. Ejebe, J. Waight, M. Santos-Nieto, W.Tinney, “Fast Calculation of Linear Available TransferCapability,” IEEE Transactions on Power Systems Vol. 15, No. 3, Aug 2000. 12. N. Kolipaka, J. Amaranth, K. Kiran Kumar, S. Kamakshiah, “Available Transfer Capability Calculations Using Neural Networks in Deregulated Power,” 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, 21-24 April, 2008. 13. B. Y. Bagde, B. S. Umre, “Security constrained economic dispatch,” International conference on Energy Communication, Data Analytics & Soft Computing, Aug 2017 14. B. Y. Bagde, B. S. Umre, K. R. Dhenuvakonda, “An Efficient Transient Stability Constrained Optimal Power Flow Using Biogeography Based Algorithm,” Internatinal Journal on Electrical Energy Systems, pp. 2-15, Aug 2017. 15. B. Y. Bagde, B. S. Umre, R. D. Bele, H. Gomase, “Optimal Network Reconfiguration of A Distribution System Using Biogeography Based Optimization,” 2016 IEEE 6th International Conference on Power Systems (ICPS), 4-6 March 2016. 16. A. Somkuwar, B. Y. Bagde, “Constrained Optimal Reactive Power Procurement in Power System”, IEEE International Conference on Smart Electric Drives and Power System (ICSEDPS), 12-13 June 2018. 17. B. Y. Bagde, B. S. Umre, N. Narde, “Power loss optimization using distribution system reconfiguration in presence of DG,” Journal of Engineering and applied Science, vol. 13, Issue 1, No. 2339-2345,2018. Authors: Rajashree Mandavgane, Narendra Bawane Paper Title: Experimental Analysis of two Encryption Schemes for Security of Video Streaming Abstract:With an exponential growth of multimedia and high speed internet, many relevant services and software are available for common person. Out of which, one of the applications is that common man wants to send multimedia data on internet. H.264 codec is used to compress this data by the systems. But data on the internet can be hacked, pirated or tampered with. Hence some security is required for multimedia data. In this paper, two schemes are provided for securing the video streaming after compression. One is with the proposed algorithm and the other with the standard algorithm (AES). In the second scheme, FMO is applied to bitstream which adds error resilience to the data. Both the schemes are applied selectively to I frame only of AVC/SVC. Performance is evaluated by finding PSNR for quality measurement. Also overhead bit and encoding time are compared. Security is discussed for some attacks. After considering the results for PSNR and discussion for other parameters, it is concluded that the second encryption scheme is much better as compared to the first one. Index Terms: Bitstream, Encryption, FMO, H.264AVC/SVC, Security.
References: 1. K. John Singh and R. Manimegalai, “A Survey on Joint Compression and Encryption Techniques for Video Data” Journal of Computer Science, ISSN 1549-3636, 8 (5): pp.731-736, 2012. 312-315 53. 2. Jolly Shah and Dr. Vikas Saxena, “Video Encryption: A Survey”, International Journal of Computer Science Issues, Vol. 8, Issue 2, pp. 525- 534, March 2011. 3. Thomas Stutz and Andreas Uhl, “A Survey of H.264 AVC/SVC Encryption”, Technical Report 2010-10, Technical Report 2010. 4. Mrs. Rajashree N. Mandavgane and Dr. N.G.Bawane, “Quality Assessment of Precodec Video Protection”, International Journal of Advance Research in Computer Science and Management Studies, (IJARCSMS), Volume 2, Issue 1, pp. 264-268, ISSN: 2321-7782 (Online) January 2014. 5. Mrs. Rajashree N. Mandavgane and Dr. N.G.Bawane, “AVC VIDEO SECURITY ON WIRELESS CHANNEL”, The international journal of multimedia & its applications, (IJMA), VOL.8, NO.5, and DOI: 10.5121/ijma.2016.8501,pp.1-8, October. 6. C. Shi, S. Y.Wang, and B. Bhargava, “MPEG video encryption in real-time using secret key cryptography”, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA ‟99), Las Vegas, Nev, USA, pp. 191– 201, June-July 1999. 7. M. W. Zeng and S. Lei, “Efficient frequency domain selective scrambling of digital video”, IEEE Transactions on Multimedia, vol. 5, no. 1, pp. 118–129, 2003. 8. ITU-T H.264. Advanced video coding for generic audiovisual services, November 2007. 9. M. Abomhara, Omar Zakaria, Othman O. Khalifa, A.A Zaidan & B.B Zaidan “Enhancing Selective Encryption for H.264/AVC Using Advanced Encryption Standard”, International Journal of Computer Theory and Engineering, Vol. 2, No. 2 pp. 223-229, April 2010. 10. Wei Huang, Wenqing Fan and Tingting Zhang, “A Selective Encryption Scheme for H.264/AVC Video Coding”, Informatics in Control, Automation and Robotics, Volume 2, LNEE 133, pp. 317–32. Authors: Sumiti, Sumit Mittal
Paper Title: An Experimental Analysis on Selfish Node Detection Measures and Methods
Abstract: Mobile network is an open space network that suffers from various internal and external attacks. Selfish node is one such attack form that occurs in intermediate nodes. In this paper, the work behavior and
characterization of selfish node is explored. The paper has presented three different algorithms called token based, agent based and watchdog methods to detect selfish node attack. The characteristics and the work behavior of these methods is provided in this paper. These methods are simulated on a mobile network. The analysis results
shows that the token based method has achieved the better packet communication, byte communication ratio and
reduced the communication loss. The watchdog and agent based method also performed better in terms of lesser
communication delay.
Index Terms: Mobile Network, Malicious Node, Selfish Node, Selfish Node Detection Method.
References:
1. Yongwei Wang, Venkata C. Giruka, Mukesh Singhal, Truthful multipath routing for ad hoc networks with selfish nodes, Journal of Parallel and Distributed Computing, Volume 68, Issue 6, June 2008, Pages 778-789 2. Yongwei Wang, Mukesh Singhal, On improving the efficiency of truthful routing in MANETs with selfish nodes, Pervasive and Mobile Computing, Volume 3, Issue 5, October 2007, Pages 537-559
3. Debjit Das, Koushik Majumder, Anurag Dasgupta, Selfish Node Detection and Low Cost Data Transmission in MANET using Game Theory, Procedia Computer Science, Volume 54, 2015, Pages 92-101 316-322 54. 4. N. Ramya and S. Rathi, "Detection of selfish Nodes in MANET - a survey," 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2016, pp. 1-6 5. Z. Ullah, M. S. Khan, I. Ahmed, N. Javaid and M. I. Khan, "Fuzzy-Based Trust Model for Detection of Selfish Nodes in MANETs," 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, 2016, pp. 965-972. 6. C. Chakrabarti, S. Chakrabarti and A. Banerjee, "A dynamic two hops reputation assignment scheme for selfish node detection and avoidance in delay tolerant network," 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, 2015, pp. 345-350. 7. S. K. Das, B. J. Saha and P. S. Chatterjee, "Selfish node detection and its behavior in WSN," Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on, Hefei, 2014, pp. 1-6. 8. C. Chakrabarti, A. Banerjee and S. Roy, "An observer-based distributed scheme for selfish-node detection in a post-disaster communication environment using delay tolerant network," Applications and Innovations in Mobile Computing (AIMoC), 2014, Kolkata, 2014, pp. 151-156. 9. N. Muthumalathi and M. M. Raseen, "Fully selfish node detection, deletion and secure replica allocation over MANET," Current Trends in Engineering and Technology (ICCTET), 2013 International Conference on, Coimbatore, 2013, pp. 413-415. 10. R. I. Ciobanu, C. Dobre, M. Dascalu, S. Trausan-Matu and V. Cristea, "Collaborative selfish node detection with an incentive mechanism for opportunistic networks," 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), Ghent, 2013, pp. 1161-1166. 11. Wang Xing-Wei, D. P. Qu and M. Huang, "Selfish nodes detection mechanism and stimulation mechanism over mobile peer-to-peer networks," 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, 2012, pp. 1030-1034 12. E. Hernandez-Orallo, M. D. Serrat, J. C. Cano, C. T. Calafate and P. Manzoni, "Improving Selfish Node Detection in MANETs Using a Collaborative Watchdog," in IEEE Communications Letters, vol. 16, no. 5, pp. 642-645, May 2012. 13. R. Gunasekaran, V. Rhymend Uthariaraj, R. Sudharsan, S. Sujitha Priyadarshini and U. Yamini, "Detection and prevention of selfish and misbehaving nodes at MAC layer in mobile ad hoc networks," Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on, Niagara Falls, ON, 2008, pp. 14. A. Sharma, D. Singh, P. Sharma and S. Dhawan, "Selfish nodes detection in delay tolerant networks," Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on, Noida, 2015, pp. 407-410. 15. R. Tarannum and Y. Pandey, "Detection and deletion of selfish MANET nodes-a distributed approach," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on, Dhanbad, 2012, pp. 152-156. 16. S. Saeed, I. Zubair and M. H. Islam, "Detection of selfish nodes in peer-to-peer networks," 2009 First International Conference on Networked Digital Technologies, Ostrava, 2009, pp. 504-507. 17. Shin Yokoyama, Y. Nakane, O. Takahashi and E. Miyamoto, "Evaluation of the Impact of Selfish Nodes in Ad Hoc Networks and Detection and Countermeasure Methods," 7th International Conference on Mobile Data Management (MDM'06), 2006, pp. 95-95. 18. D. Djenouri and N. Badache, "New approach for selfish nodes detection in mobile ad hoc networks," Workshop of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, 2005., 2005, pp. 288-294 19. Hussain, A. Nadeem, O. Khan, S. Iqbal and A. Salam, "Evaluating network layer selfish behavior and a method to detect and mitigate its effect in MANETs," Multitopic Conference (INMIC), 2012 15th International, Islamabad, 2012, pp. 283-289. 20. A. Jangra, Shalini and N. Goel, "e-ARAN: Enhanced Authenticated Routing for ad hoc networks to handle selfish nodes," Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on, Nagapattinam, Tamil Nadu, 2012, pp. 144-149 21.Sumiti and Sumit Mittal, “Characterization of Routing Approaches in Mobile Network: A Study,” International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Volume 4, Issue 10, October 2014, pp. 108-111. 22. Sumiti and Sumit Mittal, “Detecting Selfish Node over the Active Path using Neighbor Analysis based Technique,” International Journal of Science and Research (IJSR), Volume 4, Issue 3, March 2015, pp. 1295-1298. 23.Sumiti and Sumit Mittal, “Identification Technique for all Passive Selfish Node Attacks In a Mobile Network,” International Journal of Advance Research in Computer Science and Management Studies (IJARCSMS), Volume 3, Issue 4, April 2015, pp. 46-51. 24. M. Tamilarasi and T. V. P. Sundararajan, "Secure enhancement scheme for detecting selfish nodes in MANET," 2012 International Conference on Computing, Communication and Applications, Dindigul, Tamilnadu, 2012, pp. 1-5. 25. Kashyap Balakrishnan, Jing Deng, Pramod K. Varshney, “TWOACK: Preventing Selfishness in Mobile Ad Hoc Networks", Wireless Communication and Networking Conference, 2005, ISBN: 0-7803-8966-2. 26. P. Sankareswary, R. Suganthi and G. Sumathi, “Impact of Selfish Nodes in Multicast Ad Hoc on demand Distance Vector Protocol,” International Conference on Wireless Communication and Sensor Computing, (ICWCSC), IEEE, 2010, INSPEC Accession Number: 11140332. 27. Sandeep A. Throat and P. J. Kulkarni, “Opportunistic Routing in Presence of Selfish Nodes for MANET,” Wireless Personal Communication, Springer, 2015, Volume 82, issue 2,pp. 689-708. 28. Jebakumar Mohan Singh Pappaji Josh Kumar, Ayyaswamy Kathirvel, Namaskaram Kirubakaran, Perumal Sivaraman, "A unified approach for detecting and eliminating selfish nodes in MANETs using TBUT", EURASIP Journal on Wireless Communications and Networking, pp 1-11, 2015 29. Radhika Garg, Sanjay Kumar, Sangeeta Malik, Deepak Goyal, Dr. Pankaj Gupta, An Agent Based Approach to Avoid Selfish Node Dynamically in Mobile Networks, International Journal of Computer Science & Management Studies, Vol. 12, Issue 03, pp 49-53, September 2012 30. Pavithra SL, Prema P. Efficient Detection Of Selfish Node In Manet Using A Colloborative Watchdog. International Journal of Engineering Research and Applications. 2016 Jan 1;6(4):43-5. 31. A. Meeran, N. Praveen A and K. Ratheesh T, "Enhanced system for selfish node revival based on watchdog mechanism," 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, 2017, pp. 332-337. Authors: Amandeep, G. Geetha Paper Title: Research Problems in Block Cipher Cryptanalysis: An Experimental Analysis Abstract:Cryptography has always been a very concerning issue in research related to digital security. The dynamic need of applications and keeping online transactions secure have been giving pathways to the need of developing different cryptographic strategies. Though a number of cryptographic algorithms have been introduced till now, but each of these algorithms has its own disadvantages or weaknesses which are identified by the process 55. of cryptanalysis. This paper presents a survey of different block ciphers and the results of attempts to identify their weakness. Depending upon the literature review, some open research problems are being presented which the 323-327 cryptologists can depend on to work for bettering cyber security.
Index Terms: block ciphers, cryptanalysis, attacks, SPN, Feistel. References:
1. D. W. Davies, "Some Regular Properties of the DES," in Advances in Cryptology: A Report on CRYPTO 81, 1981. 2. S. Langford and M. Hellman, "Differential–linear cryptanalysis," in Advances in Cryptology - Crypto’94, Springer Verlag, 1994. 3. J. Smith, "The design of Lucifer: a cryptographic device for data communications," N.Y., USA, 1971. 4. E. B. a. A. Shamir, "Differential Cryptanalysis of Snefru, Khafre, REDOCII, LOKI and Lucifer," 1991. 5. E. Biham and I. Ben-Aroya, "Differential cryptanalysis of Lucifer," Journal of Cryptology, vol. 9, no. 1, pp. 21-34, 1996. 6. "Data Encryption Standard," 1999. 7. M. Matsui, " Linear Cryptanalysis Method for DES Cipher," in Advances in Cryptology -- EUROCRYPT '93, 1993. 8. L. Knudsen, "Partial and higher order differentials and its application to the DES," 1995. 9. P. Rogaway and J. Kilan, "How to protect DES against exhaustive key search," Journal of Cryptology, vol. 14, no. 1, pp. 17-35, 2001. 10. B. S. a. D. W. J. Kelsey, "Related-key cryptanalysis of 3-WAY, Biham-DES, CAST, DES-X, NewDES, RC2, and TEA," in First International Conference on Information and Communication Security ICICS'97, London , 1997. 11. S. Shimizu and Miyaguchi, "Fast Data Encipherment Algorithm FEAL," in Advances in Cryptology - Eurocrypt '87, 1987. 12. A. Y. M. Matsui, "A New Method for Known Plaintext Attack of FEAL Cipher," in EUROCRYPT 1992, 1992. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar 13. G. C. H.Gilbert, "A Statistical Attack of the FEAL-8 Cryptosystem," in 10th Annual International Cryptology Conference on Advances in Cryptology, 1990. 14. R. Rivest, "A description of the RC2(r) encryption algorithm," 1998. 15. R. Merkle, "Fast software encryption functions," in Advances in Cryptology - Crypto'90, A. M. a. S. Vanstone, Ed., Santa Barbara, California, Springer-Verlag, 1990, pp. 476-501. 16. E. Biham, A. Biryukov and A. Shamir, "Miss in the Middle Attacks on IDEA and Khufu," in FSE 1999, L. 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Authors: Renu Jangra, Ramesh Kait Paper Title: Modified Ant System Solving TSP Problem Abstract: ACO is applied on various combinatorial optimization problems. One among them is Travelling Salesman Problem. Generally, the fundamental ACO has the disadvantage of the entrapment in the local minimum and stagnation problem. In this paper, we proposed the algorithm named modified ant system (MAS) to resolve the above problems by modifying the pheromone update equation which results in better overall searching ability and also give better optimal solution earlier than AS. The comparison is done between basic ant system and modified ant system on different TSP problem instances. The proposed algorithm illustrates the less cost\length of the tour taken by ants to discover the shortest pathway.
Index Terms: Ant System (AS), Modified Ant System (MAS), Pheromone, Ant Colony Optimization (ACO).
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Authors: Neeraj Chauhan, Manu Sood Paper Title: Performance Analysis of POX, Open vSwitch and Open Day Light SDN Controllers on Cloud Abstract:Software Defined Networking (SDN), as a revolutionary technology is changing the way traditional computer networks used to function. It is a replacement of traditional networks where control of the network was embedded with the data layer. Software Defined Networks separates control layer from data layer which enables network administrators to reconfigure the network easily without making any physical change in the network. An SDN network has various components like switch, controllers and hosts. Generally, SDN controllers are considered the “brain” of this network. In this paper, firstly, performances of three SDN controllers POX, Open vSwitch (OVS) and OpenDayLight (ODL) are analyzed for single topology on cloud on the basis of three parameters Round-Trip-Time, Average Time and Total Time. Results show that OVS controller outperforms POX and ODL controllers. Later, the performance of these three controllers has also been analyzed for different 57. topologies on local machines. 332-339 Index Terms: Google Cloud, Open vSwitch (OVS) controller, OpenDayLight (ODL) controller, POX controller, SDN controller, Software Defined Networking (SDN).
References:
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Keywords: DCT, DWT, Steganography, Steganalysis, JPEG
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Paper Title: A Malicious Attacks and Defense Techniques on Android-Based Smartphone Platform Abstract: In this digital era after computer and internet smartphone is the third revolution and making ubiquitous computing possible. Android lead the smartphone market as most used operating system. This popularity of Android also makes it primary targets of cyber attackers and hackers. There are many different types of cyber-attacks targeted towards Andorid environment. In this review paper, we have investigated various attacks reported with respect to Android and have also gathered different type of defenses available to protect users from these attacks. This work is focus on accumulating various literature works available in this domain and provide a comprehensive representation of these works. The various works are grouped into two broad categories i.e. signature and non-signature based, and techniques mentioned in each work is studied and technical observations are made against them which help to understand the usability of these techniques. Such organized and details review work is required to study the problem in depth and works towards solution. The literature works are summarized and organized in proper table which help to visualized and easy comparison the information.
Keywords: Smartphone, Android
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Shegelman Ilia Romanovich, Galaktionov Oleg Nikolaevich, Kuznetsov Alexey Vladimirovich, Vasilev Authors: Aleksey Sergeevich, Sukhanov Yury Vladimirovich Building of the Knowledge Base for the Elaboration of Processes of Food Raw Materials and Food Paper Title: Product Transportation by Means of Tractors and Road Vehicles Abstract: Food raw materials and food products represent one of the most important and at the same time challenging cargoes, and this specifies the variety of technological processes and technical facilities used in transportation. In Russia and abroad, intense research and development are taking place, that are aimed at improvement of technological processes and technical facilities for food raw material and/or food products transportation by means of tractor, road, rail and water transport. Of special importance are tractor and road transports, which require the development of patentable technological and technical solutions in order to increase their competitiveness. This resulted in the knowledge base building for the elaboration of processes of tractor and road transportation of food raw materials and food products on the basis of scientific and technical and patent 60. search. Based on the analysis and knowledge building, the areas of improvement of food raw materials and food product transportation processes by means of tractor and road transport were identified. 370-378
Keywords: road vehicles, knowledge base, logistics, patent, food raw materials, food products, tractor transport, transportation.
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Authors: Mikhail V. Grachev Paper Title: Determination of Anisotropy Value by Compliance Parameter for Titanium Alloy OT4-1 Abstract: The characteristic of a semi-finished product from a sheet of titanium alloy OT4-1 is the presence of anisotropy of mechanical properties along and across the direction of rolling (along and across the fibers). The purpose of the experiments was to establish magnitude of the anisotropy on the parameter of the compliance of the samples in these directions and to develop measures to reduce it. The tests were comparative in nature; circles with a diameter of 80 mm and a thickness of 0.5 ... 1 mm served as samples.
Keywords: Titanium, Technology, Stiffness.
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Authors: Jaspalsinh B Dabhi, Ajitkumar N Shukla, Sukanta Kumar Dash Paper Title: Compact adsorption Cooling System: An Evaluation Abstract: This work review’s commercial and residential scale adsorption cooling systems and other industrial size adsorption refrigeration systems in line with the need to make enhanced heat transfer a compact system. The main objective of the study is to present the methods used for enhancing the specific cooling power output, cooling effect leading to make a compact adsorption cooling system. Various systems studied include silica gel-water, activated carbon-ammonia, zeolite-water, and metal-organic framework (MOF) material. The review concludes that silica gel-water working pair gives better performance as compared to MOF material with 10-15 min as short cycle time. MOF material gives better results as compared to silica gel-water with more than 60 min of adsorption- desorption cycle time. For the case of activated carbon- ammonia, when the metal powder of copper, iron, and aluminum replaces 10 to 30% of the mixture gives better performance than using activated carbon alone due to the high thermal conductivity of added metals. It is also concluded that performance of Regular Density (RD) silica gel is slightly better than RD 20-60, Type A and Type B silica gel. Silica gel-water working pair is preferable as compared to Zeolite-water pair for low-grade heat utilization.
Keywords: MOF materials, Adsorption, Refrigeration, Silica gel, Zeolite
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Sinitsyna Paper Title: Highly Efficient Phytoradiator Development for Plant Photoculture Based on Combined Spectrum Abstract: I They presented the results of photobiological studies, the purpose of which was to determine the preferred requirements for the spectrum of phytoradiators in experimental research hydroponic device for plant photoculture based on combined-spectrum light emitting diodes taking into account the specific features of specific crops and growing tasks. They presented the results and the assessment of phytoradiator spectral composition effect based on LEDs and the productivity (biomass) of green culture (Starfighter salad). They showed that the results of photobiological research will create a scientific basis for phytoradiator production organization based on LEDs at OJSC “Ardatovsky Lighting Engineering Plant”, the spectral density of the radiation flux of which takes into account the “preferences” of green salad crops as much as possible. 63. Keywords: light crops, phytoradiator with LEDs, spectral composition, irradiance, experimental hydroponic 392-394 research device, photobiological studies, biomass.
References: 1. Abouhussein, D.M.N., Sabry, P., Moghawry, H.M.Recent (2019) “insight into critical concerns in international quality regulations governing the pharmaceutical industry: A review” International Journal of Pharmaceutical Research, 11 (1), pp. 708-718. 2. Prikupets L.B., 2017. Technological lighting in the agro-industrial complex of Russia. Lighting equipment. Number 6: pp. 6–14. 3. Prikupets L.B., Boos G.V., Terekhov V.G., Tarakanov I.G., 2018. The study of radiation effect in various ranges of headlights on the production and biochemical composition of green salad crop biomass. Lighting equipment. No. 5: pp. 6-12. 4. Kurshev A.E., Ruzankin S.I., Zorikov N.S., 2018. Salad growing under a full spectrum of LED light sources. Greenhouses of Russia. No. 2: pp. 30 - 31. 5. Emelin A.A., Prikupets L.B., Tarakanov I.G., 2015. The spectral aspect of irradiator use with LEDs for salad plant growing in light- culture conditions. Lighting equipment. № 4: pp. 47 - 52. 6. Bugbee, B., 2016. Towards an optimal spectral quality for plant growth and development: The importance of radiation capture. Plants, Soils and Climate Faculty Publications. Date Views 08.04.2019. URL: https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1765&context=psc_fac-pub. 7. Boos, G.V., Prikupets, L. B., Rozovskiy, E. I., Stolyarevskaya, R. I., 2015. Standardization in the field of lighting equipment and installations for greenhouses. Light & Engineering, 26. 8. http://www.platan.ru/cgi-bin/qwery.pl/id=745391824. Date Views 03.09.2018. 9. http://www.ledengin.com/files/products/LZ1/LZ1-00R. Date Views 04.09.2018. 10. https://www.lumileds.com/uploads/637/DS171-pdf. Date Views 04.09.2018.
Authors: Shegelman I. R., Shtykov A. S., Vasilev A. S., Galaktionov O. N., Kuznetsov A. V., Sukhanov Y. V. Systematic Patent-Information Search as a Basis for Synthesis of New Objects of Intellectual Paper Title: Property: Methodology and Findings Abstract: The articlereveals that systematic patent-information search is a foundation for building the knowledge bases, which are used as a synthesis of new objects of intellectual property patented as results of intellectual activity. The article presents the methodology of building of knowledge bases and their use for the development of patentable solutions in various areas of science and technology. The knowledge bases are built upon the expanded collection and analysis of Russian and foreign scientific and technical information for the specific types of technology and technical equipment. The synthesis of patentable objects of intellectual property is carried out upon functional and technological analysis and brainstorming. The authors developed the methodology with the synthesis of patentable technological solutions from the field of cross-cutting technology that integrates the operations on food raw materials preparation and transportation, production of functional food, mining industry, manufacturing of equipment for spent nuclear fuel handling; exploration works in the field of timber industry and forestry, and low-temperature plasm.
Keywords: knowledge base, objects of intellectual property, spent nuclear fuel, patent, transportation and storage, transportation and storage container.
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Karasev, N. I., Popov, V. S., & Prosvirin, V. P. (2018). Russian Patent no. 2661335 “Device for repairing the lining of the cooling pond.” 20. Kariakin, Yu. E., Lavrentiev, S. A., Pavliukevich, N. V., Pletnev, A. A., & Fedorovich, E. D. (2012). Calculation of the process of vacuum drying of the metal-concrete container with spent nuclear fuel. Inzhenerno-Fizicheskiy Zhurnal, 85(1), 158-166. 21. Kariakin, Yu. E., Nekhozhyn, M. A., & Pletnev, A. A. (2013). Calculation method for duration of vacuum drying of the metal-concrete container with spent nuclear fuel. Inzhenerno-Fizicheskiy Zhurnal, 86(4), 689-695. 22. Khokhlov, V. A., Dokutovich, V. N., & Korzun, I. V. (2018). Russian Patent no. 2647125 “Method for regenerating chloride electrolyte for electrochemical processing of spent nuclear fuel.” 23. Kirilina, V. M., Shegelman, I. R., & Vasilev, A. S. (2019). Russian Patent no. 2681676 “Food product, including plants grown in conditions of northern latitudes.” 24. Korovkin, S. V., Garaev, I. T., Kireev, E. V. (2018). Russian Patent no.2656249 “Method of placing spent nuclear fuel”. 25. Len, D. T., & Skorin, V. N. (2019). Russian Patent no. 186881 “Trolley for transporting fresh canisters or containers for spent reactor fuel setting.” 26. Leshchev, O. P. (2018). Russian Patent no. 2654834 “Device for detecting and removing leakage.” 27. Lubimtsev, D. A., Lykov, D. N., Naumov, M. A., Romanov, V. I., Seevlev, I. N., Smirnov, D. Yu., … Kukanov, S. S. (2018). Russian Patent no. 2642853 “Container case for transportation and storage of spent nuclear fuel.” 28. Merkulov, I. A., Matselia, V. P., Seelev, I. N., Barakov, B. N., Ilinykh, Yu. S., Vasiliev, A. V., … Goncharov, D. A. (2018). Russian Patent no. 2658295 “Method of decladding of fuel elements and device for its implementation.” 29. Merkulov, I. A., Tikhomirov, D. V., Zhabin, A. Yu., Apalkov, G. A., Smirnov, S. I., Diachenko, A. S., & Malysheva, V. A. (2018). Russian Patent no. 2648283 “Method for regenerating spent extraction system based on organic solution of tributyl phosphate in hexachlorobutadiene (variants).” 30. Muratov, O. E., Stepanov, I. K., & Tsareva, S. M. (2013). Methods of liquid radioactive waste reprocessing: analytical review. Nauchnye i Tekhnicheskie Aspekty Okhrany Okruzhayushchey Sredy, 3, 17-40. 31. Muratov, O. E., Tikhonov, M. N., Piskunov, V. M., & Tairov, T. N. (2012). Assurance of radio-ecological security during radioactive waste and spent nuclear fuel handling under innovative development of nuclear energetics. Ekologicheskie Sistemy i Pribory, 1, 12-24. 32. Muratov, O. E., Tikhonov, M. N., & Rylov, M. I. (2014). Nuclear and radiological heritage in north-west Russia: Assurance of nuclear and radiation security, the role of society. Ekologiya Promyshlennogo Proizvodstva, 1(85), 34-45. 33. Paraguzov, P. A., Sharova, N. V., & Pankratova, E. V. (2018). Russian Patent no. 2664893 “Method for obtaining sorbent matrix material based on natural zeolite for immobilizing radionuclides.” 34. Popkov, V. A. (2016). Development of spent nuclear fuel handling technology (Doctoral dissertation). St. Petersburg, p. 150. 35. Putrolainen, V. V. Grishin, A. M., & Rigoev, I. V. (2019). Russian Patent no. 2680548 “Method for obtaining a transparent wear-resistant coating based on aluminum-magnesium boride on the surface of transparent glass products.” 36. Satoh, T., Iwai, T., & Aral, Ya. (2009). Electrolysis of Burnup-Simulated Uranium Nitride Fuels in LiCl-KCl Eutectic Melts. Journal of Nuclear Science and Technology, 46(6), 557-563. 37. Seelev, I. N., Barakov, B. N., Ilinykh, Yu. S., & Abrosimov, S. V. (2018). Russian Patent no.2645833 “Protective plug of spent nuclear fuel storage hub and temperature sensor.” 38. Seelev, I. N., Kochergin, A. V., Gamza, Yu. V., Barakov, B. N., Ilinykh, Yu. S., Simonov, V. N., … Rauk, K. V. (2018). 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I., Gostiaeva, E. M., Kildeev, R. I., & Polunin, A. N. (2018). Russian Patent no. 2649561 “Universal arctic vessel of class inf-2.” 45. Toshinskiy, G. I., Komlev, O. G., Dedul, A. V., & Grigoriev, S. A. (2018). Russian Patent no.2671844 “Method of long-term storage of nuclear fuel and tank for cooling and storage for its implementation.” 46. Uiba, V. V., Sneve, M. K., Samoilov, A. S., Shandala, N. K., Simakov, A. V., Kiselev, S. M., … Smit, G. M. (2017). Regulation of spent nuclear fuel handling on the temporal storage facility in Guba Andreeva on the Kola peninsula. Meditsinskaya Radiologiya i Radiatsionnaya Bezopasnost, 62(4), 12-16. 47. Vasiliev, N. D., Ivanov, A. P., Rauk, K. V., Smoliakov, A. N., & Vanifatov, D. S. (2018). Russian Patent no. 2670104 “Ampoule for spent fuel assembly.” 48. Vasilev, A. S., Shegelman, I. R., & Romanov, A. V. (2012). Creation of resource-saving manufacture of environmentally-safe transport package for transportation and storage of spent nuclear fuel. Nauka i Biznes: Puti Razvitiya, 1(7), 58-61. 49. Wannas, F.A. (2019)“Preparation, investigation, chromatographic and bio-studying of new reagents”, International Journal of Pharmaceutical Research, 11 (1), pp. 667-674.
Anatoly I. Ryazantsev, Alexey O. Antipov, Alexey I. Smirnov, Evgeny Yu. Evseev, Andrey A. Authors: Akhtyamov, Georgy K. Rembalovich Technological Features of Irrigation and Assessment Indicators of Multibasic Irrigation Machines Paper Title: Running Systems Efficiency (on the Example of IM Kuban-LK1) Abstract: It is proved that decrease in material capacity of machines and power consumption of irrigation, in particular, of IM Kuban-LK1, is the important direction of increase in economic efficiency of agro-industrial complex as economical expenditure of energy and material resources provides an increase in the output and decrease in prime cost, both for technological means of irrigation, and crop harvesting. At the same time, the equipment - economic indicators of irrigation machines are defined by their constructive and operational characteristics depending on key parameters of machines and on service conditions. The most important of them are productivity and power consumption. 65. The conducted researches IM allowed to establish that decrease in its productivity, often, happens because of the reduction of change working hours efficiency (Kcm). Its decrease is indicated by losses of basic passability of carts of the machines in places on the low bearing ability of the soil (because of the increased rutting) and when 404-406 overcoming rises (because of insufficient coupling properties of running systems, or drive power). It is proved that expedient to carry out the most objective assessment of growth of the technological level of the Kuban-LK1 IM running systems on the generalized efficiency indicator the propeller determining optimum parameters wheel proceeding from the highest indicators of power costs of movement and material capacities.
Keywords: irrigation machine, disk, leveler, irrigation technology.
References: 1. V.I. Bolovnev Defining optimum parameters and choosing the digging machines depending on the operating environment. M, 2010 2. Ryazantsev, A.I. Operating the transport systems of multibasic machines [Text] / A.I. Ryazantsev, A.O. Antipov. - Kolomna: VO Public Educational Institution of MO GSGU, 2016. - 225 pages. 3. Ryazantsev, A.I. Directions of improvement of irrigation machines and systems [Text] / A.I. Ryazantsev. - Ryazan: To FGBOU VPO RGAT, 2013. - 306 4. Ryazantsev A.I., Egorov Y.M. Track leveler of the irrigation machine. The certificate on model No. 15446, Bulletin No. 29, 2000 5. Ryazantsev, A.I. Water conservation while using irrigation devices of multiple supports in the conditions of the Moscow region [Url:] Ryazantsev A.I., Antipov A.O., Olgarenko G.V., Smirnov A.I. Amazonia Investiga. 2019. T. 8. No. 18. Page 323-329. 6. Ryazantsev, A.I. Ecological-energy directions for improving multiple irrigation machines [Url:]//Ryazantsev A.I., Antipov A.O., Olgarenko G.V., Rembolovich G.K., Kostenko M.U. etc.//ARPN Journal of Engineering and Applied Sciences, February 2019 | Vol. 14th No. 3, ISSN 1819-6608 (online).
Authors: P Subhash, Ram Mohan S A Paper Title: Multi-Modal Summarization of Read, Watch, Listen for Text and Multimedia Content Abstract: Conceptual Automatic content summarization is a main NLP apps that intends to consolidate a source content into a shorter adjustment. The quick addition in all kind of data show over the internet requires multi- modal summarization (MMS) from non-simultaneous aggregations of content, picture, sound and video. Here propose an extractive MMS procedure that joins the strategies of NLP, discourse handling and PC vision to examine the rich data contained all kind of data and to get better the idea of multimedia news summarization. The main idea is to associate the semantic openings between multimodel substance. Sound and visual are major modalities in the video. For sound data, we structure an approach to manage explicitly use its interpretation and to find the astounding nature of the translation with sound signals. For visual data, we get acquainted with the joint depictions of content and pictures using a neural framework. By then, we get the incorporation of the made framework for noteworthy visual data through content picture coordinating or multimodal topic showing. Finally, all the multimodal points are considered to make a literary once-over by increasing the striking nature, non- reiteration, clarity and consideration through the arranged streamlining of sub isolated limits.
Keywords: Summarization, Multimedia, Multi-modal, NLP
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Zimmermann, “Leveraging multimodal information for event summarization and concept-level sentiment analysis,” Knowledge-Based Systems, vol. 108, pp. 102–109, 2016. 16. S. Khuller, A. Moss, and J. S. Naor, “The budgeted maximum coverage problem,” Information Processing Letters, vol. 70, no. 1, pp. 39– 45, 1999. 17. V. Varma, V. Varma, and V. Varma, “Sentence position revisited: a robust light-weight update summarization ’baseline’ algorithm,” in International Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies, 2009, pp. 46– 52. 18. Y. Ouyang, W. Li, Q. Lu, and R. Zhang, “A study on position information in document summarization,” in COLING, 2010, pp. 919–927. 19. D. R. Radev, H. Jing, M. Sty´s, and D. Tam, “Centroid-based summarization of multiple documents,” Information Processing Management, vol. 40, no. 6, pp. 919–938, 2004. 20. R. Mihalcea and P. Tarau, “Textrank: Bringing order into texts,” in ACL, 2004. 21. X. Wan and J. Yang, “Improved affinity graph based multidocument summarization,” in NAACL, 2006, pp. 181–184. 22. G. Erkan and D. R. Radev, “Lexrank: Graph-based lexical centrality as salience in text summarization,” Journal of Qiqihar Junior Teachers College, vol. 22, p. 2004, 2011. 23. X. Zhou, X. Wan, and J. Xiao, “Cminer: Opinion extraction and summarization for chinese microblogs,” IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 7, pp. 1650–1663, 2016. X. Li, L. Du, and Y. D. Shen, “Update summarization via graphbased sentence ranking,” IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. 5, pp. 1162–1174, 2013. Authors: Mohit Anguralaa, Manju Balab, Sukhvinder Singh Bamberc 67. Paper Title: Re-Powering Technique to compare its suitability with On-Demand Distance Vector Routing Protocols Abstract: A Wireless Sensor Networks consist of several units such as the radio, the memory, and the, microcontroller that uses the most power. One of the major confronts in these networks is to aim dynamic routing protocols which consume less overhead. Power consumption decides the life span of WSNs. This research work emphasis on minimizing the power consumption to avoid packet loss of WSNs. Therefore, a communication protocol Ad-hoc 0n-demand Multipath Distance Vector Routing protocol and Ad-hoc 0n-demand Distance Vector Routing protocol, both are tested when power is regenerated by using external wireless node. WSNs are a highly dynamic wireless network which can form without the need of any pre-existing infrastructure. This paper focuses on the Ad-hoc 0n-demand Multipath Distance Vector Routing protocol and Ad-hoc 0n-demand Distance Vector Routing protocol comparison when a technique of re-powering introduced. Further, the numerical outcomes find out the optimum routing protocol among these.
Keywords: AODV, AOMDV, Power Consumption, Re-Powering, WSN.
References: 1. Caro, G.D. and Dorigo, M. (1997), “AntNet: A Mobile Agents Approach to Adaptive Routing,” Technical Report IRIDIA/97-12, Universite Libre of de Bruxelles, Brussels. 2. Hussein, O., Saadawi, T. and Lee, M.J. (2005), “Probability Routing Algorithm for Mobile Ad Hoc Networks’ Resources Management,” 412-414 IEEE Journal on Selected Areas in Communications, 23, 2248-2259. 3. Shuang, B., Li, Y., Li, Z. and Chen, J. (2007), “An Ant-Based On-Demand Energy Routing Protocol for Ad Hoc Wireless Networks,” WiCom’07: Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, 21-25 September 2007, 1516-1519. 4. Ribeiro, L.B., and de Castro, M.F. (2010), “BioSel: A Bio-Inspired Routing Algorithm for Sensor Network Lifetime Optimization,” International Conference on Telecommunications, 728-734. 5. Wang, G.F., Wang, Y. and Tao, X.L. (2009), “An Ant Colony Clustering Routing Algorithm for Wireless Sensor Networks,” Proceeding of 3rd International Conference on Genetic and Evolutionary Computing, Guilin, 14-17 October 2009, 670-673. 6. M. Gatzianas and L. Georgiadis (2008), “A distributed algorithm for maximum lifetime routing in sensor networks with mobile sink,” IEEE Trans. Wireless Communications, vol. 7, no. 3, pp. 984-994 March. 7. M. Ma and Y. Yang (2007), “SenCar: An energy-efficient data gathering mechanism for large-scale multi-hop sensor networks,” IEEE Trans. Parallel and distributed systems, vol. 18, no. 10, pp. 1476-1488. 8. M. Ma, Y. Yang and M. Zhao (2013), “Tour planning for mobile data gathering mechanisms in wireless sensor networks,” IEEE Trans. Vehicular Technology, vol. 62, no. 4, pp. 1472-1483. 9. T. Ming-hao, Y. Ren-lai, L.shu-jiang and W. Xiang-dong, “Multipath routing protocol with load balancing in WSN considering interference,” IEEE conference on Industrial Electronics and applications, 2011. 10. Y.Li, Z.jang, Q.Zhang, “Efficient load balance data aggregation methods for WSN based on compressive network coding,” IEEE International Conference on Electronic Information and Communication Technology (ICEICT), 2017. Authors: Vinod Kumar, Sanjeev Kumar Dhull Paper Title: An Experimental Analysis of MVDR and MUSIC Algorithm Abstract: This paper shows a experimental analysis of between MVDR algorithm (Minimum Variance Distortionless Response) and MUSIC (Multiple Signal classification) algorithm. These algorithms are used in direction findings for smart antenna. This paper shows the comparison between these algorithms based on the certain parameters like total no. of antennas, spacing between them, number of snapshot. At last, the Results obtained from MUSIC algorithm has been found better than MVDR algorithm.
Keywords: MUSIC, MVDR, Snapshot, Smart Antenna.
References: 68. 1. T. J. Shan, M. Wax and T. Kailath, “On Partial Smoothing for Direction of Arrival Estimation of Coherent Signals”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 33, No. 4, pp. 806-811,1985. 415-417 2. R. O. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation”, IEEE Transactions on Antennas and Propagation, Vol. 34, No.3, pp. 276-280, 1986. 3. H. Krim and M. Viberg, “Two Decades of Array Signal Processing Research: The Parametric Approach”, IEEE Signal Processing Magazine, pp 67-94, 1996. 4. Y. Khmou, S. Safi and M. Frikel, “Comparative Study between several direction of arrival estimation Methods” Journal of Telecommunications and Information Technology, pp 41-48, 2014. 5. Z. Chen, G. Gokeda and Y Yu, “Introduction to direction of arrival Estimation”, Boston, USA, Artech House, 2010. 6. J. Chen, Y. Wu, H. Cao and H. Wang, “Fast Algorithm for DOA Estimation with Partial Covariance Matrix and without Eigen decomposition”, Journal of Signal and Information Processing, Vol. No. 2, pp. 266-269, 2011. 7. P. Gupta and S. P. kar, “Music and Improved Music algorithm to Estimate Direction of Arrival”, IEEE Conference, pp. 0757-0761, 2015. 8. M. M. Abdalla, M.B. Abuitbel and M. A. Hassan, “Performance Evaluation of direction of arrival estimation of Music and Esprit algorithms for mobile communication systems”, 6th joint IFIP wireless and mobile networking conference, 2013. Authors: Vidya Chitre, Deven Shah Paper Title: Optimizing Task Scheduling for HPC using Software Defined Network Abstract: Features of cloud computing in terms of reliability, scalability, and resource pooling have inveigled scientists to deploy high performance computing (HPC) applications on cloud. Nevertheless, HPC applications have major challenges on cloud that emasculate gained benefits. This study aims to analyze challenges that are 69. faced by HPC applications on cloud and focus on how the performance of cloud can be increased through a platform. In this paper, different approaches are discussed for improving HPC task scheduling performance on 418 - 423 cloud using software-defined networking. Moreover, a task scheduling architecture is proposed to manage task scheduling of HPC applications, which can be later on used as a service to improve the profit of service providers. Bandwidth and capacity of virtual machines are the main parameters that are considered.
Keywords: Software Defined Network, Task Scheduling, HPCaaS, Mininet, Virtualization
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Authors: Jaspalsinh .B Dabhi, Ajitkumar N. Shukla, SukantaKumar Dash Paper Title: Experimental and ANOVA Analysis of Adsorption Cooling System Abstract: A Wireless Sensor Networks consist of several units such as the radio, the memory, and the, microcontroller that uses the most power. One of the major confronts in these networks is to aim dynamic routing protocols which consume less overhead. Power consumption decides the life span of WSNs. This research work emphasis on minimizing the power consumption to avoid packet loss of WSNs. Therefore, a communication protocol Ad-hoc 0n-demand Multipath Distance Vector Routing protocol and Ad-hoc 0n-demand Distance Vector Routing protocol, both are tested when power is regenerated by using external wireless node. WSNs are a highly 70. dynamic wireless network which can form without the need of any pre-existing infrastructure. This paper focuses on the Ad-hoc 0n-demand Multipath Distance Vector Routing protocol and Ad-hoc 0n-demand Distance Vector 424 - 436 Routing protocol comparison when a technique of re-powering introduced. Further, the numerical outcomes find out the optimum routing protocol among these.
Keywords: AODV, AOMDV, Power Consumption, Re-Powering, WSN.
References: 11. Caro, G.D. and Dorigo, M. (1997), “AntNet: A Mobile Agents Approach to Adaptive Routing,” Technical Report IRIDIA/97-12, Universite Libre of de Bruxelles, Brussels. Authors: Toran Verma, Sipi Dubey Paper Title: Fuzzy-Filtered Neural Network for Rice Disease Diagnosis using Image Analysis Abstract: Image mining plays a vital role in the decision-making process in many application areas. Image mining is part of information processing and management. Plant diseases compromise productivity which impacts social life and economy of the nation. The effective use of agriculture image mining can enhance yield production and give economic benefit to the farmer and the country. The research aimed to automate rice diseases identification using image mining for quick diagnosis of the diseases. The digitally captured disease infected and disinfected plant images stored in the database which carries unique feature descriptor in the form of color information, texture appearance, and spatial-frequency information. In this research, the digitally acquired five categories of infected and one category of disinfected images stored in JPEG format in the database. Each category 71. defines unique image features. The acquired images are accessed in RGB color space and cropped and resized in pre-processing steps. All pre-processed images are segmented using Otsu's two-level threshold on a* components 437 - 446 of L*a*b* color space image. The segmentation process generates three segments for each image. The 54 hybrid features are extracted using image analysis which includes 6 color entropy, 24 texture, and 24 wavelets F-ratio of spatial-frequency components. The two-way ANOVA analysis is applied in wavelet features to evaluate F-ratio. The extracted features are passed in the CART to select relevant features according to the Gini index split point. The CART created a binary decision tree, reduces 54 attributes to only 13 relevant attributes. The CART selected 13 attributes forwarded in FIS for fuzzy filtering which summarized 13 attributes to 6 attributes. The fuzzy filtered outcomes used to train MLPNN using Scaled Conjugate back-propagation training algorithm to design rice diseases recognition model. The CART feature selection and fuzzy filtering process applied to summarize relevant input features which reduce the complexity of MLPNN. The hybrid CART-FIS-MLPNN model gives 97.1% training and 95.47% testing efficiency.
Keywords: Two-way ANOVA, Classification and Regression Tree (CART), Fuzzy Inference System (FIS), Multilayer Perceptron Neural Network (MLPNN), Image Processing, Pattern Recognition.
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Authors: Namarta Paper Title: Solution of Intuitionistic Fuzzy Matrix Games using Centroid Method Abstract: The purpose of this paper is to give a solution procedure for matrix games in fuzzy enviornment. In this paper the payoffs of matrix is represented by trapezoidal intuitionistic fuzzy numbers. Different types of ranking approaches are used to solve matrix games but there exist rare use of centroid concept. In this paper centroid concept is used to rank trapezoidal intuitionistic fuzzy numbers and to solve fuzzy game problem. In this paper a relation is also given to convert trapezodial numbers into triangular intuitionistic fuzzy numbers. A numerical example is also given to justify the proposed ranking method.
Keywords: Fuzzy matrix game, Trapezodial Intuitionistic fuzzy numbers, Triangular intuitionistic fuzzy numbers, Ranking function, centroid method.
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Sharma,T S.C.T &T Kumar,T G.T (2015).T “AnT algorithmT toT solveT theT gamesT underT incompleteT information”,T AnnalsT ofT pureT andT appliedT mathematics,10(T 2),T 221-228. 29. Swarup,T K.,T Gupta,T P.K.,T Mohan,M.,T (2010).T “OperationT Research”,T SultanchandT andT sons. 30. Zadeh,T L.AT (1965).T “FuzzyT sets”,T InformationT andT Control,T 8(3),T 338-353. Authors: Reshmy Raj, Dr Sreenath Muraleedharan K. An Experimental Analysis on Reading historical narrative as literary Artefact: a Metahistorical Paper Title: Analysis of Manu s. Pillai‟s Rebel Sultans Abstract: ‘Isotoria’, the Greek term for history defines history as an enquiry or an exploration of archives and historical evidence. But it is through the narrative part that the historical imagination of the historian is transferred. Hence the process of writing of history involves a scientific and creative procedure. The creative part of historiography is centred on the ‘historical narrative’ which makes the core of this study. The primary text selected for the study is Rebel Sultans authored by Manu S. Pillai. The study tries to trace the narrative strategies that enabled the author to convert historical evidence into a proper historical narrative. The theoretical framework adopted for the analysis comes from Metahistory: Historical Imagination in Nineteenth-Century Europe, the seminal work of the American historiographer Hayden White, published in 1973. The study analyses the process 73. of construction of the narrative of Rebel Sultans based on the five levels of conceptualisation proposed by Hayden White such as chronicles, story, mode of emplotement, mode of argument and mode of ideology. 453-457 Keywords: History, historiography, narrativity, metahistory, chronicle, historical narrative, literary artefact, objectivity, Rebel Sultans etc.
References: 1. (MLA HANDBOOK, EIGHTH EDITION) 2. Carr, Edward Hallett. What is History?. Penguin UK,2018. 3. Collingwood, Robin George, and Robin George Collingwood. The Idea of History. Oxford University Press on Demand, 1994. 4. Pillai, Manu S. Rebel Sultans: The Deccan from Khilji to Shivaji. Juggernaut Books,2018. 5. Walia, Shelley. Between Truth and History: Perspectives on Culture, Politics and Theory. Sterling Publishers Pvt.ltd, 2000. 6. White, Hayden. Metahistory: The Historical Imagination in Nineteenth-Century Europe. Johns Hopkins University Press,1987. Authors: Smita Sharma, Sanjay Tyagi Paper Title: Privacy Preservation in Cloud Computing: An Experimental Analysis Abstract: Cloud Computing has become popular among organizations for managing data because of its low cost, robustness, scalability and high availability. Privacy issues are major concern while outsourcing the data on cloud. In this paper, the authors have provided a review of the existing techniques for preserving privacy in cloud computing environment.
Index Terms: Cloud Computing, Privacy Preservation, Security.
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