Deep Learning for Data Analytics

Himansu Das is working as an as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He has received his B. Tech and M. Tech degree from Biju Pattnaik University of Technology (BPUT), Odisha, India. He has published several research papers in various international journals and conferences. He has also edited several books of international repute. He is associated with different international bodies as Editorial/Reviewer board member of various journals and conferences. He is a proficient in the field of Computer Science Engineering and served as an organizing chair, publicity chair and act as member of program committees of many national and international conferences. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc., His research interest includes Grid Computing, Cloud Computing, and Machine Learning. He has also 10 years of teaching and research experience in different engineering colleges. Chittaranjan Pradhan is working at School of Computer Engineering, KIIT University, India. He obtained his Bachelors, Masters and PhD degree in Computer Science & Engineering stream. His research are includes Information Security, Image Processing, Data Analytics and Multimedia Systems. Dr. Pradhan has published more than 40 articles in the national and international journals and conferences. Also, he has been associated to a number of events organized at national and international level. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc. He has also experience of more than 10 years in teaching and research activities. Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.

• Section I Deep Learning Basics and Mathematical Background1. Introduction to Deep Learning2. Probability and information Theory3. Deep Learning Basics4. Deep Architectures5. Deep Auto-Encoders6. Multilayer Perceptron7. Artificial Neural Network8. Deep Neural Network9. Deep Belief Network10. Recurrent Neural Networks11. Convolutional Neural Networks12. Restricted Boltzmann MachinesSection II Deep Learning in Data Science13. Data Analytics Basics14. Enterprise Data Science15. Predictive Analysis16. Scalability of deep learning methods17. Statistical learning for mining and analysis of big data18. Computational Intelligence Methodology for Data Science19. Optimization for deep learning (e.g. model structure optimization, large-scale optimization, hyper-parameter optimization, etc)20. Feature selection using deep learning21. Novel methodologies using deep learning for classification, detection and segmentationSection III Deep Learning in Engineering Applications22. Deep Learning for Pattern Recognition23. Deep Learning for Biomedical Engineering24. Deep Learning for Image Processing25. Deep Learning for Image Classification26. Deep Learning for Medical Image Recognition27. Deep learning for Remote Sensing image processing28. Deep Learning for Image and Video Retrieval29. Deep Learning for Visual Saliency30. Deep Learning for Visual Understanding31. Deep Learning for Visual Tracking32. Deep Learning for Object Segmentation and Shape Models33. Deep Learning for Object Detection and Recognition34. Deep Learning for Human Actions Recognition35. Deep Learning for Facial Recognition36. Deep Learning for Scene Understanding37. Deep Learning for Internet of Things38. Deep Learning for Big Data Analytics39. Deep Learning for Clinical and Health Informatics40. Deep Learning foe Sentiment Analysis