
Cloud-based Multi-Modal Information Analytics
Description
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Features
Life cycle of the multi- modal data analytics is discussed with applications of modalities of text, image, and video.
Deep Learning fundamentals and architectures covering convolutional Neural Networks, recurrremt neural networks, and types of learning for different multi-modal networks.
Applications of Multi-Modal Analytics covering Text , Speech, and Image.
This book is aimed at researchers in Multi-modal analytics and related areas
More details
Other editions
Additional editions


Persons
Dr. Siddesh G M is currently working as professor in Department of Computer Science & Engineering (Cyber Security), M S Ramaiah Institute of Technology, Bangalore. He has published a good number of research papers in reputed International Conferences and Journals. He is a member of ISTE, IETE etc., He has authored books on Network Data Analytics, Statistical Programming in R, Internet of Things with Springer, Oxford University Press and Cengage publishers respectively. He has edited research monographs in the area of Cyber Physical Systems, Fog Computing and Energy Aware Computing, Bioinformatics with CRC Press, IGI Global and Springer publishers respectively. His research interests include Internet of Things, Distributed Computing and Data Analytics.
Dr. Srinivasa K G is a Professor of Data Science and Artificial Intelligence Programme at DSPM IIIT-Naya Raipur, C. G. India. Earlier he worked as a Professor at Information Management and Emerging Engineering Department of National Institute of Technical Teachers Training and Research, Chandigarh an autonomous Institute under Ministry of Education, Government of India. He also worked as an Associate Professor at CBP Government Engineering College, New Delhi (through UPSC) between 2016 - 19. He also served as Professor in the Department of CSE at M S Ramaiah Institute of Technology, Bangalore between 2003 - 2016.
He received his Ph.D. in Computer Science and Engineering from Bangalore University in 2007. He is the recipient of All India Council for Technical Education - Career Award for Young Teachers, Indian Society of Technical Education - ISGITS National Award for Best Research Work Done by Young Teachers, Institution of Engineers (India) - IEI Young Engineer Award in Computer Engineering, Rajarambapu Patil National Award for Promising Engineering Teacher Award from ISTE - 2012, IMS Singapore - Visiting Scientist Fellowship Award. He has published more than 150 research papers in International Conferences and Journals. He has visited many Universities abroad as a visiting researcher - He has visited University of Oklahoma, USA, Iowa State University, USA, Hong Kong University, Korean University, National University of Singapore, University of British Columbia, Canada are his few prominent visits. He has authored many books in the area of Learning Analytics, Network Data Analytics, Soft Computing, Social Network Analysis, High Performance Computing, R Programming etc. with prestigious international publishers like Springer, TMH, Oxford, Cengage, and IGI Global. He has edited research monographs in the area of Cyber Physical Systems, Fog Computing and Energy Aware Computing with CRC Press and IGI Global.
He has been awarded BOYSCAST Fellowship by DST, Govt. of India, for conducting post-doctoral research work at University of Melbourne, Australia. He is the principal Investigator for many funded projects from AICTE, UGC, DRDO, and DST. He has undertaken consultancy projects worth 60 lakhs towards conducting Professional Development Programmes under World Bank Project. He is the senior member of IEEE and ACM. His recent research areas include Innovative Teaching Practices in Engineering Education, pedagogy; outcomes based education, and teaching philosophy.
Content
1. Multi-Modal data analytics and lifecycle using Cloud. 2. Cloud Computing. 3. Overview of Deep learning. 4. Deep Learning Platforms and Cloud
Part 2: Architectures & Examples for Multi-Modal data and Analytics using Cloud
5. Neural Networks for Multi-modal data analytics. 6. Cloud examples for Neural Networks Multi-modal architectures. 7. Training Neural Networks on Cloud
Part 3: Cloud based Applications of Multi-Modal Analytics
8. Image Analytics. 9. Text Analytics. 10. Speech Analytics. Exercises.
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