Concepts and Real-Time Applications of Deep Learning (EAI/Springer Innovations in Communication and Computing) - Tapa dura

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Prof. Smriti Srivastava received the B.E. degree in electrical engineering and the M.Tech. degree in heavy electrical equipment from Maulana Azad College of Technology [now Maulana Azad National Institute of Technology (MANIT)], Bhopal, India, in 1987 and 1991, respectively, and the Ph.D. degree in intelligent control from the Indian Institute of Technology, New Delhi, India, in 2005. From 1988 to 1991, she was a faculty member with MANIT, and since August 1991, she has been with the Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India, where she is presently working as Professor in the Instrumentation and Control Engineering Division at NSIT, New Delhi from September 2008 to till date and Dean Under Graduate Studies. She also worked as Associate Head of Instrumentation & Control Engineering Division at NSIT, New Delhi from April 2004 to Nov. 2007 and from September 2008 to December 2011. She is the author of a number of publications in Transactions, journals and conferences in the areas of neural networks, fuzzy logic, and control systems. She has given a number of invited talks and tutorials mostly in the area of fuzzy logic, process control, and neural networks. Her current research interests include neural networks, fuzzy logic, and hybrid methods in modeling, identification and control of nonlinear systems. She is the reviewer of Reviewer of IEEE Transactions on Systems, Man and Cybernetics (SMC), Part-B. IEEE Transactions on Fuzzy Systems, International Journal of Applied Soft Computing (Elsevier), International Journal of Energy, Technology and Policy (Inder Science). She is the member of World Scientific and Engineering Academy and Society (WSEAS) working committee on computers. She is also on the Editorial board of Scientific and Academic publishing

 

Dr. Manju Khari Is an Assistant Professor in Ambedkar Institute of Advanced Communication Technology and Research, Under Govt. Of NCT Delhi affiliated with Guru Gobind Singh Indraprastha University, Delhi, India. She is also the Professor- In-charge of the IT Services of the Institute and has experience of more than twelve years in Network Planning & Management. She holds a Ph.D. in Computer Science & Engineering from National Institute Of Technology Patna and She received her master's degree in Information Security from Ambedkar Institute of Advanced Communication Technology and Research, formally this institute is known as Ambedkar Institute Of Technology affiliated with Guru Gobind Singh Indraprastha University, Delhi, India. Her research interests are software testing, information security, optimization, Image processing and machine learning. She has 70 published papers in refereed National/International Journals & Conferences (viz. IEEE, ACM, Springer, Inderscience, and Elsevier), 06 book chapters in a springer. She is also co-author of two books published by NCERT of Secondary and senior Secondary School.

 

Dr. Rubén González Crespo has a Ph.D. in Computer Science Engineering. Currently, he is Vice-Chancellor of Academic Affairs and Faculty from UNIR and Global Director of Engineering Schools from PROEDUCA Group. He is an advisory board member for the Ministry of Education at Colombia and evaluator from the National Agency for Quality Evaluation and Accreditation of Spain (ANECA). He is a member of different committees at ISO Organization. Finally, he has published more than 200 papers in indexed journals and congresses.

 

Dr. Gopal Chaudhary is currently working as an assistant professor in Bharati Vidyapeeth's College of Engineering, Guru Gobind Singh Indraprastha University, Delhi, India. He holds a Ph.D. in Biometrics at the division of Instrumentation and Control engineering, Netaji Subhas Institute of Technology, University of Delhi, India. He received the B.E. degree in electronics and communication engineering in 2009 and the M.Tech. degree in Microwave and optical communication from Delhi Technological University (formerly known as Delhi College of Engineering), New Delhi, India, in 2012. He has 30 publications in refereed National/International Journals & Conferences (Elsevier, Springer, Inderscience) in the area of Biometrics and its applications. His current research interests include soft computing, intelligent systems, information fusion and pattern recognition. He has organized many conferences and special issues.

 

Dr Parul Arora is currently working as an assistant professor in Jaypee institute of information technology, Noida,  India. She received her B.Tech degree from Kurukshetra University in 2003 and M. Tech degree from Maharishi Dayanand University in 2010. She received Ph.D. degree from NSIT, Delhi University in 2018. Her current research interests include soft computing, intelligent systems, information fusion and pattern recognition. He hasorganized many conferences and special issues.

De la contraportada

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more.  The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields.

• Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures;
• Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies;
• Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.