Deep Learning Applications, Volume 2

Dr. M. Arif Wani is a Professor at the University of Kashmir, having previously served as a Professor at California State University, Bakersfield. He completed his M.Tech. in Computer Technology at the Indian Institute of Technology, Delhi, and his Ph.D. in Computer Vision at Cardiff University, UK. His research interests are in the area of machine learning, with a focus on neural networks, deep learning, inductive learning, and support vector machines, and with application to areas that include computer vision, pattern recognition, classification, prediction, and analysis of gene expression datasets. He has published many papers in reputed journals and conferences in these areas. Dr. Wani has co-authored the book ‘Advances in Deep Learning,’ co-edited the book ‘Deep Learning Applications,’ and co-edited 17 conference proceeding books in machine learning and applications area. He is a member of many academic and professional bodies, e.g., the Indian Society for Technical Education, Computer Society of India, and IEEE USA.  Dr. Taghi M. Khoshgoftaar is the Motorola Endowed Chair professor of the Department of computer and electrical engineering and Computer Science, Florida Atlantic University, and the Director of NSF Big Data Training and Research Laboratory. His research interests are in big data analytics, data mining and machine learning, health informatics and bioinformatics, social network mining, and software engineering. He has published more than 750 refereed journal and conference papers in these areas. He was the Conference Chair of the IEEE International Conference on Machine Learning and Applications (ICMLA 2019). He is the Co-Editor-in-Chief of the Journal of Big Data. He has served on organizing and technical program committees of various international conferences, symposia, and workshops. He has been a Keynote Speaker at multiple international conferences and has given many invited talks at various venues. Also, he has served as North American Editor of the Software Quality Journal, was on the editorial boards of the journals Multimedia Tools and Applications, Knowledge and Information Systems, and Empirical Software Engineering, and is on the editorial boards of the journals Software Quality, Software Engineering and Knowledge Engineering, and Social Network Analysis and Mining.  Dr. Vasile Palade is currently a Professor of Artificial Intelligence and Data Science at Coventry University, UK. He previously held several academic and research positions at the University of Oxford – UK, University of Hull – UK, and the University of Galati – Romania. His research interests are in the area of machine learning, with a focus on neural networks and deep learning, and with main application to image processing, social network data analysis and web mining, smart cities, health, among others. Dr. Palade is author and co-author of more than 170 papers in journals andconference proceedings as well as several books on machine learning and applications. He is an Associate Editor for several reputed journals, such as Knowledge and Information Systems and Neurocomputing. He has delivered keynote talks to international conferences on machine learning and applications. Dr. Vasile Palade is an IEEE Senior Member.

Deep Learning Based Recommender Systems.- A Comprehensive Set of Novel Residual Blocks for Deep Learning Architectures for Diagnosis of Retinal Diseases from Optical Coherence Tomography Images.- Three-Stream Convolutional Neural Network for Human Fall Detection.- Diagnosis of Bearing Faults in Electrical Machines using Long Short-Term Memory.- Automatic Solar Panel Detection from High Resolution Orthoimagery Using Deep Learning Segmentation Networks.- Training Deep Learning Sequence Models to Understand Driver Behavior.- Exploiting Spatio-temporal Correlation in RF Data using Deep Learning.- Human Target Detection and Localization with Radars Using Deep Learning.- Thresholding Strategies for Deep Learning with Highly Imbalanced Big Data.- Vehicular Localisation at High and Low Estimation Rates during GNSS Outages: A Deep Learning Approach.- Multi-Adversarial Variational Autoencoder Nets for Simultaneous Image Generation and Classification.- Non-convex Optimization using Parameter Continuation Methods for Deep Neural Networks.