Deep Learning Applications, Volume 3

Deep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting.- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology.- RGB and Depth Image Fusion for Object Detection using Deep Learning.- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis.- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks.- Deep Learning based Time Series Forecasting.- DEAL: Deep Evidential Active Learning for Image Classification.- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification.- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment.- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience.- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.

Dr. M. Arif Wani is Professor at the University of Kashmir, having previously served as 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 many books in 'Machine Learning and Applications' and 'Deep Learning Applications'. He is a member of many academic and professional bodies.

Dr. Bhiksha Raj is Professor in the School of Computer Science at Carnegie Mellon University, with additional affiliations to the Electrical Engineering and Machine Learning departments. He is Fellow of the IEEE. At CMU he leads the Machine Learning for Signal Processing group, which conducts research on Speech and Audio Processing, Machine Learning, Deep Learning, AI, and Security and Privacy Issues in Speech. He also teaches CMU's flagship course on introduction to Deep Learning, a course simultaneously broadcast to multiple countries across the world, and attended by more than 2000 students from many universities and organizations around the world, each semester. He has authored or co-authored more than 300 scientific papers with over 12,000 citations and an H-index greater than 50 and holds numerous patents in the areas of signal processing, machine learning, privacy, audio and speech processing, and artificial intelligence.

Dr. Feng Luo currently is Professor at the School of Computing, Clemson University, and Founding Director of