Machine learning algorithms for signal and image processing / edited by Deepika Ghai, Suman Lata Tripathi, Sobhit Saxena, Manash Chanda, Mamoun Alazab

Includes bibliographical references and index.

Section-1 Machine & Deep Learning techniques for Image Processing -- 1.1 Image Features in Machine Learning -- 1.2 Image Segmentation and Classification using Deep Learning -- 1.3 Deep Learning based Synthetic Aperture Radar Image Classification -- 1.4 Design Perspectives of Multitask Deep Learning Models and Applications -- 1.5 Image Reconstruction using Deep Learning -- 1.6 Machine and Deep Learning Techniques for Image Super-Resolution -- Section-2 Machine & Deep Learning techniques for Text and Speech Processing -- 2.1 Machine and Deep Learning Techniques for Text and Speech Processing -- 2.2 Manipuri Handwritten Script Recognition using Machine and Deep Learning -- 2.3 Comparison of Different Text Extraction Techniques for Complex Color Images -- 2.4 Smart Text Reader System for Blind Person using Machine and Deep Learning -- 2.5 Machine Learning Techniques for Deaf People -- 2.6 Design and Development of Chatbot based on Reinforcement Learning -- 2.7 DNN based Speech Quality Enhancement and Multi-speaker Separation for Automatic Speech Recognition System -- 2.8 Design and Development of Real-Time Music Transcription using Digital Signal Processing -- Section-3 Applications of Signal and Image Processing with Machine & Deep learning techniques -- 3.1 Role of Machine Learning in Wrist Pulse Analysis -- 3.2 An Explainable Convolutional Neural Network based Method for Skin Lesion Classification from Dermoscopic Images -- 3.3 Future of Machine-Learning and Deep-Learning in Health-Care Monitoring System -- 3.4 Usage of AI & Wearable IoT Devices for Healthcare Data: A Study -- 3.5 Impact of IoT in Biomedical Applications using Machine and Deep Learning -- 3.6 Wireless Communications using Machine Learning and Deep Learning -- 3.7 Applications of Machine Learning and Deep Learning in Smart Agriculture -- 3.8 Structural Damage Prediction from Earthquakes using Deep Learning -- 3.9 Machine Learning and Deep Learning Techniques in Social Sciences -- 3.1O Green Energy using Machine and Deep Learning -- 3.11 Light Deep CNN Approach for Multi-Label Pathology Classification using Frontal Chest X-Ray -- Index.

"Machine Learning Algorithms for Signal and Image Processing aid the reader in designing and developing real-world applications of societal and industrial needs using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, text processing, etc. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. It will advance the current understanding of various machine and deep learning techniques in terms of their ability to improve upon the existing solutions with accuracy, precision rate, recall rate, processing time or otherwise. The most important is, it aims to bridge the gap among closely related fields of information processing including ML, DL, DSP, Statistics, Kernel Theory and others. It also aims to bridge the gap between academicians, researchers and industry to provide new technological solutions for healthcare, speech recognition, object detection and classification, etc. It will improve upon the current understanding about data collection and data preprocessing of signals and images for various applications, implementation of suitable machine and deep learning techniques for variety of signals and images, as well, possible collaboration to ensure successful design according to industry standards by working in a team. It will be helpful for researchers and designers to find out key parameters for future work in this area. The researchers working on machine and deep learning techniques can correlate their work with real-life applications of smart sign language recognition system, healthcare, smart blind reader system, text to image generation or vice-versa, etc. The book will be of interest to both beginners working in the field of machine and deep learning used for signal and image analysis, interdisciplinary in its nature"-- Provided by publisher.