Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing

Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.

Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

• Speech recognition, image reconstruction, object classification and detection, and text processing
• Healthcare monitoring, biomedical systems, and green energy
• How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
• Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

Dr. Deepika Ghai is Assistant Professor of Signal and Image Processing at Lovely Professional University, India. Dr. Ghai received her PhD from Punjab Engineering College, India.

Dr. Suman Lata Tripathi is Professor of VLSI Design at Lovely Professional University, India. She is an IEEE senior member and received her PhD in microelectronics and VLSI from MNNIT, Allahabad.

Dr. Sobhit Saxena is Associate Professor at Lovely Professional University. He completed his PhD from IIT Roorkee in Nanomaterials.

Dr. Manash Chanda is Assistant Professor in the Department of ECE, Meghnad Saha Institute of Technology, India. He received his PhD in Engineering from Jadavpur University in 2018.

Dr. Mamoun Alazab is Associate Professor at the College of Engineering, IT and Environment at Charles Darwin University, Australia. He has published 150+ research papers in international journals and conferences, such as IEEE Transactions on Industrial Informatics.

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