Machine Learning Algorithms for Signal and Image Processing - Tapa dura

Acerca del autor

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

De la contraportada

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.