River Publishers Series in Signal, Image and Speech Processing Series

Machine Learning Methods for Signal, Image and Speech Processing

January 2022
More details
  • Publisher
    River Publishers
  • Published
    24th January
  • ISBN 9788770223690
  • Language English
  • Pages 250 pp.
  • Size 6" x 9"
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December 2021
More details
  • Publisher
    River Publishers
  • Published
    29th December 2021
  • ISBN 9788770223683
  • Language English
  • Pages 250 pp.
  • Size 6" x 9"

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and image analysis as well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering).

This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests, etc. This book focuses on AI utilization in the speech, image, communications and virtual reality domains.

1) Evaluation of adaptive algorithms for recognition of cavities in industry

2) Lung Cancer Prediction using Feature selection and Recurrent Residual Convolutional Neural Network (RRCNN)

3) Application of Machine Learning Algorithm for Detecting Leaf Diseases Using Image Processing Schemes

4) COVID-19 Forecasting Using Deep Learning Models

5) 3D Smart learning using machine learning technique

6) Signal Processing for OFDM Spectrum Sensing Approaches in Cognitive Networks

7) A machine learning algorithm for Biomedical signal processing application

8) Reversible Image Data Hiding Based On Prediction-Error of Prediction Error Histogram (Ppeh)

9) Object Detection using Deep Convolutional Neural Network

10) An intelligent patient health monitoring system based on a multi-scale convolutional neural network (MCCN) and Raspberry Pi

Meerja Akhil Jabbar

Dr. Meerja Akhil Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. He obtained his Doctor of Philosophy (Ph.D.) in the year 2015 from JNTUH, Hyderabad, and Telangana, India. He has been teaching for more than 20 years. His research interests include Artificial Intelligence, Big Data Analytics, Bio-Informatics, Cyber Security, Machine Learning, Attack Graphs, and Intrusion Detection Systems.

Kantipudi MVV Prasad

Dr. Kantipudi MVV Prasad received his bachelor's degree in Electronics & Communications Engineering from ASR College of Engineering, Tanuku, India; his master's degree in Digital Electronics and Communication Systems from Godavari Institute of Engineering & Technology, Rajahmundry, India; his Ph.D. from BITS, VTU, Belgum and currently works as Director Of Advancements, Sreyas Institute Of Engineering & Technology, Hyderabad. He has been teaching for about 10 years. Previously he was working with RK University, Rajkot. His current research interests are in Signal Processing and Machine Learning, Education and Research.

Sheng-Lung Peng

Dr. Sheng-Lung Peng obtained his Doctor of Philosophy in Computer Science, National Tsing Hua University, Taiwan in 1999. He is a professor in the Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan. Presently he is a Director, Information Service Association of Chinese Colleges, Taiwan and Taiwan Regional Contest Director of ACM-ICPC.

Mamun Bin Ibne Reaz

Dr. Mamun Bin Ibne Reaz received his B.Sc. and M.Sc. degrees in Applied Physics and Electronics from University of Rajhashi, Bangladesh, in 1985 and 1986, respectively. He received his D.Eng. degree in 2007 from Ibaraki University, Japan. He is currently an associate professor in the Universiti Kebangsaan Malaysia, Malaysia involved in teaching, research, and industrial consultation. He has been a regular associate of the Abdus Salam International Center for Theoretical Physics since 2008. He has vast research experiences in Norway, Ireland, and Malaysia. He has published extensively in the area of IC Design and Biomedical Application IC. He is author and co-author of more than 100 research articles in design automation and IC design for biomedical applications.

Ana Maria Madureira

Dr. Ana Maria Madureira got her BSc degree in Computer Engineering in 1993 from ISEP; her master's degree in Electrical and Computers Engineering Industrial Informatics in 1996 from FEUP; and her Ph.D. degree in Production and Systems in 2003, from University of Minho, Portugal. She became an IEEE Senior Member in 2010. She was Chair of IEEE Portugal Section (2015-2017), Vice-chair of IEEE Portugal Section, Teacher Coordinator (Polytechnic Teacher) Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto, Portugal.

Signal processing, Machine learning, Deep learning, Image Processing, Speech Processing; ODFM; 3D smart learning; object detection; Ppeh; MCCN