Machine Learning Methods for Signal, Image and Speech Processing_Meerja Akhil Jabbar, Kantipudi MVV Prasad, Sheng-Lung Peng.pdf

Machine Learning Methods for Signal, Image and Speech Processing RIVER PUBLISHERS SERIES IN SIGNAL, IMAGE AND SPEECH PROCESSING Series Editors: MARCELO SAMPAIO DE ALENCAR Universidade Federal da Bahia UFBA, Brasil MONCEF GABBOUJ Tampere University of Technology, Finland THANOS STOURAITIS University of Patras, Greece and Khalifa University, UAE The “River Publishers Series in Signal, Image and Speech Processing” is a series of com- prehensive academic and professional books which focus on all aspects of the theory and practice of signal processing. Books published in the series include research monographs, edited volumes, handbooks and textbooks. The books provide professionals, researchers, educators, and advanced students in the eld with an invaluable insight into the latest research and developments. Topics covered in the series include, but are by no means restricted to the following: • • • • • • • • • • • • • • • • • For a list of other books in this series, visit www.riverpublishers.com Machine Learning Methods for Signal, Image and Speech Processing Editors M. A. Jabbar Vardhaman College of Engineering, India Kantipudi MVV Prasad Symbiosis Institute of Technology, India Sheng-Lung Peng National Dong Hwa University, Taiwan Mamun Bin Ibne Reaz Universiti Kebangsaan, Malaysia Ana Madureira Interdisciplinary Studies Research Center, PortugalRiverPublishers Published, sold and distributed by: River Publishers Alsbjergvej 10 9260 Gistrup Denmark www.riverpublishers.com ISBN: 978-87-7022-369-0 (Hardback) 978-87-7022-368-3 (Ebook) ©2021 River Publishers All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Contents Preface xi List of Figures xv List of Tables xix List of Contributors xxi List of Abbreviations xxv 1 Evaluation of Adaptive Algorithms for Recognition of Cavities