Machine Learning Methods for Signal, Image and Speech Processing
eBook - ePub

Machine Learning Methods for Signal, Image and Speech Processing

  1. 250 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Machine Learning Methods for Signal, Image and Speech Processing

About this book

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 yirtual reality domains.

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Yes, you can access Machine Learning Methods for Signal, Image and Speech Processing by M.A. Jabbar,MVV Prasad Kantipudi,Sheng-Lung Peng,Mamun Bin Ibne Reaz,Ana Maria Madureira in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Energy. We have over one million books available in our catalogue for you to explore.

Information

Publisher
River Publishers
Year
2022
Print ISBN
9788770223690
eBook ISBN
9781000791624
Edition
1
Topic
Physical Sciences
Subtopic
Energy

Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Table of Contents
  7. Preface
  8. List of Figures
  9. List of Tables
  10. List of Contributors
  11. List of Abbreviations
  12. 1 Evaluation of Adaptive Algorithms for Recognition of Cavities in Dentistry
  13. 2 Lung Cancer Prediction using Feature Selection and Recurrent Residual Convolutional Neural Network (RRCNN)
  14. 3 Machine Learning Application for Detecting Leaf Diseases with Image Processing Schemes
  15. 4 COVID-19 Forecasting Using Deep Learning Models
  16. 5 3D Smartlearning Using Machine Learning Technique
  17. 6 Signal Processing for OFDM Spectrum Sensing Approaches in Cognitive Networks
  18. 7 A Machine Learning Algorithm for Biomedical Signal Processing Application
  19. 8 Reversible Image Data Hiding Based on Prediction-Error of Prediction Error Histogram (PPEH)
  20. 9 Object Detection using Deep Convolutional Neural Network
  21. 10 An Intelligent Patient Health Monitoring System Based on A Multi-Scale Convolutional Neural Network (MCCN) and Raspberry Pi
  22. Index
  23. About the Editors