Machine Learning Methods for Signal, Image and Speech Processing / editors, M. A. Jabbar [and four others].

Format:

Book

Contributor:

Jabbar, M. A., editor.

Series:

River Publishers series in signal, image and speech processing.

River Publishers series in signal, image and speech processing

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Artificial intelligence.

Signal processing--Digital techniques.

Signal processing.

Physical Description:

1 online resource (258 pages)

Edition:

First edition.

Place of Publication:

Gistrup, Denmark : River Publishers, [2021]

Summary:

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.

Contents:

Front Cover

Machine Learning Methods for Signal, Image and Speech Processing

Contents

Preface

List of Figures

List of Tables

List of Contributors

List of Abbreviations

1 Evaluation of Adaptive Algorithms for Recognition of Cavities in Dentistry

1.1 Introduction

1.2 Related Work

1.3 Proposed Model for Cavities Detection

1.3.1 Pre-processing

1.3.2 Contrast Enhancement

1.4 Feature Extraction using MPCA and MLDA

1.4.1 MPCA

1.4.2 MLDA

1.5 Classification

1.5.1 Classification

1.5.2 Nonlinear Programming Optimization

1.6 Proposed Artificial Dragonfly Algorithm

1.7 Results and Discussion

1.8 Result Interpretation

1.9 Performance Analysis by Varying Learning Percentage

1.10 Conclusion

References

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

2.1 Introduction

2.2 Related Work

2.3 Methodology

2.4 Experimental Analysis

2.5 Cross Validation

2.6 Conclusion

3 Machine Learning Application for Detecting Leaf Diseases with Image Processing Schemes

3.1 Introduction

3.2 Existing Work on Machine Learning with Image Processing

3.3 Present Work of Image Recognition Using Machine

3.4 Conclusion

4 COVID-19 Forecasting Using Deep Learning Models

4.1 Introduction

4.2 Deep Learning Against Covid-19

4.2.1 Medical Image Processing

4.2.2 Forecasting COVID-19 Series

4.2.3 Deep Learning and IoT

4.2.4 NLP and Deep Learning Tools

4.2.5 Deep Learning in Computational Biology and Medicine

4.3 Population Attributes - Covid-19

4.4 Various Deep Learning Model

4.4.1 LSTM Model

4.4.2 Bidirectional LSTM

4.5 Conclusion

4.6 Acknowledgement

4.7 Figures and Tables Caption List

5 3D Smartlearning Using Machine Learning Technique.

5.1 Introduction

5.1.1 Literature Survey

5.1.1.1 Machine learning basics

5.1.1.1.1 Supervised learning

5.1.1.1.2 Unsupervised Learning

5.1.1.1.3 Semi supervised learning

5.1.1.1.4 Reinforcement learning

5.2 Methodology

5.2.1 Problem Definition

5.2.2 Block Diagram of Proposed System

5.2.2.1 myDAQ

5.2.2.2 Speaker

5.2.2.3 Camera

5.2.3 Optical Character Recognition

5.2.3.1 Acquisition

5.2.3.2 Segmentation

5.2.3.3 Pre-Processing

5.2.3.4 Feature Extraction

5.2.3.5 Recognition

5.2.3.6 Post-Processing

5.2.4 K-Nearest Neighbors Algorithm

5.2.5 Proposed Approach

5.2.6 Discussion of Proposed System

5.2.6.1 Flow Chart

5.2.6.2 Algorithm

5.3 Results and Discussion

5.4 Conclusion and Future Scope

6 Signal Processing for OFDM Spectrum Sensing Approaches in Cognitive Networks

6.1 Introduction

6.1.1 Spectrum Sensing in CRNs

6.1.2 Multiple Input Multiple Output OFDM Cognitive Radio Network Technique (MIMO-OFDMCRN)

6.1.3 Improved Sensing of Cognitive Radio for MB pectrum using Wavelet Filtering

6.1.3.1 MB-Spectrum Sensing Method

6.1.3.1.1 Estimation of PSD

6.1.3.1.2 Edge detection (a)

6.1.3.1.3 Edge detection (b)

6.1.3.1.4 Edge classifier

6.1.3.1.5 Correction of errors

6.1.3.1.6 Generation of spectral mask

6.1.3.1.7 Sensing of OFDM signals

6.1.4 OFDM-Based Blind Sensing of Spectrum in Cognitive Networks

6.1.4.1 Model of the Proposed System

6.1.4.2 Constrained GLRT Algorithm

6.1.4.3 A Multipath Correlation Coefficient Test

6.1.4.4 Probability Calculation

6.1.5 Comparative Analysis

6.2 Conclusion

7 A Machine Learning Algorithm for Biomedical Signal Processing Application

7.1 Introduction

7.1.1 Introduction to Signal Processing

7.1.1.1 ECG Signal

7.2 Related Work.

7.2.1 Signal Processing Based on Traditional Methods

7.2.2 Signal Processing Based on Artificial Intelligence

7.2.3 Problem Context

7.3 Results and Discussion Based on Recent Work

7.4 Real-Time Applications

7.5 Conclusion

8 Reversible Image Data Hiding Based on Prediction-Error of Prediction Error Histogram (PPEH)

8.1 Introduction

8.2 Existing Methodology

8.2.1 Histogram-Based RDH

8.2.2 PEH-Based RDH

8.3 Proposed Method

8.4 Results and Discussions

8.5 Conclusion

9 Object Detection using Deep Convolutional Neural Network

9.1 Introduction

9.2 Related and Background Work

9.3 Object Detection Techniques

9.3.1 Histogram of Oriented Gradients (HOG)

9.3.2 Speeded-up Robust Features (SURF)

9.3.3 Local Binary Pattern (LBP)

9.3.4 Single Shot MultiBox Detector (SSD)

9.3.5 You Only Look Once (YOLO)

9.3.6 YOLOv1

9.3.7 YOLOv2

9.3.8 YOLOv3

9.3.9 Regions with CNN (RCNN)

9.3.10 Fast RCNN

9.3.11 Faster RCNN

9.4 Datasets for Object Detection

9.5 Conclusion

10 An Intelligent Patient Health Monitoring System Based on A Multi-Scale Convolutional Neural Network (MCCN) and Raspberry Pi

10.1 Introduction to Signal Processing

10.1.1 Cases of Implanted Frameworks

10.1.2 Features of Embedded Systems

10.1.3 Domain Applications

10.2 Background of the Medical Signal Processing

10.2.1 Literature Review

10.2.2 Problem Identification

10.3 Real-Time Monitoring Device

10.3.1 Hardware Design Approach

10.3.2 Multi-Scale Convolutional Neural Networks

10.3.3 Raspberry Pi

10.3.4 162 Liquid Crystal Display (LCD)

10.3.5 Ubidots

10.3.6 Blood Pressure Module

10.3.7 Temperature Sensor (TMP103)

10.3.8 Respiratory Devices

10.3.9 Updation of Data Using MCNN and MATLAB

10.4 Outcome and Discussion.

10.5 Conclusion

10.6 Future Work

Index

About the Editors

Back Cover.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781000791624

1000791629

9781003338789

100333878X

9781000794748

1000794741

9781523144556

1523144556

9788770223683

8770223688

OCLC:

1290483983