Machine Learning Methods for Signal, Image and Speech Processing.

• Author: Jabbar, M. A.
• Place / Publishing House: Aalborg : : River Publishers,, 2021.; Ã2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (258 pages)

Table of 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
• References
• 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
• References
• 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
• References
• 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
• References
• 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
• References
• 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
• References
• 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
• References
• 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
• References
• 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
• References
• Index
• About the Editors
• Back Cover.