Machine Learning Methods for Signal, Image and Speech Processing.

By: Jabbar, M. AMaterial type: TextTextPublisher: Aalborg : River Publishers, 2021Copyright date: �2021Edition: 1st edDescription: 1 online resource (258 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9788770223683Subject(s): Signal processing--Digital techniques | Artificial intelligence | Image processing--Digital techniquesGenre/Form: Electronic books.Additional physical formats: Print version:: Machine Learning Methods for Signal, Image and Speech ProcessingDDC classification: 003.54 LOC classification: TK5102.9Online resources: Click to View
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.
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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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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