Machine learning methods for signal, image and speech processing /

Mostrar otras versiones (1)

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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Jabbar, M. A. (Autor)
Otros Autores: Prasad, Kantipudi MVV (Editor ), Peng, Sheng-Lung, Bin Ibne Reaz, Mamun (Editor ), Madureira, Ana
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Aalborg : River Publishers, [2021]
Colección:River Publishers series in signal, image and speech processing.
Temas:
Signal processing > Digital techniques.
Image processing > Digital techniques.
Artificial intelligence.
Artificial Intelligence
Traitement du signal > Techniques numériques.
Traitement d'images > Techniques numériques.
Intelligence artificielle.
digital imaging.
artificial intelligence.
SCIENCE > Energy.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

Ejemplares similares