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Machine learning methods for signal, image and speech processing /

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Bibliographic Details
Main Author: Jabbar, M. A. (Author)
Corporate Author: ProQuest (Firm)
Other Authors: Kantipudi, M. V. V. Prasad, Peng, Sheng-Lung, Reaz
Format: eBook
Language:English
Published: Aalborg : River Publishers, 2021.
Series:River Publishers series in signal, image and speech processing.
Subjects:
Signal processing > Digital techniques.
Image processing > Digital techniques.
Artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
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)