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Machine learning methods for signal, image and speech processing /

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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...

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Bibliographic Details
Main Author: Jabbar, M. A. (Author)
Corporate Author: Knovel (Firm)
Other Authors: Prasad, Kantipudi MVV (Editor), Peng, Sheng-Lung, Bin Ibne Reaz, Mamun (Editor), Madureira, Ana
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.
Artificial Intelligence
Traitement du signal > Techniques numériques.
Traitement d'images > Techniques numériques.
Intelligence artificielle.
digital imaging.
artificial intelligence.
SCIENCE > Energy.
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)