UID:
almahu_9949434944902882
Umfang:
1 online resource (258 pages)
ISBN:
9788770223683
,
8770223688
,
9781003338789
,
100333878X
,
9781000794748
,
1000794741
,
9781000791624
,
1000791629
Serie:
River Publishers series in signal, image and speech processing
Inhalt:
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 signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.
Anmerkung:
6.1.3 Improved Sensing of Cognitive Radio for MB pectrum using Wavelet Filtering.
,
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
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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
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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
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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
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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)
Weitere Ausg.:
Print version: Jabbar, M.A. Machine Learning Methods for Signal, Image and Speech Processing. Aalborg : River Publishers, ©2021
Sprache:
Englisch
DOI:
10.1201/9781003338789
URL:
https://www.taylorfrancis.com/books/9781003338789
