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Machine Learning Methods for Signal, Image and Speech Processing [Kõva köide]

, , , , (National Taipei University of Business, Taiwan)
  • Formaat: Hardback, 256 pages, kõrgus x laius: 234x156 mm, kaal: 508 g
  • Ilmumisaeg: 30-Oct-2021
  • Kirjastus: River Publishers
  • ISBN-10: 8770223696
  • ISBN-13: 9788770223690
Teised raamatud teemal:
Machine Learning Methods for Signal, Image and Speech ProcessingSuurem pilt
  • Formaat: Hardback, 256 pages, kõrgus x laius: 234x156 mm, kaal: 508 g
  • Ilmumisaeg: 30-Oct-2021
  • Kirjastus: River Publishers
  • ISBN-10: 8770223696
  • ISBN-13: 9788770223690
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9788770223690.html
Märksõnad:
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.
Preface xi
List of Figures xv
List of Tables xix
List of Contributors xxi
List of Abbreviations xxv
1 Evaluation of Adaptive Algorithms for Recognition of Cavities in Dentistry 1(22)
Shashikant Patil
Smita Nirkhi
Suresh Kurumbanshi
Sachin Sonawane
1.1 Introduction
2(1)
1.2 Related Work
3(1)
1.3 Proposed Model for Cavities Detection
4(4)
1.3.1 Pre-processing
5(1)
1.3.2 Contrast Enhancement
6(2)
1.4 Feature Extraction using MPCA and MLDA
8(1)
1.4.1 MPCA
8(1)
1.4.2 MLDA
8(1)
1.5 Classification
9(3)
1.5.1 Classification
10(1)
1.5.2 Nonlinear Programming Optimization
10(2)
1.6 Proposed Artificial Dragonfly Algorithm
12(1)
1.7 Results and Discussion
13(1)
1.8 Result Interpretation
14(2)
1.9 Performance Analysis by Varying Learning Percentage
16(3)
1.10 Conclusion
19(1)
References
20(3)
2 Lung Cancer Prediction using Feature Selection and Recurrent Residual Convolutional Neural Network (RRCNN) 23(24)
Syed Saba Raoof
M.A. Jabbar
Syed Aley Fathima
2.1 Introduction
24(1)
2.2 Related Work
25(5)
2.3 Methodology
30(8)
2.4 Experimental Analysis
38(1)
2.5 Cross Validation
39(2)
2.6 Conclusion
41(1)
References
42(5)
3 Machine Learning Application for Detecting Leaf Diseases with Image Processing Schemes 47(30)
Canavoy Narahari Sujatha
V. Padmavathi
3.1 Introduction
48(7)
3.2 Existing Work on Machine Learning with Image Processing
55(12)
3.3 Present Work of Image Recognition Using Machine
67(6)
3.4 Conclusion
73(1)
References
74(3)
4 Covid-19 Forecasting Using Deep Learning Models 77(22)
Sahiti Cheguru
Prerana CH
K. Tejasree
Tarine Deepthi
Y. Vijayalata
Ghosh Siddhartha
4.1 Introduction
78(1)
4.2 Deep Learning Against Covid-19
79(5)
4.2.1 Medical Image Processing
80(1)
4.2.2 Forecasting Covid-19 Series
81(1)
4.2.3 Deep Learning and IoT
82(1)
4.2.4 NLP and Deep Learning Tools
83(1)
4.2.5 Deep Learning in Computational Biology and Medicine
84(1)
4.3 Population Attributes - Covid-19
84(5)
4.4 Various Deep Learning Model
89(3)
4.4.1 LSTM Model
90(2)
4.4.2 Bidirectional LSTM
92(1)
4.5 Conclusion
92(1)
4.6 Acknowledgement
93(1)
4.7 Figures and Tables Caption List
93(1)
References
93(6)
5 3D Smartlearning Using Machine Learning Technique 99(28)
M. Srilatha
D. Nagajyothi
S. Harini
T. Sushanth
5.1 Introduction
100(7)
5.1.1 Literature Survey
101(6)
5.1.1.1 Machine learning basics
101(1)
5.1.1.1.1 Supervised learning
102(1)
5.1.1.1.2 Unsupervised Learning
102(1)
5.1.1.1.3 Semi supervised learning
102(1)
5.1.1.1.4 Reinforcement learning
102(5)
5.2 Methodology
107(10)
5.2.1 Problem Definition
107(1)
5.2.2 Block Diagram of Proposed System
107(3)
5.2.2.1 myDAQ
107(2)
5.2.2.2 Speaker
109(1)
5.2.2.3 Camera
109(1)
5.2.3 Optical Character Recognition
110(2)
5.2.3.1 Acquisition
110(1)
5.2.3.2 Segmentation
111(1)
5.2.3.3 Pre-Processing
111(1)
5.2.3.4 Feature Extraction
111(1)
5.2.3.5 Recognition
111(1)
5.2.3.6 Post-Processing
111(1)
5.2.4 K-Nearest Neighbors Algorithm
112(1)
5.2.5 Proposed Approach
113(2)
5.2.6 Discussion of Proposed System
115(13)
5.2.6.1 Flow Chart
115(1)
5.2.6.2 Algorithm
116(1)
5.3 Results and Discussion
117(5)
5.4 Conclusion and Future Scope
122(1)
References
122(5)
6 Signal Processing for OFDM Spectrum Sensing Approaches in Cognitive Networks 127(22)
Ishrath Unissa
Md. Aleem
Syed Jalal Ahmad
6.1 Introduction
128(18)
6.1.1 Spectrum Sensing in CRNs
129(1)
6.1.2 Multiple Input Multiple Output OFDM Cognitive Radio Network Technique (MIMO-OFDMCRN)
130(6)
6.1.3 Improved Sensing of Cognitive Radio for MB pectrum using Wavelet Filtering
136(4)
6.1.3.1 MB-Spectrum Sensing Method
136(1)
6.1.3.1.1 Estimation of PSD
137(1)
6.1.3.1.2 Edge detection (a)
137(1)
6.1.3.1.3 Edge detection (b)
138(1)
6.1.3.1.4 Edge classifier
138(1)
6.1.3.1.5 Correction of errors
138(1)
6.1.3.1.6 Generation of spectral mask
139(1)
6.1.3.1.7 Sensing of OFDM signals
139(1)
6.1.4 OFDM-Based Blind Sensing of Spectrum in Cognitive Networks
140(5)
6.1.4.1 Model of the Proposed System
140(3)
6.1.4.2 Constrained GLRT Algorithm
143(1)
6.1.4.3 A Multipath Correlation Coefficient Test
144(1)
6.1.4.4 Probability Calculation
144(1)
6.1.5 Comparative Analysis
145(1)
6.2 Conclusion
146(1)
References
146(3)
7 A Machine Learning Algorithm for Biomedical Signal Processing Application 149(20)
Abhishek Choubey
Shruti Bhargava Choubey
S.P.V Subba Rao
7.1 Introduction
149(4)
7.1.1 Introduction to Signal Processing
149(4)
7.1.1.1 ECG Signal
152(1)
7.2 Related Work
153(7)
7.2.1 Signal Processing Based on Traditional Methods
153(2)
7.2.2 Signal Processing Based on Artificial Intelligence
155(4)
7.2.3 Problem Context
159(1)
7.3 Results and Discussion Based on Recent Work
160(2)
7.4 Real-Time Applications
162(3)
7.5 Conclusion
165(1)
References
166(3)
8 Reversible Image Data Hiding Based on Prediction-Error of Prediction Error Histogram (PPEH) 169(12)
K. Upendra Raju
D. Srinivasulu Reddy
P.E.S.N. Krishna Prasad
8.1 Introduction
170(2)
8.2 Existing Methodology
172(3)
8.2.1 Histogram-Based RDH
173(1)
8.2.2 PEH-Based RDH
174(1)
8.3 Proposed Method
175(2)
8.4 Results and Discussions
177(1)
8.5 Conclusion
178(1)
References
178(3)
9 Object Detection using Deep Convolutional Neural Network 181(24)
G.A.E. Satish Kumar
R. Sumalatha
9.1 Introduction
182(1)
9.2 Related and Background Work
182(1)
9.3 Object Detection Techniques
183(12)
9.3.1 Histogram of Oriented Gradients (HOG)
183(1)
9.3.2 Speeded-up Robust Features (SURF)
184(1)
9.3.3 Local Binary Pattern (LBP)
185(1)
9.3.4 Single Shot MultiBox Detector (SSD)
185(2)
9.3.5 You Only Look Once (YOLO)
187(1)
9.3.6 YOLOv1
188(1)
9.3.7 YOLOv2
189(3)
9.3.8 YOLOv3
192(1)
9.3.9 Regions with CNN (RCNN)
193(1)
9.3.10 Fast RCNN
193(1)
9.3.11 Faster RCNN
194(1)
9.4 Datasets for Object Detection
195(6)
9.5 Conclusion
201(1)
References
201(4)
10 An Intelligent Patient Health Monitoring System Based on A Multi-Scale Convolutional Neural Network (MCCN) and Raspberry Pi 205(22)
Shruti Bhargava Choubey
Abhishek Choubey
10.1 Introduction to Signal Processing
206(4)
10.1.1 Cases of Implanted Frameworks
207(1)
10.1.2 Features of Embedded Systems
208(1)
10.1.3 Domain Applications
209(1)
10.2 Background of the Medical Signal Processing
210(2)
10.2.1 Literature Review
210(2)
10.2.2 Problem Identification
212(1)
10.3 Real-Time Monitoring Device
212(5)
10.3.1 Hardware Design Approach
212(2)
10.3.2 Multi-Scale Convolutional Neural Networks
214(1)
10.3.3 Raspberry Pi
215(1)
10.3.4 16x2 Liquid Crystal Display (LCD)
215(1)
10.3.5 Ubidots
215(1)
10.3.6 Blood Pressure Module
216(1)
10.3.7 Temperature Sensor (TMP103)
216(1)
10.3.8 Respiratory Devices
217(1)
10.3.9 Updation of Data Using MCNN and MATLAB
217(1)
10.4 Outcome and Discussion
217(3)
10.5 Conclusion
220(1)
10.6 Future Work
221(1)
References
222(5)
Index 227(2)
About the Editors 229
M.A. Jabbar, MVV Prasad Kantipudi, Sheng-Lung Peng, Mamun Bin Ibne Reaz, Ana Maria Madureira