Deep Learning Theory Architectures and Applications in Speech Image and Language Processing 1st Edition by Gyanendra Verma ISBN 9815079239 9789815079234 by Verma, Gyanendra;doriya, Rajesh; instant download after payment.

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ISBN 10: 9815079239 
ISBN 13: 9789815079234
Author: Gyanendra Verma

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Deep Learning Theory Architectures and Applications in Speech Image and Language Processing 1st Table of contents:

1. INTRODUCTION
2. MACHINE-LEARNING
3. Supervised Learning
4. Unsupervised Learning
5. Semi-supervised Learning
6. Active Learning
7. Reinforcement Learning
8. Evolutionary Learning
9. Introduction to Deep Learning
10. APPLICATION OF ML IN MEDICAL IMAGING
11. DEEP LEARNING IN MEDICAL IMAGING
12. Image Classification
13. Object Classification
14. Organ or Region Detection
15. Data Mining
16. The Sign-up Process
17. Other Imaging Applications
18. CONCLUSION
19. CONSENT FOR PUBLICATION
20. CONFLICT OF INTEREST
21. ACKNOWLEDGEMENT
22. REFERENCES
23. Classification Tool to Predict the Presence of Colon Cancer Using Histopathology Images
24. Saleena Thorayanpilackal Sulaiman1,*, Muhamed Ilyas Poovankavil2 and Abdul Jabbar Perumbalath3
25. INTRODUCTION
26. METHODS AND PREPARATION
27. Dataset Preparation
28. Related Works
29. METHODOLOGY
30. Convolutional Neural Network (CNN)
31. ResNet50
32. RESULTS
33. CONCLUSION
34. CONSENT FOR PUBLICATION
35. CONFLICT OF INTEREST
36. ACKNOWLEDGEMENT
37. REFERENCES
38. Deep Learning For Lung Cancer Detection
39. Sushila Ratre1,*, Nehha Seetharaman1 and Aqib Ali Sayed1
40. INTRODUCTION
41. RELATED WORKS
42. METHODOLOGY
43. VGG16 ARCHITECTURE
44. RESNET50 ARCHITECTURE
45. FLOWCHART OF THE METHODOLOGY
46. EXPERIMENTAL RESULTS
47. CONCLUDING REMARKS
48. ACKNOWLEDGEMENTS
49. REFERENCES
50. Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization
51. Jayalakshmi Ramachandran Nair1,*, Sumathy Pichai Pillai2 and Rajkumar Narayanan3
52. INTRODUCTION
53. LITERATURE REVIEW
54. METHODOLOGIES
55. Pre-Upsampling Super Resolution
56. Very Deep Super-Resolution Models
57. Post Upsampling Super Resolution
58. Residual Networks
59. Multi-stage Residual Networks (MDSR)
60. Balanced Two-Stage Residual Networks
61. Recursive Networks
62. Deep Recursive Convolution Network (DRCN)
63. Progressive Reconstruction Networks
64. Attention-Based Network
65. Pixel Loss
66. Perceptual Loss
67. Adversarial Loss
68. SYSTEM TOOLS
69. FINDINGS
70. CONCLUSION
71. ACKNOWLEDGEMENTS
72. REFERENCES
73. Analyzing the Performances of Different ML Algorithms on the WBCD Dataset
74. Trupthi Muralidharr1,*, Prajwal Sethu Madhav1, Priyanka Prashanth Kumar1 and Harshawardhan Tiwari1
75. INTRODUCTION
76. LITERATURE REVIEW
77. DATASET DESCRIPTION
78. PRE-PROCESSING OF DATA
79. Exploratory Data Analysis(EDA)
80. Model Accuracy: Receiver Operating Characteristic (ROC) curve:
81. RESULTS
82. CONCLUSION
83. ACKNOWLEDGEMENTS
84. REFERENCES
85. Application and Evaluation of Machine LearningAlgorithms in Classifying Cardiotocography(CTG) Signal
86. Deep SLRT: The Development of Deep Learning based Multilingual and Multimodal Sign Language Recognit
87. Natarajan Balasubramanian1 and Elakkiya Rajasekar1,*
88. INTRODUCTION
89. RELATED WORKS
90. Subunit Modelling and Extraction of Manual Features and Non-manual Features
91. Challenges and Deep Learning Methods for SLRT Research
92. THE PROPOSED MODEL
93. Algorithm: 2 NMT-GAN based Deep SLRT Video Generation (Backward)
94. Training Details
95. EXPERIMENTAL RESULTS
96. CONCLUSION
97. ACKNOWLEDGEMENTS
98. REFERENCES
99. Hybrid Convolutional Recurrent Neural Network for Isolated Indian Sign Language Recognition
100. Rajasekar Elakkiya1, Archana Mathiazhagan1 and Elakkiya Rajalakshmi1,*
101. INTRODUCTION
102. RELATED WORK
103. METHODOLOGY
104. Proposed H-CRNN Framework
105. Data Acquisition, Preprocessing, and Augmentation
106. Proposed H-CRNN Architecture
107. Experiments and Results
108. CONCLUSION AND FUTURE WORK
109. ACKNOWLEDGEMENTS
110. REFERENCES
111. A Proposal of an Android Mobile Application for Senior Citizen Community with Multi-lingual Sentimen
112. Harshee Pitroda1,*, Manisha Tiwari1 and Ishani Saha1
113. INTRODUCTION
114. LITERATURE REVIEW
115. Twitter data
116. PROPOSED FRAMEWORK
117. IMPLEMENTATION OVERVIEW
118. Exploratory Data Analysis (EDA)
119. Feature Extraction
120. Classification
121. Support Vector Machine
122. Decision Tree
123. Random Forest
124. Implementation
125. Pickling the Model
126. Translation
127. Integrating with the Android App
128. Code Snippets
129. Support Vector Machine
130. Decision Tree
131. Random Forest
132. RESULTS AND CONCLUSION
133. Results
134. Feature Extraction
135. Classification
136. CONCLUSION
137. CONSENT FOR PUBLICATION
138. CONFLICT OF INTEREST
139. ACKNOWLEDGEMENT
140. REFERENCES
141. Technology Inspired-Elaborative Education Model (TI-EEM): A futuristic need for a Sustainable Educat
142. Anil Verma1, Aman Singh1,*, Divya Anand1 and Rishika Vij2
143. INTRODUCTION
144. BACKGROUND
145. METHODOLOGY
146. RESULT AND DISCUSSION
147. CONCLUSION
148. CONSENT FOR PUBLICATION
149. CONFLICT OF INTEREST
150. ACKNOWLEDGEMENT
151. REFERENCES
152. Knowledge Graphs for Explaination of Black-Box Recommender System
153. Mayank Gupta1 and Poonam Saini1,*
154. INTRODUCTION
155. Introduction to Recommender System
156. Introduction to Knowledge Graphs
157. RECOMMENDER SYSTEMS
158. Types of Recommender Systems
159. KNOWLEDGE GRAPHS
160. Knowledge Graphs for Providing Recommendations
161. Knowledge Graphs for Generating Explanations
162. GENERATING EXPLANATIONS FOR BLACK-BOX RECOMME-NDER SYSTEMS
163. PROPOSED CASE STUDY
164. MovieLens Dataset
165. Modules
166. Knowledge Graph Generation
167. The Proposed Approach for Case Study
168. Results
169. Graph Visualisation
170. CONCLUSION
171. REFERENCES
172. Universal Price Tag Reader for Retail Supermarket
173. Jay Prajapati1,* and Siba Panda1
174. INTRODUCTION
175. LITERATURE REVIEW
176. METHODOLOGY
177. Image Pre-processing and Cropping
178. Optical Character Recognition
179. Price of the product
180. Name of the product
181. Discounted Price
182. RESULTS AND FUTURE SCOPE
183. CONCLUDING REMARKS
184. ACKNOWLEDGEMENTS
185. REFERENCES
186. The Value Alignment Problem: Building Ethically Aligned Machines
187. Sukrati Chaturvedi1,*, Chellapilla Vasantha Lakshmi1 and Patvardhan Chellapilla1
188. INTRODUCTION
189. Value Alignment Problem
190. Approaches for Solving AI-VAP
191. Top-Down Approach
192. Limitations, Issues, and Challenges of Extant Approaches
193. Eastern Perspectives of Intelligence for Solving AI-VAP
194. Proposed Approach
195. CONCLUSION
196. REFERENCES
197. Cryptocurrency Portfolio Management Using Reinforcement Learning
198. Vatsal Khandor1,*, Sanay Shah1, Parth Kalkotwar1, Saurav Tiwari1 and Sindhu Nair1
199. INTRODUCTION
200. RELATED WORK
201. DATASET PRE-PROCESSING
202. Simple Moving Average
203. Moving Average Convergence/Divergence
204. Parabolic Stop and Reverse
205. Relative Strength Index
206. MODELING AND EVALUATION
207. Convolutional Neural Networks (CNN)
208. Dense Neural Network Model
209. CONCLUSION AND FUTURE SCOPE
210. REFERENCES
211. Subject Index

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Tags: Gyanendra Verma, Theory, Architectures