The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research.
Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications.
Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems.
The book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning.
Audience
The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.
Table of Contents
Preface xiii
Part I: Cognitive Analytics in Continual Learning 1
1 Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research 3
Renuga Devi T., Muthukumar K., Sujatha M. and Ezhilarasie R.
1.1 Introduction 4
1.2 Evolution of Data Analytics 5
1.3 Conceptual View of Cognitive Systems 7
1.4 Elements of Cognitive Systems 7
1.5 Features, Scope, and Characteristics of Cognitive System 9
1.6 Cognitive System Design Principles 12
1.7 Backbone of Cognitive System Learning/Building Process 13
1.8 Cognitive Systems vs. AI 17
1.9 Use Cases 18
1.10 Conclusion 25
2 Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model 29
Sasikumar A., Logesh Ravi, Malathi Devarajan, Hossam Kotb and Subramaniyaswamy V.
2.1 Introduction 30
2.2 Smart City Applications 32
2.3 Related Work 36
2.4 Proposed Cognitive Computing RL Model 39
2.5 Simulation Results 45
2.6 Conclusion 47
3 Deep Recommender System for Optimizing Debt Collection Using Reinforcement Learning 51
Keerthana S., Elakkiya R. and Santhi B.
3.1 Introduction 52
3.2 Terminologies in RL 54
3.3 Different Forms of RL 57
3.4 Related Works 59
3.5 Proposed Methodology 62
3.6 Result Analysis 66
3.7 Conclusion 68
Part II: Computational Intelligence of Reinforcement Learning 73
4 Predicting Optimal Moves in Chess Board Using Artificial Intelligence 75
Thangaramya K., Logeswari G., Sudhakaran G., Aadharsh R., Bhuvaneshwar S., Dheepakraaj R. and Parasu Sunny
4.1 Introduction 76
4.2 Literature Survey 83
4.3 Proposed System 88
4.4 Results and Discussion 95
4.5 Conclusion 98
5 Virtual Makeup Try-On System Using Cognitive Learning 103
Divija Sanapala and J. Angel Arul Jothi
5.1 Introduction 104
5.2 Related Works 105
5.3 Proposed Method 111
5.4 Experimental Results and Analysis 118
5.5 Conclusion 119
6 Reinforcement Learning for Demand Forecasting and Customized Services 123
Sini Raj Pulari, T. S. Murugesh, Shriram K. Vasudevan and Akshay Bhuvaneswari Ramakrishnan
6.1 Introduction 124
6.2 RL Fundamentals 125
6.3 Demand Forecasting and Customized Services 130
6.4 eMart: Forecasting of a Real-World Scenario 131
6.5 Conclusion and Future Works 133
7 COVID-19 Detection through CT Scan Image Analysis: A Transfer Learning Approach with Ensemble Technique 135
P. Padmakumari, S. Vidivelli and P. Shanthi
7.1 Introduction 136
7.2 Literature Survey 137
7.3 Methodology 140
7.4 Results and Discussion 144
7.5 Conclusion 148
8 Paddy Leaf Classification Using Computational Intelligence 151
S. Vidivelli, P. Padmakumari and P. Shanthi
8.1 Introduction 151
8.2 Literature Review 153
8.3 Methodology 155
8.4 Results and Discussion 160
8.5 Conclusion 163
9 An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques 167
M. Sharmila Begum, A. V. M. B. Aruna, A. Balajee and R. Murugan
9.1 Introduction 168
9.2 Literature Survey 169
9.3 Proposed Methodology 171
9.4 Experimental Results 182
9.5 Conclusion 185
Part III: Advancements in Cognitive Computing: Practical Implementations 189
10 Fuzzy-Based Efficient Resource Allocation and Schedulingin a Computational Distributed Environment 191
Suguna M., Logesh R. and Om Kumar C. U.
10.1 Introduction 192
10.2 Proposed System 193
10.3 Experimental Results 196
10.4 Conclusion 201
11 A Lightweight CNN Architecture for Prediction of Plant Diseases 203
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A. and Subramaniyaswamy V.
11.1 Introduction 204
11.2 Precision Agriculture 206
11.3 Related Work 211
11.4 Proposed Architecture for Prediction of Plant Diseases 214
11.5 Experimental Results and Discussion 217
11.6 Conclusion 219
12 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification 223
P. Saravanan, V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy
12.1 Introduction 224
12.2 Literature Review 227
12.3 Proposed Feature Fusioned Dictionary Learning Model 229
12.4 Experimental Results and Discussion 232
12.5 Conclusion and Future Work 235
13 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure 239
Akshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari and Shriram K. Vasudevan
13.1 Introduction 240
13.2 Cognitive Computing in Action 241
13.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing 243
13.4 Cognitive Solutions Revolutionizing the Healthcare Industry 246
13.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study) 249
13.6 Conclusion and Future Work 251
14 Automating ESG Score Rating with Reinforcement Learning for Responsible Investment 253
Mohan Teja G., Logesh Ravi, Malathi Devarajan and Subramaniyaswamy V.
14.1 Introduction 254
14.2 Comparative Study 259
14.3 Literature Survey 263
14.4 Methods 266
14.5 Experimental Results 273
14.6 Discussion 277
14.7 Conclusion 278
15 Reinforcement Learning in Healthcare: Applications and Challenges 283
Tribhangin Dichpally, Yatish Wutla and Sheela Jayachandran
15.1 Introduction 283
15.2 Structure of Reinforcement Learning 285
15.3 Applications 289
15.4 Challenges 310
15.5 Conclusion 312
16 Cognitive Computing in Smart Cities and Healthcare 317
Dave Mahadevprasad V., Ondippili Rudhra and Sanjeev Kumar Singh
16.1 Introduction 318
16.2 Machine Learning Inventions and Its Applications 322
16.3 What is Reinforcement Learning and Cognitive Computing? 326
16.4 Cognitive Computing 327
16.5 Data Expressed by the Healthcare and Smart Cities 331
16.6 Use of Computers to Analyze the Data and Predict the Outcome 332
16.7 Machine Learning Algorithm 332
16.8 How to Perform Machine Learning? 336
16.9 Machine Learning Algorithm 338
16.10 Common Libraries for Machine Learning Projects 340
16.11 Supervised Learning Algorithm 341
16.12 Future of the Healthcare 343
16.13 Development of Model and Its Workflow 346
16.13.1 Types of Evaluation 347
16.14 Future of Smart Cities 347
16.15 Case Study I 349
16.16 Case Study II 352
16.17 Case Study III 355
16.18 Case Study IV 358
16.19 Conclusion 360
References 360
Index 365
