Cognitive Analytics and Reinforcement Learning

Elakkiya R., PhD, is an assistant professor in the Department of Computer Science at the Birla Institute of Technology & Science in Dubai, UAE. She received a Ph.D. in 2018 and did her doctoral research in sign language recognition. Her research focuses on addressing trending issues in computer science, mathematics, and engineering. Along with publishing two books, 50 research articles, and three patents, she is an editor of the Information Engineering and Applied Computing journal. She received the Young Achiever Award in 2019. Subramaniyaswamy V., PhD, is a professor at the School of Computing at SASTRA Deemed University in Tamilnadu, India. He received a Ph.D. from Anna University in 2013. His research areas include cognitive computing, reinforced learning, recommender systems, artificial intelligence, and the Internet of Things. He has published more than 200 research papers and book chapters in international journals and books.

• Preface xiiiPart I: Cognitive Analytics in Continual Learning 11 Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research 3Renuga Devi T., Muthukumar K., Sujatha M. and Ezhilarasie R.1.1 Introduction 41.2 Evolution of Data Analytics 51.3 Conceptual View of Cognitive Systems 71.4 Elements of Cognitive Systems 71.5 Features, Scope, and Characteristics of Cognitive System 91.6 Cognitive System Design Principles 121.7 Backbone of Cognitive System Learning/Building Process 131.8 Cognitive Systems vs. AI 171.9 Use Cases 181.10 Conclusion 252 Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model 29Sasikumar A., Logesh Ravi, Malathi Devarajan, Hossam Kotb and Subramaniyaswamy V.2.1 Introduction 302.2 Smart City Applications 322.3 Related Work 362.4 Proposed Cognitive Computing RL Model 392.5 Simulation Results 452.6 Conclusion 473 Deep Recommender System for Optimizing Debt Collection Using Reinforcement Learning 51Keerthana S., Elakkiya R. and Santhi B.3.1 Introduction 523.2 Terminologies in RL 543.3 Different Forms of RL 573.4 Related Works 593.5 Proposed Methodology 623.6 Result Analysis 663.7 Conclusion 68Part II: Computational Intelligence of Reinforcement Learning 734 Predicting Optimal Moves in Chess Board Using Artificial Intelligence 75Thangaramya K., Logeswari G., Sudhakaran G., Aadharsh R., Bhuvaneshwar S., Dheepakraaj R. and Parasu Sunny4.1 Introduction 764.2 Literature Survey 834.3 Proposed System 884.4 Results and Discussion 954.5 Conclusion 985 Virtual Makeup Try-On System Using Cognitive Learning 103Divija Sanapala and J. Angel Arul Jothi5.1 Introduction 1045.2 Related Works 1055.3 Proposed Method 1115.4 Experimental Results and Analysis 1185.5 Conclusion 1196 Reinforcement Learning for Demand Forecasting and Customized Services 123Sini Raj Pulari, T. S. Murugesh, Shriram K. Vasudevan and Akshay Bhuvaneswari Ramakrishnan6.1 Introduction 1246.2 RL Fundamentals 1256.3 Demand Forecasting and Customized Services 1306.4 eMart: Forecasting of a Real-World Scenario 1316.5 Conclusion and Future Works 1337 COVID-19 Detection through CT Scan Image Analysis: A Transfer Learning Approach with Ensemble Technique 135P. Padmakumari, S. Vidivelli and P. Shanthi7.1 Introduction 1367.2 Literature Survey 1377.3 Methodology 1407.4 Results and Discussion 1447.5 Conclusion 1488 Paddy Leaf Classification Using Computational Intelligence 151S. Vidivelli, P. Padmakumari and P. Shanthi8.1 Introduction 1518.2 Literature Review 1538.3 Methodology 1558.4 Results and Discussion 1608.5 Conclusion 1639 An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques 167M. Sharmila Begum, A. V. M. B. Aruna, A. Balajee and R. Murugan9.1 Introduction 1689.2 Literature Survey 1699.3 Proposed Methodology 1719.4 Experimental Results 1829.5 Conclusion 185Part III: Advancements in Cognitive Computing: Practical Implementations 18910 Fuzzy-Based Efficient Resource Allocation and Schedulingin a Computational Distributed Environment 191Suguna M., Logesh R. and Om Kumar C. U.10.1 Introduction 19210.2 Proposed System 19310.3 Experimental Results 19610.4 Conclusion 20111 A Lightweight CNN Architecture for Prediction of Plant Diseases 203Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A. and Subramaniyaswamy V.11.1 Introduction 20411.2 Precision Agriculture 20611.3 Related Work 21111.4 Proposed Architecture for Prediction of Plant Diseases 21411.5 Experimental Results and Discussion 21711.6 Conclusion 21912 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification 223P. Saravanan, V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy12.1 Introduction 22412.2 Literature Review 22712.3 Proposed Feature Fusioned Dictionary Learning Model 22912.4 Experimental Results and Discussion 23212.5 Conclusion and Future Work 23513 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure 239Akshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari and Shriram K. Vasudevan13.1 Introduction 24013.2 Cognitive Computing in Action 24113.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing 24313.4 Cognitive Solutions Revolutionizing the Healthcare Industry 24613.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study) 24913.6 Conclusion and Future Work 25114 Automating ESG Score Rating with Reinforcement Learning for Responsible Investment 253Mohan Teja G., Logesh Ravi, Malathi Devarajan and Subramaniyaswamy V.14.1 Introduction 25414.2 Comparative Study 25914.3 Literature Survey 26314.4 Methods 26614.5 Experimental Results 27314.6 Discussion 27714.7 Conclusion 27815 Reinforcement Learning in Healthcare: Applications and Challenges 283Tribhangin Dichpally, Yatish Wutla and Sheela Jayachandran15.1 Introduction 28315.2 Structure of Reinforcement Learning 28515.3 Applications 28915.4 Challenges 31015.5 Conclusion 31216 Cognitive Computing in Smart Cities and Healthcare 317Dave Mahadevprasad V., Ondippili Rudhra and Sanjeev Kumar Singh16.1 Introduction 31816.2 Machine Learning Inventions and Its Applications 32216.3 What is Reinforcement Learning and Cognitive Computing? 32616.4 Cognitive Computing 32716.5 Data Expressed by the Healthcare and Smart Cities 33116.6 Use of Computers to Analyze the Data and Predict the Outcome 33216.7 Machine Learning Algorithm 33216.8 How to Perform Machine Learning? 33616.9 Machine Learning Algorithm 33816.10 Common Libraries for Machine Learning Projects 34016.11 Supervised Learning Algorithm 34116.12 Future of the Healthcare 34316.13 Development of Model and Its Workflow 34616.13.1 Types of Evaluation 34716.14 Future of Smart Cities 34716.15 Case Study I 34916.16 Case Study II 35216.17 Case Study III 35516.18 Case Study IV 35816.19 Conclusion 360References 360Index 365