Cognitive Analytics and Reinforcement Learning - Theories, Techniques and Applications

Table of Contents
Series Page
Title Page
Copyright Page
Preface
Part I: COGNITIVE ANALYTICS IN CONTINUAL LEARNING
	1 Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research
		1.1 Introduction
		1.2 Evolution of Data Analytics
		1.3 Conceptual View of Cognitive Systems
		1.4 Elements of Cognitive Systems
		1.5 Features, Scope, and Characteristics of Cognitive System
		1.6 Cognitive System Design Principles
		1.7 Backbone of Cognitive System Learning/Building Process [10]
		1.8 Cognitive Systems vs. AI
		1.9 Use Cases
		1.10 Conclusion
		References
	2 Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model
		2.1 Introduction
		2.2 Smart City Applications
		2.3 Related Work
		2.4 Proposed Cognitive Computing RL Model
		2.5 Simulation Results
		2.6 Conclusion
		References
	3 Deep Recommender System for Optimizing Debt Collection Using Reinforcement Learning
		3.1 Introduction
		3.2 Terminologies in RL
		3.3 Different Forms of RL
		3.4 Related Works
		3.5 Proposed Methodology
		3.6 Result Analysis
		3.7 Conclusion
		References
Part II: COMPUTATIONAL INTELLIGENCE OF REINFORCEMENT LEARNING
	4 Predicting Optimal Moves in Chess Board Using Artificial Intelligence
		4.1 Introduction
		4.2 Literature Survey
		4.3 Proposed System
		4.4 Results and Discussion
		4.5 Conclusion
		References
	5 Virtual Makeup Try-On System Using Cognitive Learning
		5.1 Introduction
		5.2 Related Works
		5.3 Proposed Method
		5.4 Experimental Results and Analysis
		5.5 Conclusion
		References
	6 Reinforcement Learning for Demand Forecasting and Customized Services
		6.1 Introduction
		6.2 RL Fundamentals
		6.3 Demand Forecasting and Customized Services
		6.4 eMart: Forecasting of a Real-World Scenario
		6.5 Conclusion and Future Works
		References
	7 COVID-19 Detection through CT Scan Image Analysis: A Transfer Learning Approach with Ensemble Technique
		7.1 Introduction
		7.2 Literature Survey
		7.3 Methodology
		7.4 Results and Discussion
		7.5 Conclusion
		References
	8 Paddy Leaf Classification Using Computational Intelligence
		8.1 Introduction
		8.2 Literature Review
		8.3 Methodology
		8.4 Results and Discussion
		8.5 Conclusion
		References
	9 An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques
		9.1 Introduction
		9.2 Literature Survey
		9.3 Proposed Methodology
		9.4 Experimental Results
		9.5 Conclusion
		References
Part III: ADVANCEMENTS IN COGNITIVE COMPUTING: PRACTICAL IMPLEMENTATIONS
	10 Fuzzy-Based Efficient Resource Allocation and Scheduling in a Computational Distributed Environment
		10.1 Introduction
		10.2 Proposed System
		10.3 Experimental Results
		10.4 Conclusion
		References
	11 A Lightweight CNN Architecture for Prediction of Plant Diseases
		11.1 Introduction
		11.2 Precision Agriculture
		11.3 Related Work
		11.4 Proposed Architecture for Prediction of Plant Diseases
		11.5 Experimental Results and Discussion
		11.6 Conclusion
		References
	12 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification
		12.1 Introduction
		12.2 Literature Review
		12.3 Proposed Feature Fusioned Dictionary Learning Model
		12.4 Experimental Results and Discussion
		12.5 Conclusion and Future Work
		References
	13 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure
		13.1 Introduction
		13.2 Cognitive Computing in Action
		13.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing
		13.4 Cognitive Solutions Revolutionizing the Healthcare Industry
		13.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study)
		13.6 Conclusion and Future Work
		References
	14 Automating ESG Score Rating with Reinforcement Learning for Responsible Investment
		14.1 Introduction
		14.2 Comparative Study
		14.3 Literature Survey
		14.4 Methods
		14.5 Experimental Results
		14.6 Discussion
		14.7 Conclusion
		References
	15 Reinforcement Learning in Healthcare: Applications and Challenges
		15.1 Introduction
		15.2 Structure of Reinforcement Learning
		15.3 Applications
		15.4 Challenges
		15.5 Conclusion
		References
	16 Cognitive Computing in Smart Cities and Healthcare
		16.1 Introduction
		16.2 Machine Learning Inventions and Its Applications
		16.3 What is Reinforcement Learning and Cognitive Computing?
		16.4 Cognitive Computing
		16.5 Data Expressed by the Healthcare and Smart Cities
		16.6 Use of Computers to Analyze the Data and Predict the Outcome
		16.7 Machine Learning Algorithm
		16.8 How to Perform Machine Learning?
		16.9 Machine Learning Algorithm
		16.10 Common Libraries for Machine Learning Projects
		16.11 Supervised Learning Algorithm
		16.12 Future of the Healthcare
		16.13 Development of Model and Its Workflow
		16.14 Future of Smart Cities
		16.15 Case Study I
		16.16 Case Study II
		16.17 Case Study III
		16.18 Case Study IV
		16.19 Conclusion
		References
Index
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