Cognitive Analytics and Reinforcement Learning - Theories, Techniques and Applications

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Table of Contents
Series Page
Title Page
Copyright Page
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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