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Cognitive analytics and reinforcement learning theories, techniques and applications

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...

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Bibliographic Details
Other Authors: Elakkiya, R. (Editor), Subramaniyaswamy, V. (Editor)
Format: eBook
Language:English
Published: Hoboken, NJ John Wiley & Sons, Inc. 2024
Subjects:
Big Data / Http://id.loc.gov/authorities/subjects/sh2012003227
Informatique Douce
Soft Computing / Http://id.loc.gov/authorities/subjects/sh96004789
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 15 Reinforcement Learning in Healthcare: Applications and Challenges
  • 15.1 Introduction
  • 15.2 Structure of Reinforcement Learning
  • 15.3 Applications
  • 15.3.1 Treatment of Sepsis with Deep Reinforcement
  • 15.3.2 Chemotherapy and Clinical Trial Dosing Regimen Selection
  • 15.3.3 Dynamic Treatment Recommendation
  • 15.3.4 Dynamic Therapy Regimes Using Data from the Medical Registry
  • 15.3.5 Encouraging Physical Activity in Diabetes Patients
  • 15.3.6 Diagnosis Utilizing Medical Images
  • 15.3.7 Clinical Research for Non-Small Cell Lung Cancer
  • 15.3.8 Segmentation of Transrectal Ultrasound Images
  • 15.3.9 Personalized Control of Glycemia in Septic Patients
  • 15.3.10 An AI Structure for Simulating Clinical Decision-Making
  • 15.4 Challenges
  • 15.5 Conclusion
  • References
  • Chapter 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
  • 16.13.1 Types of Evaluation
  • 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
  • EULA.
  • 12.1.3 Sparsity Issues in Brain Image Analysis
  • 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
  • Chapter 13 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure
  • 13.1 Introduction
  • 13.2 Cognitive Computing in Action
  • 13.2.1 Natural Language Processing (NLP)
  • 13.2.2 Application of Cognitive Computing in Everyday Life
  • 13.2.3 The Importance of Cognitive Computing in the Development of Smart Cities
  • 13.2.4 The Importance of Cognitive Computing in the Healthcare Industry
  • 13.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing
  • 13.3.1 Cognitive Data Analytics for Smarter Cities
  • 13.3.2 Predictive Maintenance and Proactive Services
  • 13.3.3 Personalized Urban Services
  • 13.3.4 Cognitive Computing and the Role It Plays in Obtaining Energy Optimization
  • 13.3.5 Data-Driven Decisions for City Development and Governance
  • 13.4 Cognitive Solutions Revolutionizing the Healthcare Industry
  • 13.4.1 Artificial Intelligence-Driven Diagnostics and the Detection of Disease
  • 13.4.2 Individualized and Tailored Treatment Programs
  • 13.4.3 Real-Time Monitoring of Patients and Predictive Analytical Tools
  • 13.4.3.1 Cognitively Assisted Robotic Surgery
  • 13.4.4 Patient Empowerment with Health AI
  • 13.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study)
  • 13.6 Conclusion and Future Work
  • References
  • Chapter 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
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Part I: Cognitive Analytics in Continual Learning
  • Chapter 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
  • 1.8 Cognitive Systems vs. AI
  • 1.9 Use Cases
  • 1.10 Conclusion
  • References
  • Chapter 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
  • Chapter 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
  • Chapter 4 Predicting Optimal Moves in Chess Board Using Artificial Intelligence
  • 4.1 Introduction
  • 4.2 Literature Survey
  • 4.3 Proposed System
  • 4.3.1 Human vs. Human
  • 4.3.2 Human vs. Alpha-Beta Pruning
  • 4.3.3 Human vs. Hybrid Algorithm
  • 4.4 Results and Discussion
  • 4.4.1 ELO Rating
  • 4.4.2 Comparative Analysis
  • 4.5 Conclusion
  • References
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 12 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification
  • 12.1 Introduction
  • 12.1.1 Importance of Accurate and Early Diagnosis and Treatment
  • 12.1.2 Role of Machine Learning in Brain Tumor Classification

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