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...
Other Authors: | , |
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Format: | eBook |
Language: | English |
Published: |
Hoboken, NJ
John Wiley & Sons, Inc.
2024
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Subjects: | |
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