Cognitive analytics and reinforcement learning : theories, techniques and applications / edited by Elakkiya R. and Subramaniyaswamy V.

Format:

Book

Author/Creator:

R., Elakkiya.

Contributor:

Elakkiya, R., editor.

Subramaniyaswamy, V., editor.

Language:

English

Subjects (All):

Soft computing.

Big data.

Physical Description:

1 online resource

Edition:

1st ed.

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc. ; Beverly, MA : Scrivener Publishing LLC, 2024.

Summary:

COGNITIVE ANALYTICS AND REINFORCEMENT LEARNING The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research. Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications. Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems. The book's real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. 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 help you navigate the complex and exciting world of cognitive analytics and reinforcement learning. Audience The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.

Contents:

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

Index

EULA.

Notes:

OCLC-licensed vendor bibliographic record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781394214068

1394214065

9781394214051

1394214057

OCLC:

1428953557