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Submit a ProposalCognitive Analytics and Reinforcement Learning
 | Theories, Techniques and Applications Edited by Elakkiya, R. and Subramaniyaswamy V Copyright: 2024 | Status: Published ISBN: 9781394214037 | Hardcover | 382 pages Price: $195 USD  |
One Line Description
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.
Audience
This 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.
This 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.
Description
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.
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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.
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Author / Editor Details
Elakkiya R., PhD, is an assistant professor in the Department of Computer Science at the Birla Institute of Technology & Science in Dubai, UAE. She received a Ph.D. in 2018 and did her doctoral research in sign language recognition. Her research focuses on addressing trending issues in computer science, mathematics, and engineering. Along with publishing two books, 50 research articles, and three patents, she is an editor of the Information Engineering and Applied Computing journal. She received the Young Achiever Award in 2019.
Elakkiya R., PhD, is an assistant professor in the Department of Computer Science at the Birla Institute of Technology & Science in Dubai, UAE. She received a Ph.D. in 2018 and did her doctoral research in sign language recognition. Her research focuses on addressing trending issues in computer science, mathematics, and engineering. Along with publishing two books, 50 research articles, and three patents, she is an editor of the Information Engineering and Applied Computing journal. She received the Young Achiever Award in 2019.
Subramaniyaswamy V, PhD, is a professor at the School of Computing at SASTRA Deemed University in Tamilnadu, India. He received a Ph.D. from Anna University in 2013. His research areas include cognitive computing, reinforced learning, recommender systems, artificial intelligence, and the Internet of Things. He has published more than 200 research papers and book chapters in international journals and books.
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Table of Contents
Preface
Part I: Cognitive Analytics in Continual Learning
1. Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research
Renuga Devi T., Muthukumar K., Sujatha M. and Ezhilarasie R.
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
2. Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model
Sasikumar A., Logesh Ravi, Malathi Devarajan, Hossam Kotb and Subramaniyaswamy V.
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
Keerthana S., Elakkiya R. and Santhi B.
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
Thangaramya K., Logeswari G., Sudhakaran G., Aadharsh R., Bhuvaneshwar S., Dheepakraaj R. and Parasu Sunny
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
5. Virtual Makeup Try-On System Using Cognitive Learning
Divija Sanapala and J. Angel Arul Jothi
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
Sini Raj Pulari, T. S. Murugesh, Shriram K. Vasudevan and Akshay Bhuvaneswari Ramakrishnan
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
P. Padmakumari, S. Vidivelli and P. Shanthi
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
S. Vidivelli, P. Padmakumari and P. Shanthi
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
M. Sharmila Begum, A. V. M. B. Aruna, A. Balajee and R. Murugan
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
Suguna M., Logesh R. and Om Kumar C. U.
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
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A. and Subramaniyaswamy V.
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
P. Saravanan, V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy
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
References
13. Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure
Akshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari and Shriram K. Vasudevan
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
14. Automating ESG Score Rating with Reinforcement Learning for Responsible Investment
Mohan Teja G., Logesh Ravi, Malathi Devarajan and Subramaniyaswamy V.
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
Tribhangin Dichpally, Yatish Wutla and Sheela Jayachandran
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
16. Cognitive Computing in Smart Cities and Healthcare
Dave Mahadevprasad V., Ondippili Rudhra and Sanjeev Kumar Singh
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
Back to Top
Preface
Part I: Cognitive Analytics in Continual Learning
1. Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research
Renuga Devi T., Muthukumar K., Sujatha M. and Ezhilarasie R.
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
2. Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model
Sasikumar A., Logesh Ravi, Malathi Devarajan, Hossam Kotb and Subramaniyaswamy V.
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
Keerthana S., Elakkiya R. and Santhi B.
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
Thangaramya K., Logeswari G., Sudhakaran G., Aadharsh R., Bhuvaneshwar S., Dheepakraaj R. and Parasu Sunny
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
5. Virtual Makeup Try-On System Using Cognitive Learning
Divija Sanapala and J. Angel Arul Jothi
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
Sini Raj Pulari, T. S. Murugesh, Shriram K. Vasudevan and Akshay Bhuvaneswari Ramakrishnan
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
P. Padmakumari, S. Vidivelli and P. Shanthi
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
S. Vidivelli, P. Padmakumari and P. Shanthi
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
M. Sharmila Begum, A. V. M. B. Aruna, A. Balajee and R. Murugan
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
Suguna M., Logesh R. and Om Kumar C. U.
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
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A. and Subramaniyaswamy V.
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
P. Saravanan, V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy
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
References
13. Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure
Akshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari and Shriram K. Vasudevan
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
14. Automating ESG Score Rating with Reinforcement Learning for Responsible Investment
Mohan Teja G., Logesh Ravi, Malathi Devarajan and Subramaniyaswamy V.
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
Tribhangin Dichpally, Yatish Wutla and Sheela Jayachandran
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
16. Cognitive Computing in Smart Cities and Healthcare
Dave Mahadevprasad V., Ondippili Rudhra and Sanjeev Kumar Singh
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
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