Artificial Intelligence based Solutions for Industrial Applications (Paperback, 1)

1. Understanding the Fundamentals of Deep Learning for Industrial Applications.?2.?Application of Machine Learning/Deep Learning in Healthcare. 3.?Implementing Machine Learning Technology for Smart Agriculture based on Data Collection Systems and Predictive Analytics Use Cases. 4.?Empowering Learners through Generative AI: Human-Centric Design and Machine Learning in Transforming Education. 5.?Machine Learning / Deep Learning Use Cases in Education. 6.?Digital Dark Arts: Combating Trolls with AI Based Solutions. 7.?Artificial Intelligence for Smart Manufacturing Using Digital Twin Technology. 8.?Machine Learning and Deep Learning in Cyber Security. 9.?Artificial Intelligence and Media Industry: Exploring New Avenues and Future. 10.?Machine Learning Feature Selection Method for Anatomic Brain MRI in Dementia: A Comparative Study. 11.?Study of bio-nanofluid flow via vertical surface: A machine learning approach. 12.?Detection of Bearing Fault Using Vibration Signal Through Multiple Feature Extraction Based Machine Learning Algorithms. 13.?Artificial Neural Network-Based Stock Price Prediction using Levenberg-Marquardt Algorithm.?14.?Technological transformation in learning patterns in Media and Entertainment.?15.?Intelligent Heart Disease Prediction using Machine Learning.

Reinforcement Learning in Action : From Foundations to Frontier AI (Hardcover)

List of Figures. List of Tables. Foreword. Preface. Author Bios. Contributors. Notation. Chapter 1: Introduction to Reinforcement Learning. Section I: Basics of Reinforcement Learning. Chapter 2: Fundamentals of Reinforcement Learning. Section II: Classical Reinforcement Learning. Chapter 3: Classical Reinforcement Learning Algorithms. Section III: Deep Reinforcement Learning. Chapter 4: Scaling Reinforcement Learning: Function Approximation and Deep Methods. Section IV: LLMs and Reinforcement Learning. Chapter 5: Preference-Based Alignment: Reward Modeling and Reinforcement Learning for LLMs. Chapter 6: Reinforcement Learning for Reasoning Models. Section V: Agentic and Reinforcement Learning. Chapter 7: Reinforcement Learning Enabled Agentic AI. Appendix A: Mathematical Proofs and Derivations. Bibliography. Index.

Reinforcement Learning in Action : From Foundations to Frontier AI (Paperback)

List of Figures. List of Tables. Foreword. Preface. Author Bios. Contributors. Notation. Chapter 1: Introduction to Reinforcement Learning. Section I: Basics of Reinforcement Learning. Chapter 2: Fundamentals of Reinforcement Learning. Section II: Classical Reinforcement Learning. Chapter 3: Classical Reinforcement Learning Algorithms. Section III: Deep Reinforcement Learning. Chapter 4: Scaling Reinforcement Learning: Function Approximation and Deep Methods. Section IV: LLMs and Reinforcement Learning. Chapter 5: Preference-Based Alignment: Reward Modeling and Reinforcement Learning for LLMs. Chapter 6: Reinforcement Learning for Reasoning Models. Section V: Agentic and Reinforcement Learning. Chapter 7: Reinforcement Learning Enabled Agentic AI. Appendix A: Mathematical Proofs and Derivations. Bibliography. Index.

Superhuman : Beyond the Limits of Our Intelligence (Hardcover)

Prologue I. Artificial Narrow Intelligence ? ANI 1. The day after 2. The first overtaking 3. Beyond the experts 4. The next challenge II. Artificial General Intelligence ? AGI 5. How to measure a mind 6. Measuring machine intelligence 7. The tests never end 8. AGI on the horizon 9. Two ways of thinking, two paths to follow? 10. The Scaling Hypothesis 11. Reasoning one step at a time 12. Reluctance and vertigo III. Artificial Super Intelligence ? ASI 13. The final challenge: super Artificial Intelligence? 14. Beating ourselves at our own game 15. Beyond the limits of our intelligence 16. The final test for humanity Epilogue Informal glossary Bibliography

Superhuman : Beyond the Limits of Our Intelligence (Paperback)

Prologue I. Artificial Narrow Intelligence ? ANI 1. The day after 2. The first overtaking 3. Beyond the experts 4. The next challenge II. Artificial General Intelligence ? AGI 5. How to measure a mind 6. Measuring machine intelligence 7. The tests never end 8. AGI on the horizon 9. Two ways of thinking, two paths to follow? 10. The Scaling Hypothesis 11. Reasoning one step at a time 12. Reluctance and vertigo III. Artificial Super Intelligence ? ASI 13. The final challenge: super Artificial Intelligence? 14. Beating ourselves at our own game 15. Beyond the limits of our intelligence 16. The final test for humanity Epilogue Informal glossary Bibliography

Modern Computing : Principles and Paradigms (Hardcover)

Part 1. Modern Computing: Architecture, Systems and Paradigms ?1. Modern Computing at a Glance ?2. AI for Cloud Resource Management for Net Zero Carbon Emissions? 3. Proactive Resource Management for Scalable Edge AI Environments? 4. QoS Based Transformer Architecture for Carbon Neutral Cloud Computing ?5. Sustainable Serverless Computing for Industrial Internet of Things? Part 2. Modern Applications, Services and Methods? 6. Privacy Preserving and Secure Federated Transfer Learning for Industry 5.0? 7. Geospatial Artificial Intelligence for Smart Agriculture and Modern Farming? 8. AI Based Modern Cloud Computing for Smart Kitchen ??9. Artificial Intelligence of Things for Modern Healthcare using Edge Cloud Continuum? 10. Vertical Federated Learning with Data-Space Heterogeneity? Part 3. Modern Technologies and Emerging Trends? 11. Transformers Based Performance Analysis in Modern Serverless Infrastructures? 12. BERT Based Hate Speech Detection Framework Using LLMs? 13. Formula 1 Model Context Protocol Platform for LLMs Using Cloud Computing? 14. AI-Driven Smart Home Energy Optimization Using Digital Twins? 15. Reshaping Modern Computing with AI

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Modern Computing : Principles and Paradigms (Paperback)

Part 1. Modern Computing: Architecture, Systems and Paradigms ?1. Modern Computing at a Glance ?2. AI for Cloud Resource Management for Net Zero Carbon Emissions? 3. Proactive Resource Management for Scalable Edge AI Environments? 4. QoS Based Transformer Architecture for Carbon Neutral Cloud Computing ?5. Sustainable Serverless Computing for Industrial Internet of Things? Part 2. Modern Applications, Services and Methods? 6. Privacy Preserving and Secure Federated Transfer Learning for Industry 5.0? 7. Geospatial Artificial Intelligence for Smart Agriculture and Modern Farming? 8. AI Based Modern Cloud Computing for Smart Kitchen ??9. Artificial Intelligence of Things for Modern Healthcare using Edge Cloud Continuum? 10. Vertical Federated Learning with Data-Space Heterogeneity? Part 3. Modern Technologies and Emerging Trends? 11. Transformers Based Performance Analysis in Modern Serverless Infrastructures? 12. BERT Based Hate Speech Detection Framework Using LLMs? 13. Formula 1 Model Context Protocol Platform for LLMs Using Cloud Computing? 14. AI-Driven Smart Home Energy Optimization Using Digital Twins? 15. Reshaping Modern Computing with AI

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AI for HCI (Paperback)

Introduction. AI in the User Interface. User Interfaces for AI-Rich Systems. Synergy. What Happens When It All Goes Wrong?. AI Behind the Scenes in UX/IxD. HCI for Backend AI Developers. Human-like Computing. Wider Context.

AI for HCI (Hardcover)

Introduction. AI in the User Interface. User Interfaces for AI-Rich Systems. Synergy. What Happens When It All Goes Wrong?. AI Behind the Scenes in UX/IxD. HCI for Backend AI Developers. Human-like Computing. Wider Context.

Large Language Models in Healthcare : Opportunities, Applications, and Challenges (Hardcover)

Chapter 1: Multimodal Foundation Models for Radiological Understanding and Report Generation.

Sama E. Rashed, Ghada Khoriba, and Essam A. Rashed

Chapter 2: Automating Healthcare Decision-Making: The Role of Large Language Models in Disease Diagnosis.

Muhammad Uzair Khan, Muhammad Zia, Fazal Muhammad, Dawar Awan, Muhammad Sohail Khan, Saadia Tabassum, Shahid Khan, Qammer H. Abbasi, and Muhammad Lais

Chapter 3: Report Generation of Medical Procedures using Multimodal Large Language Models.

Hussam Ali, Rabia Parveen, and Muhammad Sadaqat Janjua

Chapter 4: Transparency and Ethical Frameworks for LLMs in Healthcare.

Ruhul Amin Khalil and Muhammad Asif Khan

Chapter 5: Detecting Hallucinations and Biases in LLMs: A Study of Trust Score Calibration and Ethics in Healthcare.

Ayesha Butt and Dr Muneeb Ul Hassan

Chapter 6: LLMS in Healthcare: Exploring Digital Inequality, Language Bias, and Epistemic Injustice in india.

Dr. Laxmi Mishra and Dr. Govind Kumar

Chapter 7: Integration of Large Language Models into Nursing Care Planning.

Troy Smith, Naila Muhammad, and Margaret St George

Chapter 8: Insights into Language Models and AI on Edge Devices for Healthcare Applications.

Shravan Somashekara Rai

Chapter 9: Chest Insight: A Multimodal AI Framework for Lung Disease Diagnosis and Clinical Explanation Generation.

Dr Thara D K and Varsha N

Bibliography.

Index

Mathematical Foundations of Deep Learning : Theory and Algorithms (Paperback)

1. Deep Neural Networks. 2 Network Training. 3 Deep Optimal Control. 4 Deep Reinforcement Learning.?5 Generative Models.

Intelligent Estimation : A Soft Computing Paradigm (Hardcover)

1. Introduction: Intelligent Estimation.? 2. Concepts and Theory of Artificial Neural Networks.? 3. System Identification.? 4. Filtering and Parameter Estimation.? 5. Fuzzy Logic Based Approaches.? 6. System Identification and Estimation with Fuzzy Logic/System.? 7. Concept and Theory of Genetic Algorithms (GAs).? 8. Estimation with Genetic Algorithms (GAs).? 9. Hybrid Methods-NN-FL-GA Triad Paradigm.? Appendix A. Key Theorems in System Identification, Filtering, and State & Parameter Estimation.? Appendix B. Theorem in Soft Computing.? Appendix C. Procedure of Building an AI System.??

Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents (Paperback)

Part 1. Models and applications of distributed machine learning
1. Federated learning solutions and applications
2. Distributed multi-agent learning
3. Models for machine learning on the edge of the network
4. Sharing knowledge about machine learning models in a community of agents

Part 2. Infrastructures for distributed AI
5. Integration of machine learning models in agent-based applications
6. Frameworks for organizing heterogeneous entities and resources in complex scenarios
7. Adaptive interaction across heterogeneous electronic environments

Part 3. Agents in the real-world
8. Interaction in swarms of physically deployed robots
9. Interaction in social humanoid robots
10. Socio-technical networks and hyperagents
11. Web of things and autonomous agents

Convolutional Neural Networks in Healthcare (Hardcover)