This book combines cutting-edge advancements in neuroscience-inspired artificial intelligence, edge computing, and sustainable practices, presenting a compelling narrative for researchers, industry professionals, and policymakers alike. It discusses the principles of neuroscience that inspire artificial intelligence development, including neural dynamics, spiking neural networks, and the role of biological intelligence in creating adaptive systems.
Features:
- Illustrates how artificial intelligence is revolutionizing areas such as climate modelling, green energy solutions, resource management, and precision farming.
- Addresses critical issues such as replicating human cognition responsibly, addressing bias in artificial intelligence systems, protecting user privacy, and minimizing the environmental impact of artificial intelligence.
- Discusses energy-efficient artificial intelligence, ethical considerations, and advanced concepts like neuromorphic computing and federated learning.
- Explores the convergence of neuroscience and edge computing, demonstrating real-world applications in healthcare, agriculture, robotics, and environmental sustainability.
- Explains principles of neuroscience that inspire artificial intelligence development, including neural dynamics, spiking neural networks, and the role of biological intelligence in creating adaptive systems.
It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.
Table of Contents:
Chapter 1. Introduction to Neuromorphic Computing. Chapter 2. Computational Modeling of Genetic Algorithms: DNA Encoding, Mutation, and Evolutionary Optimization. Chapter 3. Smart Diagnostic System for Cardiovascular, Neural, and Muscular Health Using AI-Based ECG, EEG, and EMG Processing. Chapter 4. Application of AI in Healthcare. Chapter 5. Drone-Enabled IoT-Integrated Smart Healthcare System for Reducing Ambulance Delays and Mortality in Emergency Conditions. Chapter 6. Detecting cancer stages in patients using machine learning integrated with Classification Threshold Tuner. Chapter 7. Lung Cancer Stage Detection System Using Machine Learning. Chapter 8. Harnessing Neuroplasticity: Advances in Brain Recovery and Cognitive Rehabilitation. Chapter 9. AI Integrated Diagnostic Models for Neurological Disorders: Current Trends, Technologies, and Future Directions. Chapter 10. Deep learning-based approaches for enhancing battery prognostics and health management. Chapter 11. A Review on Applications of Artificial Intelligence in Hydrogen Energy. Chapter 12. An In-Depth Review of Electric Vehicles within the Framework of Super Smart Grids. Chapter 13. AI-Driven Energy Optimization in Edge Data Centres: The 4-Gear Predictive Threshold (4-GPT) Framework. Chapter 14. Transforming Agriculture and Environmental Monitoring with Artificial Intelligence: Advancing Sustainability and Precision. Chapter 15. A Comprehensive Review on AI Technologies for Detecting defects from Fruits and Vegetables. Chapter 16. Generative AI for Sustainable Solutions. Chapter 17. Next-Gen AI in Education: Teacher Engagement for Sustainable Learning Performance. Chapter 18. AI-Driven Cybersecurity Awareness and Readiness: A case study from Albania Reis. Chapter 19. AI-Powered Cyber Warfare: Safeguarding Data Privacy and Economic Stability in a Hyperconnected World. Chapter 20. Responsible AI in Public Policy. Chapter 21. Responsible AI and the Next Generation: Policy Challenges and Governance Opportunities. Chapter 22. Challenges and Future Trends in Next-Generation AI: Integrating Neuroscience, Edge Computing, and Sustainability
About the Author :
Manzoor Ansari is an Assistant Professor in the Department of Computer Science at the Institute of Information Technology & Management (IITM), GGSIP University, New Delhi. His research spans IoT-Cloud integration, machine learning, smart surveillance systems, cyber forensics, business intelligence, environmental monitoring, and AI-driven predictive modelling.
Syed Arshad Ali is an Assistant Professor in the Department of Computer Science & Applications at Sharda University, Greater Noida. His areas of expertise include Cloud Computing, Data Structures, Algorithms, Programming, Operating Systems, and Big Data technologies. Dr. Ali's research contributions cover cloud resource management, energy-efficient computing, IoT-cloud integration, big data analytics, federated learning, and NoSQL data solutions.
Mansaf Alam is working as a Professor in the Department of Computer Science, Faculty of Sciences, Jamia Millia Islamia, New Delhi. His area of research includes Artificial Intelligence, Big Data Analytics, Machine Learning and Deep Learning, Cloud Computing, and Data Mining.
