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Full Description
Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing.
This book:
Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond.
Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data.
Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks.
Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security.
Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence.
It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
Contents
I. AI Trends and Challenges. 1. AI based Computing Applications in Future Communication. 2. Advances of Deep Learning and related Applications. 3. Machine Learning for Big Data and Neural Networks. II. Machine Intelligence in Network Technologies. 4. Deformation Prediction and Monitoring using Real-Time WSN and Machine Learning Algorithms: A Review. 5. Unmanned Aerial Vehicle: Integration in Healthcare Sector for Transforming Interplay among Smart Cities. 6. Blockchain Technologies Using Machine Learning. 7. Q-learning and Deep Q Networks for Securing IoT Networks, Challenges and Solution. 8. The Application of Artificial Intelligence and Machine Learning in Network Security using a Bibliometric Study. 9. Machine Learning Approaches for Intrusion Detection: Enhancing Cybersecurity and Threat Mitigation. III. Cognitive Machine Intelligence Applications. 10. The Rise of AI in the Field of Healthcare. 11. A Comprehensive Survey of Machine Learning Applications in Healthcare. 12. A Deep Learning Approach for the Early Diagnosis of Melanoma Cancer: Study and Analysis. 13. A Study and Analysis on Nowcasting: Forms of Precipitation using Improvised Random Forest Classifier. 14. A Study and Comparative Analysis on Prediction of Tsunami Using Convolutional Neural Network. 15. Towards Smarter Chatbots: Unravelling the Capabilities of ChatGPT.
