Название: Artificial Intelligence for Intelligent Systems: Fundamentals, Challenges, and ApplicationsАвтор: Ullah Khan, Mariya Ouaissa, Mariyam Ouaissa, Muhammad Fayaz
Издательство: CRC Press
Серия: Intelligent Data-Driven Systems and Artificial Intelligence
Год: 2025
Страниц: 375
Язык: английский
Формат: pdf (true)
Размер: 26.3 MB
The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities' concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic.
Recent technological infrastructure developments have diverse applications. Smart efforts from academia and industry have already been put to research on artificial intelligence (AI) for intelligent computation. Although, AI is designed to optimize 5G, 6G, the internet of things (IoT), cloud computing, edge, ad hoc networks, and intrusion detection systems, the AI-based intelligent system, network security, and data analytics need to be used for better communication. As the overall topological structure has advanced with the passage of time, there exist several issues that need to be addressed. AI is deployed in many real-time applications, including agriculture, transportation, health care, and industry. Nowadays, machine learning (ML) models have the capabilities of intelligent predication. Cognitive computing techniques schedule resources that provide better communication channels, and novel algorithms have been made possible using the ML approach to give low-cost solutions.
This book is divided into three parts. Part I introduces the recent trends and challenges of AI. Part II goes in depth to highlight the security of intelligent systems using AI, and part III explores the role of big data analytics in current applications.
Chapter 1 examines the interdisciplinary uses of AI and how it is revolutionizing several sectors, by introducing the concept of AI and the significance of a multidisciplinary approach in harnessing its potential. Chapter 2 introduces deep neural networks and their structures, methodologies, properties, and limits. Also discussed are the significant distinctions between deep neural networks and standard ML, as well as the big obstacles ahead. Chapter 3 focuses on providing a comprehensive comparative analysis of AI, deep learning (DL), and ML. It briefly overviews the state-of-the-art developments in these fields, highlighting key trends, challenges, and applications. Chapter 4 sheds light on the challenges and possibilities offered by these technologies by addressing the fundamental principles of big data mining and distributed processing. Also discussed are the distributed processing frameworks’ roles in the administration of large datasets and the extraction of valuable intelligence. In Chapter 5, we embark on an exploration of quantum AI—an innovative domain, where quantum computing and AI converge to reshape our technological landscape.
This book:
Provides a better understanding of artificial intelligence-based applications for future smart cities
Presents a detailed understanding of artificial intelligence tools for intelligent technologies
Showcases intelligent computing technologies in obtaining optimal solutions using artificial intelligence
Discusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs)
Covers machine learning-based Intrusion detection system (IDS) for smart grid
It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
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