Inam Ullah Khan – författare

This book sheds light on the cyber security challenges associated with nextgeneration computing technologies, emphasizing the serious threats posed to individuals, businesses, and nations. With everything becoming increasingly interconnected via the Internet, data security becomes paramount. As technology advances, people need to secure their data communication processes. Personal data security, including data integrity and confidentiality, is particularly vulnerable. Therefore, the concept of cyber security forensics emerges to ensure data security for everyone, addressing issues such as data control, hijacking, and threats to personal devices such as mobile phones, laptops, and other smart technologies. This book covers key topics related to cyber security in next-generation computing technologies, ultimately enhancing the quality of life for citizens, facilitating interaction with smart governments, and promoting secure communication processes.KEY FEATURESHighlights innovative principles and practices using next generation computing technologies based cybersecurityPresents an introduction to recent trends regarding the convergence of AI/ML in cybersecurityOffers an overview of theoretical, practical, simulation concepts of cybersecurity

This book sheds light on the cyber security challenges associated with nextgeneration computing technologies, emphasizing the serious threats posed to individuals, businesses, and nations. With everything becoming increasingly interconnected via the Internet, data security becomes paramount. As technology advances, people need to secure their data communication processes. Personal data security, including data integrity and confidentiality, is particularly vulnerable. Therefore, the concept of cyber security forensics emerges to ensure data security for everyone, addressing issues such as data control, hijacking, and threats to personal devices such as mobile phones, laptops, and other smart technologies. This book covers key topics related to cyber security in next-generation computing technologies, ultimately enhancing the quality of life for citizens, facilitating interaction with smart governments, and promoting secure communication processes.KEY FEATURESHighlights innovative principles and practices using next generation computing technologies based cybersecurityPresents an introduction to recent trends regarding the convergence of AI/ML in cybersecurityOffers an overview of theoretical, practical, simulation concepts of cybersecurity

This book presents a comprehensive exploration of LPWANs, delving into their fundamental concepts, underlying technologies, and the multifaceted challenges they tackle. This book recognizes that LPWANs don't operate in isolation; they are intimately intertwined with Artificial Intelligence and Machine Learning (AI/ML) technologies, which play a pivotal role in optimizing LPWAN performance and capabilities. The book is a collection of original contributions regarding air interface, transmission technologies and novel network architectures, such as network slicing, cloud/fog/edge computing, ad hoc networks and software-defined network. Also, this book provides a guide for researchers of IoT applications to choose suitable LPWAN technologies and describe the design aspects, network architectures, security issues and challenges.Features:Explains machine learning algorithms onto low-power wide area network sensors for compressed communications. Illustrates wireless-based Internet of Things networks using low-power wide area networks technology for quality air. Presents cognitive Internet of Things networks using wireless communication, and low-power wide area network technologies for Ad Hoc networks. Discusses a comprehensive study of low-power wide area networks for flying Ad Hoc networks. Showcases the study of energy efficient techniques aided by low-power wide area network technologies for the Internet of Things networks. The text is aimed at senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

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.This book:Provides a better understanding of artificial intelligence-based applications for future smart citiesPresents a detailed understanding of artificial intelligence tools for intelligent technologiesShowcases intelligent computing technologies in obtaining optimal solutions using artificial intelligenceDiscusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs)Covers machine learning-based Intrusion detection system (IDS) for smart gridIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This book focuses on the techniques of artificial intelligence that are mainly used in the power electronics field for the optimization of lost vehicle power. With the intention of optimizing the powerful energy of the vehicles and producing reliable energy, the most efficient methods, algorithms, and strategies of ethical artificial intelligence (AI) are being applied. By employing machine learning methods, the optimization of power energy in vehicles can be quickly recovered and managed efficiently. In today’s bustling world, power energy is indispensable for progress, yet in congested Vehicular Ad-hoc Networks (VANETs), vehicles often face power depletion and decreased efficiency. This book explores these challenges, encompassing not only power but also other critical power electronics within vehicles. We aim to introduce innovative approaches, leveraging ethical AI methods, to optimize energy performance in the face of these difficulties. Through this exploration, we seek to provide practical insights into navigating congested VANET environments while upholding ethical principles in technological advancements. Our book will discuss the current power energy concerns faced by vehicles and also contribute a novel strategy to overcome those concerns. The employment of ethical AI in vehicular power energy will undoubtedly improve the effectiveness and production of vehicles.

Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of artificial intelligence (AI), Internet of Things (IoT) and data science for future communications systems.The goal of AI, IoT and data science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on data science, AI and IoT. Recent breakthroughs in IoT, mobile and fixed communications and computation have paved the way for a data‑centric society of the future. New applications are increasingly reliant on machine‑to‑machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self‑optimization for the task at hand while ensuring high dependability and ultra‑low latency.Networking devices, sensors, agents, meters and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management and operation. Predictive network analytics will play a critical role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g., preventing network failures and anticipating capacity requirements) and overall network decision‑making. To increase user experience and service quality, data mining and analytic techniques for inferring quality of experience (QoE) signals are required.AI, IoT, machine learning, reinforcement learning and network data analytics innovations open new possibilities in areas such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi‑agent systems and network ultra‑broadband deployment prioritization. These new analytic platforms will aid in the transformation of our networks and user experience. Future networks will enable unparalleled automation and optimization by intelligently gathering, analyzing, learning and controlling huge volumes of information.

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.

Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of artificial intelligence (AI), Internet of Things (IoT) and data science for future communications systems.The goal of AI, IoT and data science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on data science, AI and IoT. Recent breakthroughs in IoT, mobile and fixed communications and computation have paved the way for a data‑centric society of the future. New applications are increasingly reliant on machine‑to‑machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self‑optimization for the task at hand while ensuring high dependability and ultra‑low latency.Networking devices, sensors, agents, meters and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management and operation. Predictive network analytics will play a critical role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g., preventing network failures and anticipating capacity requirements) and overall network decision‑making. To increase user experience and service quality, data mining and analytic techniques for inferring quality of experience (QoE) signals are required.AI, IoT, machine learning, reinforcement learning and network data analytics innovations open new possibilities in areas such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi‑agent systems and network ultra‑broadband deployment prioritization. These new analytic platforms will aid in the transformation of our networks and user experience. Future networks will enable unparalleled automation and optimization by intelligently gathering, analyzing, learning and controlling huge volumes of information.

This book focuses on the techniques of artificial intelligence that are mainly used in the power electronics field for the optimization of lost vehicle power. With the intention of optimizing the powerful energy of the vehicles and producing reliable energy, the most efficient methods, algorithms, and strategies of ethical artificial intelligence (AI) are being applied. By employing machine learning methods, the optimization of power energy in vehicles can be quickly recovered and managed efficiently. In today’s bustling world, power energy is indispensable for progress, yet in congested Vehicular Ad-hoc Networks (VANETs), vehicles often face power depletion and decreased efficiency. This book explores these challenges, encompassing not only power but also other critical power electronics within vehicles. We aim to introduce innovative approaches, leveraging ethical AI methods, to optimize energy performance in the face of these difficulties. Through this exploration, we seek to provide practical insights into navigating congested VANET environments while upholding ethical principles in technological advancements. Our book will discuss the current power energy concerns faced by vehicles and also contribute a novel strategy to overcome those concerns. The employment of ethical AI in vehicular power energy will undoubtedly improve the effectiveness and production of vehicles.

Smart Applications of Artificial Intelligence and Big Data covers a wide range of topics related to AI and big data, including machine learning, deep learning, natural language processing, computer vision, data analytics, and data mining. It focuses on the integration of these technologies to create smart applications, such as intelligent transportation systems, smart healthcare, smart cities, and smart grids.This book comprises 21 chapters, each providing technical details pertaining to research, practical examples, and case studies to help readers understand the real-world applications of AI and big data technologies. The book also highlights cutting-edge research on AI and big data, including novel algorithms, tools, and techniques. It discusses the challenges and opportunities of using AI and big data to develop smart applications and provides recommendations for the development of responsible and transparent AI-based systems. This book is a valuable resource for researchers and professionals looking to stay up-to-date with the latest advancements in AI and big data and how they can be applied to solve real-world challenges.

Our book, Future of Tech Startups and Innovations in the Age of AI, mainly focuses on artificial intelligence (AI) tools, AI-based startups, AI-enabled innovations, Autonomous AI Agents (Auto-GPT), AI-based marketing startups, machine learning for organizations, AI-internet of things (IoT) for new tech companies, AI-enabled drones for agriculture industry, machine learning (ML)/deep learning (DL)-based drip farming, AI-based driverless cars, AI-based weather prediction startups, AI tools for personal branding, AI-based teaching, AI-based doctor/hospital startups, AI for game companies, AI-based finance tools, AI for human resource management, AI-powered management tools, AI tools for future pandemics, AI/ML-based transportation companies, AI for media, AI for carrier counseling, AI for customer care, AI for next generation businesses, and many more applications. AI tools and techniques will revolutionize startups all over the world. Entrepreneurs, engineers, and practitioners have already moved toward AI-based solutions to reshape businesses. AI/ML will create possibilities and opportunities for improving human lifestyles. AI-enabled startups will work on cost-effective solutions to solve difficult problems. Recently, many research companies are interested in providing solutions and investing a lot in AI-based startups. AI-driven products will revolutionize the "smart world." AI computing tech companies will help to model human speech recognition systems. Also, AI-based startups will focus on perception and reasoning of autonomous robotic systems. AI/ML-based tech startups will introduce smart online education systems for future pandemics. More interestingly, people are also moving for online job opportunities and trying to work from home. Future innovation needs closer relations between academia and industry. Therefore, online platforms need to be introduced that will only focus on academia and industry linkage. Future AI tech-based startups will focus more on research and development to introduce novel products to the market. Accordingly, engineers and many other people should be trained on AI tools and techniques to introduce innovative solutions for the smart world. In addition, integration of many new technologies with AI will be made possible. AI with IoT, smart cities, unmanned aerial vehicles (UAVs), wireless sensor networks, software-defined networks, network management, vehicular ad hoc networks, flying ad hoc networks, wireless communication technologies, ML, reinforcement learning, federated learning and other mechanisms will introduce new technological products.

Our book, Future of Tech Startups and Innovations in the Age of AI, mainly focuses on artificial intelligence (AI) tools, AI-based startups, AI-enabled innovations, Autonomous AI Agents (Auto-GPT), AI-based marketing startups, machine learning for organizations, AI-internet of things (IoT) for new tech companies, AI-enabled drones for agriculture industry, machine learning (ML)/deep learning (DL)-based drip farming, AI-based driverless cars, AI-based weather prediction startups, AI tools for personal branding, AI-based teaching, AI-based doctor/hospital startups, AI for game companies, AI-based finance tools, AI for human resource management, AI-powered management tools, AI tools for future pandemics, AI/ML-based transportation companies, AI for media, AI for carrier counseling, AI for customer care, AI for next generation businesses, and many more applications. AI tools and techniques will revolutionize startups all over the world. Entrepreneurs, engineers, and practitioners have already moved toward AI-based solutions to reshape businesses. AI/ML will create possibilities and opportunities for improving human lifestyles. AI-enabled startups will work on cost-effective solutions to solve difficult problems. Recently, many research companies are interested in providing solutions and investing a lot in AI-based startups. AI-driven products will revolutionize the "smart world." AI computing tech companies will help to model human speech recognition systems. Also, AI-based startups will focus on perception and reasoning of autonomous robotic systems. AI/ML-based tech startups will introduce smart online education systems for future pandemics. More interestingly, people are also moving for online job opportunities and trying to work from home. Future innovation needs closer relations between academia and industry. Therefore, online platforms need to be introduced that will only focus on academia and industry linkage. Future AI tech-based startups will focus more on research and development to introduce novel products to the market. Accordingly, engineers and many other people should be trained on AI tools and techniques to introduce innovative solutions for the smart world. In addition, integration of many new technologies with AI will be made possible. AI with IoT, smart cities, unmanned aerial vehicles (UAVs), wireless sensor networks, software-defined networks, network management, vehicular ad hoc networks, flying ad hoc networks, wireless communication technologies, ML, reinforcement learning, federated learning and other mechanisms will introduce new technological products.

This book presents a comprehensive exploration of LPWANs, delving into their fundamental concepts, underlying technologies, and the multifaceted challenges they tackle. This book recognizes that LPWANs don't operate in isolation; they are intimately intertwined with Artificial Intelligence and Machine Learning (AI/ML) technologies, which play a pivotal role in optimizing LPWAN performance and capabilities. The book is a collection of original contributions regarding air interface, transmission technologies and novel network architectures, such as network slicing, cloud/fog/edge computing, ad hoc networks and software-defined network. Also, this book provides a guide for researchers of IoT applications to choose suitable LPWAN technologies and describe the design aspects, network architectures, security issues and challenges.Features:Explains machine learning algorithms onto low-power wide area network sensors for compressed communications. Illustrates wireless-based Internet of Things networks using low-power wide area networks technology for quality air. Presents cognitive Internet of Things networks using wireless communication, and low-power wide area network technologies for Ad Hoc networks. Discusses a comprehensive study of low-power wide area networks for flying Ad Hoc networks. Showcases the study of energy efficient techniques aided by low-power wide area network technologies for the Internet of Things networks. The text is aimed at senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Artificial Intelligence (AI) innovations in digital health offer unprecedented opportunities to facilitate human health and provide tools and techniques that reduce overall costs. This book discusses the use of AI to improve diagnostic accuracy, patient monitoring, the use of remote diagnostic tools, identification of life-threatening diseases, medical robotics applications, drug discovery, technology-driven solutions, and much more.AIoT Innovations in Digital Health: Emerging Trends, Challenges, and Solutions presents integrated technologies such as Green Computing, IoT, and Big Data using AI, Machine Learning, Deep Learning, and Federated Learning for Healthcare. It discusses the future of medical robotics using Machine Learning and highlights the use of Federated Learning-based patient monitoring applications. This book also elaborates on the role that AI and Machine Learning play in drug discovery.Interested readers will include anyone working in or involved in smart healthcare research which includes, but is not limited to, healthcare specialists, computer science engineers, electronics engineers, systems engineers, and pharmaceutical practitioners.

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.This book:Provides a better understanding of artificial intelligence-based applications for future smart citiesPresents a detailed understanding of artificial intelligence tools for intelligent technologiesShowcases intelligent computing technologies in obtaining optimal solutions using artificial intelligenceDiscusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs)Covers machine learning-based Intrusion detection system (IDS) for smart gridIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

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.

This comprehensive guide examines the restructuring of instructional design principles and practices for digital learning environments. It combines established pedagogical principles with the latest technological innovations to help practitioners create effective, learner‑centered online experiences.The volume grounds its recommendations in foundational instructional design models, including ADDIE, TPACK, and the Community of Inquiry framework, to demonstrate how these approaches are being reconfigured for digital transformation. It explores multimedia learning design through Cognitive Load Theory, Dual Coding Theory, and Mayer’s Cognitive Theory of Multimedia Learning, offering guidance on optimizing the integration of text, images, audio, video, and interactive elements. The text presents architectural frameworks for flipped classrooms using Small Private Online Courses and examines the integration of synchronous and asynchronous modalities in blended higher education. Accessibility is addressed through Universal Design for Learning and Web Content Accessibility Guidelines implementation strategies, while emotional design principles illustrate the creation of meaningful learner‑content connections. The volume explores emerging technologies, including GenAI integration within learning management systems for personalized feedback and intelligent tutoring, AI‑driven adaptive learning systems, and virtual reality and augmented reality applications that foster embodied cognition and situated learning across disciplines, from medical education to historical reconstruction.This collection is designed for instructional designers, educational technologists, and faculty members seeking evidence‑based strategies for digital course development. It will help graduate students, curriculum developers, and policymakers in promoting equitable access to quality online education.

Industry Transformation: Strategies for Digital Innovation and AI Adoption Technologies offers a timely and comprehensive guide to navigating the evolving landscape of digital transformation across industries. As organisations face mounting pressure to adapt to technological disruption and shifting market demands, this timely work provides a strategic framework for integrating artificial intelligence, big data, IoT, blockchain, and cybersecurity into business operations. Drawing on real-world case studies and actionable insights, it bridges the gap between theory and practice, equipping readers with the tools to lead successful digital initiatives.Beyond the technical dimensions, the authors address the ethical and regulatory considerations essential to responsible AI adoption. With a balanced focus on both innovation and human-centric approaches, it empowers executives, managers, policymakers, and academics to foster sustainable growth in the digital age. By synthesising cutting-edge research and industry expertise Industry Transformation serves as an indispensable resource for those seeking to unlock the full potential of digital innovation in their sectors.

This book aims to provide a vision that can combine the best of both Artificial Intelligence (AI) and communication networks for designing the deployment trajectory to establish flexible Unmanned Aerial Vehicles (UAV) communication networks.This book will discuss the major challenges that can face deploying unmanned aerial vehicles in emergent networks. It will focus on possible applications of UAV in a Smart City environment where they can be supported by Internet of Things (IoT), wireless sensor networks, as well as 5G, and beyond.  This book presents the possible problems and solutions, the network integration of the UAV and compare the communication technologies to be used.This book will be a collection of original contributions regarding state of the art AI/ML based solutions in UAV communication networks which can be used for routing protocol design, transport layer optimization, user/application behaviour prediction, communicationnetwork optimization, security, and anomaly detection.

This book aims to provide a vision that can combine the best of both Artificial Intelligence (AI) and communication networks for designing the deployment trajectory to establish flexible Unmanned Aerial Vehicles (UAV) communication networks.This book will discuss the major challenges that can face deploying unmanned aerial vehicles in emergent networks. It will focus on possible applications of UAV in a Smart City environment where they can be supported by Internet of Things (IoT), wireless sensor networks, as well as 5G, and beyond.  This book presents the possible problems and solutions, the network integration of the UAV and compare the communication technologies to be used.This book will be a collection of original contributions regarding state of the art AI/ML based solutions in UAV communication networks which can be used for routing protocol design, transport layer optimization, user/application behaviour prediction, communicationnetwork optimization, security, and anomaly detection.

This book presents a collection of state-of-the-art artificial intelligence and big data analytics approaches to cybersecurity intelligence. It illustrates the latest trends in AI/ML-based strategic defense mechanisms against malware, vulnerabilities, cyber threats, as well as proactive countermeasures. It also introduces other trending technologies, such as blockchain, SDN, and IoT, and discusses their possible impact on improving security. The book discusses the convergence of AI/ML and big data in cybersecurity by providing an overview of theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in different approaches of security. It also displays solutions that will help analyze complex patterns in user data and ultimately improve productivity.This book can be a source for researchers, students, and practitioners interested in the fields of artificial intelligence, cybersecurity, data analytics, and recent trends ofnetworks.

This book presents a collection of state-of-the-art artificial intelligence and big data analytics approaches to cybersecurity intelligence. It illustrates the latest trends in AI/ML-based strategic defense mechanisms against malware, vulnerabilities, cyber threats, as well as proactive countermeasures. It also introduces other trending technologies, such as blockchain, SDN, and IoT, and discusses their possible impact on improving security. The book discusses the convergence of AI/ML and big data in cybersecurity by providing an overview of theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in different approaches of security. It also displays solutions that will help analyze complex patterns in user data and ultimately improve productivity.This book can be a source for researchers, students, and practitioners interested in the fields of artificial intelligence, cybersecurity, data analytics, and recent trends ofnetworks.

With chapters dedicated to exploring the role of AI in securing IoT devices, employing supervised and unsupervised learning techniques for threat classification, and harnessing the power of recurrent neural networks for time series analysis, this book presents a holistic view of the evolving cybersecurity landscape.

This book delves into the next generation of robotics, where the fusion of AGI with IoT networks brings unprecedented capabilities to machines, allowing them to think, adapt, and collaborate in ways previously confined to science fiction. In the rapidly evolving world of technology, the convergence of Artificial General Intelligence (AGI) and the Internet of Things (IoT) is unlocking new frontiers for robotics, transforming how we interact with machines and the environment. This book explores the groundbreaking synergy between AGI and IoT, focusing on their transformative impact on robotics and automation systems.It presents advanced applications in computer vision, showing how robots are becoming more intelligent, autonomous, and capable of interpreting and interacting with the world around them with human-like precision.The author offers insightful analyses into the future of robotics, discussing upcoming trends, the challenges of developing AG-IoT ecosystems, and the ethical implications of these technological advancements. With a focus on cutting-edge research and real-world examples, this book serves as an essential resource for researchers, engineers, and enthusiasts interested in the future of robotics, artificial intelligence, and the IoT.Whether you are exploring the potential of AG-IoT integration or seeking to understand the future of intelligent robotics, this book is your gateway to the future of automation and intelligent systems.

In today’s rapidly advancing technological landscape, the integration of Artificial Intelligence (AI) and Intelligent Transportation Systems (ITS) is revolutionizing transportation. This integration is reshaping ITS by enhancing accuracy and reliability, enabling efficient navigation, and optimizing both traffic and public transport management. AI-Driven Transportation Systems: Real-Time Applications & Related Technologies explores the powerful synergy between AI and modern transportation infrastructures, highlighting their transformative impact on traffic management, autonomous vehicles, and real-time decision-making.This book delves into the next generation of transportation systems, where AI-driven solutions enhance efficiency, safety, and sustainability. From intelligent traffic monitoring and predictive analytics to autonomous navigation and smart city applications, AI is redefining how transportation networks operate, ensuring seamless connectivity and optimized resource utilization. The authors provide in-depth analyses of emerging trends, addressing the challenges of AI integration in ITS, along with ethical considerations and anticipated future advancements. With a focus on cutting-edge research and real-world applications, this book serves as an essential resource for researchers, engineers, policymakers, and professionals interested in the future of AI-powered transportation. Whether you are exploring the potential of AI in transportation or seeking to understand the future of ITS, this book is your gateway to the next era of smart and connected transportation networks.

"AI at Edge: Transforming Edge Networks with Computational Intelligence" explores how artificial intelligence enhances the efficiency and capabilities of edge computing. It focuses on key topics like task offloading, resource management, communication optimization, Explainable AI, Security, privacy, and real-time decision-making at the edge. Readers will gain insights into practical methods and tools used to solve real-world challenges in low-latency and high-efficiency networks. The book includes clear explanations, practical case studies, and illustrations to help readers understand complex concepts. It is designed for researchers, professionals, and students, offering a mix of foundational knowledge and advanced techniques. By reading this book, readers will learn how to design smarter, faster, and more secure edge networks using AI. " AI at Edge: Transforming Edge Networks with Computational Intelligence " is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of computational intelligence and edge computing.

This book brings together forward-looking research and practical innovations that address real-world technological and societal challenges. It offers readers actionable insights, modern solutions, and interdisciplinary perspectives that support smarter decision-making, sustainable development, and digital transformation.The contributions highlight emerging trends across artificial intelligence, cybersecurity, data science, intelligent systems, healthcare technologies, and sustainable computing. Each chapter presents applied methodologies, experimental findings, and practical frameworks that can be directly adapted by researchers, practitioners, and industry professionals. By connecting academic research with real implementation challenges, the book serves as a valuable reference for those seeking to translate ideas into impact. It supports collaboration between disciplines and promotes innovation that responds to global needs. Designed for researchers, engineers, and advanced students, this collection reflects the spirit of knowledge exchange and international cooperation fostered through the 3rd International Conference on Emerging Trends and Innovation (ICETI 2025).