Mudassir Khan – författare

Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systemsDiscusses the integration of artificial intelligence with machine learning using mathematical modelingElaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matricesIntroduces modern algorithms such as convolutional neural networks and support vector machinesPresents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.

Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systemsDiscusses the integration of artificial intelligence with machine learning using mathematical modelingElaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matricesIntroduces modern algorithms such as convolutional neural networks and support vector machinesPresents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.

The book highlights the importance of using machine learning algorithms for smart intelligent devices, security and privacy issues of the Internet of Things.Features:Discusses comprehensively topics such as sensor data collection, data preprocessing, feature extraction, and model trainingOffers simplified explanations of algorithms like neural networks, decision trees, and clustering techniques, making them accessible to readers with limited technical backgroundShowcases practical insights by presenting real-world applications of the Internet of Things, and machine learning in domains such as smart homes, healthcare, transportation, and manufacturingCovers issues such as data quality, model overfitting, security vulnerabilities, and scalability. Includes tutorials on setting up Internet of Things devices, collecting and preprocessing data, and building machine learning modelsThe text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Cloud computing has revolutionized digital infrastructure, but it has also introduced complex forensic challenges that demand intelligent, scalable, and automated investigative solutions. AI Driven Cloud Forensics: Advanced Techniques for Next Generation Digital Investigations provides a comprehensive exploration of how artificial intelligence, machine learning, big data analytics, and automation are transforming the future of digital forensics in cloud environments. This edited volume examines advanced methodologies for forensic acquisition in virtual machines, containers, and ephemeral environments; intelligent cloud architectures for secure investigations; and AI-powered frameworks for anomaly detection, fraud investigation, phishing detection, and log analysis. It highlights emerging approaches such as AutoML-enabled forensic systems, federated learning for communication-efficient anomaly detection, AI-centric privileged access management, and real-time detection of data fabrication attacks in decentralized systems. Beyond core forensic technologies, the book addresses interdisciplinary applications across smart micro-grids, dark web environments, digital supply chains, tourism systems, and e-discovery processes. By integrating automation with forensic intelligence, the contributors demonstrate how next-generation digital investigations can become more proactive, predictive, and adaptive. Special emphasis is placed on addressing challenges such as data volatility, multi-tenancy, encryption, jurisdictional constraints, and large-scale log analysis in distributed cloud ecosystems. The book also includes a bibliometric and science-mapping analysis to provide insights into evolving research trends in AI-driven cloud forensic automation. Designed for researchers, cybersecurity professionals, forensic analysts, legal practitioners, cloud architects, and postgraduate students, this volume bridges theoretical foundations with real-world implementations. It serves as a forward-looking reference for building resilient, trustworthy, and AI-enabled forensic ecosystems capable of responding to increasingly sophisticated cyber threats. By combining innovation with investigative rigor, AI Driven Cloud Forensics contributes to shaping the next generation of intelligent digital security and forensic readiness in cloud-driven environments.

Artificial Intelligence is reshaping modern healthcare in remarkable ways. This book explores how multimodal generative AI integrates clinical, genetic, and imaging data to improve the understanding and management of neurodegenerative diseases. Bridging technology and medicine, it offers accessible insights into next-generation precision healthcare and its real-world impact. Designed for researchers, clinicians, and informed readers alike, it highlights practical applications, ethical considerations, and future possibilities in AI-driven neurological care.This volume provides a comprehensive examination of how multimodal generative AI techniques can be applied to the diagnosis, monitoring, and management of neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. It covers foundational concepts in precision healthcare, multimodal data integration (including genetic, clinical, and neuroimaging data), and advanced modeling approaches for disease progression prediction. The book also explores synthetic data generation for rare conditions, explainable and interpretable AI models for clinical decision support, and AI-driven early detection systems. Dedicated chapters address ethical, legal, and regulatory considerations, interdisciplinary collaboration, and patient-centric AI design. Through case studies and real-world applications, the book demonstrates how intelligent systems can enhance personalized treatment planning, improve clinical outcomes, and support data-driven healthcare innovation.

This book addresses the environmental challenges of the digital technology sector and presents actionable solutions to mitigate its impacts. It provides a detailed examination of the environmental footprint of digital technologies across their entire lifecycle, from resource extraction to disposal. The text explores advanced energy-saving techniques, including renewable energy integration and power optimization strategies for data centers, while offering insights into eco-friendly product design and innovative manufacturing processes. Comprehensive strategies for improving energy efficiency, integrating renewable energy, and optimizing cooling and power management are discussed in depth. Additionally, the book evaluates global policies, their effectiveness, and proposes a robust framework to address the environmental impact of digital technologies.Primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, communications engineering, computer science, environmental engineering, and information technology, this text serves as a critical resource for understanding and addressing the environmental implications of digital innovation.

This book is essential for any leader seeking to understand how to leverage intelligent automation and predictive maintenance to drive innovation, enhance productivity, and minimize downtime in their manufacturing processes. Intelligent automation is widely considered to have the greatest potential for Industry 4.0 innovations for corporations. Industrial machinery is increasingly being upgraded to intelligent machines that can perceive, act, evolve, and interact in an industrial environment. The innovative technologies featured in this machinery include the Internet of Things, cyber-physical systems, and artificial intelligence. Artificial intelligence enables computer systems to learn from experience, adapt to new input data, and perform intelligent tasks. The significance of AI is not found in its computational models, but in how humans can use them. Consistently observing equipment to keep it from malfunctioning is the procedure of predictive maintenance. Predictive maintenance includes a periodic maintenance schedule and anticipates equipment failure rather than responding to equipment problems. Currently, the industry is struggling to adopt a viable and trustworthy predictive maintenance plan for machinery. The goal of predictive maintenance is to reduce the amount of unanticipated downtime that a machine experiences due to a failure in a highly automated manufacturing line. In recent years, manufacturing across the globe has increasingly embraced the Industry 4.0 concept. Greater solutions than those offered by conventional maintenance are promised by machine learning, revealing precisely how AI and machine learning-based models are growing more prevalent in numerous industries for intelligent performance and greater productivity. This book emphasizes technological developments that could have great influence on an industrial revolution and introduces the fundamental technologies responsible for directing the development of innovative firms. Decision-making requires a vast intake of data and customization in the manufacturing process, which managers and machines both deal with on a regular basis. One of the biggest issues in this field is the capacity to foresee when maintenance of assets is necessary. Leaders in the sector will have to make careful decisions about how, when, and where to employ these technologies. Artificial Intelligence and Machine Learning for Industry 4.0offers contemporary technological advancements in AI and machine learning from an Industry 4.0 perspective, looking at their prospects, obstacles, and potential applications.

Digital Twins and ESG provides essential insight on how integrating cutting-edge Digital Twin technology with ESG practices can transform the understanding of sustainability and propel businesses towards a more transparent, accountable, and responsible future. Digital Twins and ESG introduces the dynamic world of ESG practices, emphasizing the pivotal role technology plays in shaping and advancing sustainability goals. It introduces readers to the multifaceted world of Digital Twin technology, offering a comprehensive understanding of its historical development and diverse applications across industries. This volume will intricately examine the integration of Digital Twins in ESG metrics and reporting frameworks. Artificial intelligence, machine learning, and blockchain technologies are also discussed as key enablers for achieving ESG goals, providing readers with a glimpse into the potential advancements and breakthroughs that lie ahead. Through detailed analyses and case studies, readers will gain insights into how Digital Twins enhance data collection, monitoring, and reporting, elevating transparency and accountability. Digital Twins and ESG serves as a rallying call, urging businesses to embrace Digital Twins as an integral component of their ESG strategies, ultimately paving the way for a more sustainable and responsible future.

In an era defined by global disruptions, sustainability mandates and rapid technological advancement, Next-Gen Supply Chains provides an essential roadmap for navigating the complex transformation of global supply networks. This comprehensive book moves beyond isolated trends to present an integrated framework where generative AI, blockchain, autonomous systems and the Internet of Things converge to build resilient, efficient and responsible operations. This book masterfully bridges the critical gap between theoretical concepts and practical implementation, offering readers actionable strategies, detailed case studies from industry leaders such as Amazon and Lenovo, and robust frameworks for risk management, ethical AI governance and circular economy integration. With its unique emphasis on the synergy between technological innovation and the necessary human capital development, the book is an indispensable resource for supply chain executives, operations managers, technology implementers and academics seeking to future-proof their organizations and master the strategic imperatives of the modern supply chain landscape.

This book is considered as an essential medium to introduce the up-to-date findings, new level research, and developing fields in the area of technology and computing for First International Conference on Emerging Technologies and Computing Innovations (ICETCI-2025). The objective of the ICETCI-2025 is to provide a platform to highlight state-of-the-art research work and innovative findings in technology and computing. The conference is focused on filling the gap between modern theoretical improvements and real-life issues by providing a platform for discussions among experts in different areas concerning innovative fields like AI, ML, big data, blockchain, IoT giant computing, etc. ICETCI-2025 benefits from being a multidisciplinary approach for a broad set of emerging technologies. The conference aims to foster the exchange of ideas and experiences by bringing together leaders, researchers, academics, industry practitioners as well as policymakers. Much of the diversity they provide aids unconventional thinking, applicable implementation, and strategic growth that tackles current as well as future technology hurdles. ICETCI-2025 proceedings present high quality research papers, case studies and reviews focusing on state of the art developments in domains like artificial intelligence, big data analytics, cybersecurity and privacy, IoT and blockchain and cloud computing. By collaborating with academia, industry leaders, and policymakers, these proceedings are essential for bringing attention to emerging trends and best practices. They play a critical role in promoting knowledge, stimulating innovation, and providing cross-disciplinary support to help drive technological development. This book is aimed at researchers, academics, graduate students, industry professionals, and policymakers who have an interest in the field of computing, information technologies, and their application.

This book provides a deep dive into how Generative Artificial Intelligence is fostering innovation apart from being impactful, and key problems that affect the healthcare ecosystem as a whole are now being solved. With the rapid rise of Generative Artificial Intelligence (AI), industries are changing rapidly, and healthcare is no exception. Generative AI, as a transformative subset of AI, has the extraordinary ability to create new data, simulate scenarios, and optimize complex processes. Its potential for medical diagnostics and drug development is beyond doubt and it is born to revolutionize medical research and Personalized treatment. Reasoning subtitled Generative AI for Healthcare, this exploratory volume on cutting-edge technology's profound impact on modern medicine is based on years of dedicated research. Key highlights include:·         Using Generative AI to actually produce synthetic teaching sets and consequently re-armed medical-solving equipment will in turn enhance diagnosis and accuracy·         Reducing the amount of time and costs for developing drugs by means of artificial intelligence, by iterating through virtual compounds and simulating molecular environments·         Creating personalized treatment strategies out of specific patient profiles that embrace precision medicine, improving outcomes·         Simulating medical scenarios to measure the effectiveness of a cure in advance expensive, high-risk trials, the profound secrets behind complex diseases are slowly coming to light.This book is designed to help the next generation of healthcare practices address research gaps, regulatory challenges, ethical considerations, and other concerns. Balancing rich case studies with practical insights and forward -looking ideas, this book aims to link up the promise of technological innovation in healthcare with its real application. The book also highlights the need for responsible AI adoption, addressing issues such as data privacy, bias reduction in models, explainable and reliable AI systems. The goal is to provide healthcare professionals, researchers and policy makers with knowledge and tools needed to use Generative AI responsibly and effectively. Whether you are a healthcare provider, AI researcher or industry innovator, Generative AI for Healthcare gives you an essential primer on how AI can be applied to transform patient care outcomes, drive medical research forward -- and even shape the future of healthcare itself.