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Special education is an innovative practice to teach students with special needs, which incorporates alternate provisions, individual differences, and exceptional requirements to address the needs of people with disabilities. The reality is that typical classrooms are not sufficient to address the needs of students suffering from learning disabilities. AI-Assisted Special Education for Students With Exceptional Needs is a comprehensive and insightful exploration of the intersection between special education and artificial intelligence (AI), and it delves into the fundamental principles, historical background, and current challenges faced in providing effective education to students with special needs. Through comparisons of traditional education methods with special education practices, and an examination of the limitations of conventional classrooms in addressing the diverse learning difficulties of exceptional students, this book brings new solutions to the problem of how to best facilitate learning for these students. Recognizing the transformative potential of technology, the book emphasizes the role of AI, virtual reality (VR), and augmented reality (AR) in enhancing the learning experiences of disabled students. By leveraging these innovative resources and services, educators and students can benefit from interactive applications, adaptive personalized experiences, and smart classrooms equipped with the latest technologies. AI-assisted tools are explored in depth, showcasing their ability to support and improve the learning abilities of individuals with mental, physical, emotional, behavioral, or intellectual disorders. The book also explores the impact of AI-based special education on both educators and students, providing valuable insights and strategies for stakeholders to adopt and implement these technologies effectively. Legal aspects surrounding the right to education for disabled individuals are also discussed, ensuring a comprehensive understanding of the legal framework supporting special education initiatives. Real-world case studies and success stories serve as inspiration, illustrating the transformative potential of AI in special education. Featuring contributions from academicians and professionals from esteemed organizations, this book provides hands-on chapters that cover topics such as research challenges, data analysis, machine learning, intelligent learning environments, IoT wearable devices, comprehensive systems with intelligent teacher assistance, parental and societal roles, and the prospects of AI-assisted special education. Whether educator, mentor, training professional, researcher, or student, AI-Assisted Special Education for Students With Exceptional Needs offers a wealth of knowledge and practical insights. From historical perspectives to future reflections, this book serves as a guide for reshaping inclusive education and ensuring that specialized students receive the support and resources they need to thrive in their educational journeys. This book is an invaluable resource for anyone invested in advancing special education through the power of AI.

In today's era, there is a need for a system that can automate the process of treatment for the patient if medical facilities are out of reach. Smart healthcare can step in to make the patient more self-dependent. 6G with its features can be seen as the future of smart healthcare with IoT and AI. 6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis offers the fundamentals, history, reality, and challenges faced in the smart healthcare industry today. It discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used. The book details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems. The book goes on to presents applications of smart healthcare through various real-world examples and includes chapters on security and privacy in the 6G-enabled and IoT environment, as well as research on the future prospects of the smart healthcare industry.This book: Offers the fundamentals, history, reality, and the challenges faced in the smart healthcare industry Discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used Details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems Presents applications of smart healthcare through various real-world examples Includes topics on security and privacy in 6G-enabled IoT, as well as research and future prospectus of the smart healthcare industry Interested readers of this book 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.

With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.