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This book highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include: - The Role of Deep Learning in Healthcare Industry: Limitations - Generative Adversarial Networks for Deep Learning in Healthcare - The Role of Blockchain in the Healthcare Sector - Brain Tumor Detection Based on Different Deep Neural Networks Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening. Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.

Increasingly global and online social interactions and financial transactions involve digital data, computing devices and the internet. With cloud computing, remote computing, enterprise mobility and e-commerce on the rise, network security has become a priority. Selecting an appropriate algorithm and policy is a challenge for computer security engineers, as new technologies provide malicious users with opportunities to intrude into computer networks. New Age Cyber Threat Mitigation for Cloud Computing Networks provides cloud and network engineers answers to cybersecurity challenges. It highlights new options, methodologies and feasible solutions that can be implemented in cloud architecture and IT Infrastructure, thereby securing end users. Chapters cover many topics related to cyber threats in the modern era. These topics include: · Ransomware and DDoS attacks · Security algorithms · Design and implementation solutions for resilient and fault-tolerant cloud and network services · Security policy · End user data security The book is an essential resource for anyone involved in cloud computing and network security, including learners, professionals and enthusiasts.

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care. Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science. Key topics covered in the book: - An introduction to the concept of the Internet of Medical Things (IoMT). - Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data. - A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs). - Artificial Intelligence and the Internet of Medical Things. - A review of new machine learning and AI solutions in different medical areas. - A Deep Learning based solution to optimize obstacle recognition for visually impaired patients. - A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications. - Deep Learning for brain tumor detection. - Blockchain and patient data management.

Video data analytics is rapidly evolving and transforming the way we live in urban environments. In Video Data Analytics for Smart City Applications: Methods and Trends, data science experts present a comprehensive review of the latest advances and trends in video analytics technologies and their extensive applications in smart city planning and engineering. The book covers a wide range of topics including object recognition, action recognition, violence detection, and tracking, exploring deep learning approaches and other techniques for video data analytics. It also discusses the key enabling technologies for smart cities and homes and the scope and application of smart agriculture in smart cities. Moreover, the book addresses the challenges and security issues in terahertz band for wireless communication and the empirical impact of AI and IoT on performance management. One contribution also provides a review of the progress in achieving the Jal Jeevan Mission Goals for institutional capacity building in the Indian State of Chhattisgarh. For researchers, computer scientists, data analytics professionals, smart city planners and engineers, this book provides detailed references for further reading and demonstrates how technologies are serving their use-cases in the smart city. The book highlights the advances and trends in video analytics technologies and extensively addresses key themes, making it an essential resource for anyone looking to gain a comprehensive understanding of video data analytics for smart city applications.

The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers. Key Features: - 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology - Provides examples of algorithms for heart diseases, Alzheimer’s disease, cancer, pneumonia and more - Includes techniques to detect plant disease - Includes information about the application of machine learning in specific imaging modalities - Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN - Each chapter provides an introduction and literature review and the relevant protocols to follow The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.

Computer Assistive Technologies for Physically and Cognitively Challenged Users focuses on the technologies and devices that assist individuals with physical and cognitive disabilities. These technologies facilitate independent activity and participation, serving to improve daily functional capabilities. The book features nine chapters that cover a wide range of computer assistive technologies that give readers an in-depth understanding of the available resources to help the elderly or individuals with disabilities. The topics covered in the book include 1) The category and ontology of assistive devices, 2) Web accessibility and ICT accessibility for persons with disability (PWD), 3) Assistive technologies for blind and visually impaired people, 4) Assistive technologies for home comfort and care, 5) Assistive technologies for hearing impaired people using Indian sign language synthetic animations, 6) Augmentative and alternative communication/hearing impairments, 7) Accessibility innovations to help physically disabled users, 8) Adhesive tactile walking surface indicators for elderly and visually impaired people mobility, 9) future of assistive technologies. This book serves as a textbook resource for students undertaking modular courses that require learning material on computer assistive technology. It also serves as a reference for graduate level courses in disability studies, human-computer interaction, gerontology and rehabilitation engineering. Researchers working in the allied fields intersecting computer science, medicine and psychology will also benefit from the information provided in the book.

Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimodal Affective Computing offers readers a concise overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches in applied affective computing systems and social signal processing. It covers affective facial expression and recognition, affective body expression and recognition, affective speech processing, affective text, and dialogue processing, recognizing affect using physiological measures, computational models of emotion and theoretical foundations, and affective sound and music processing. This book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.The book is an informative resource for academicians, professionals, researchers, and students at engineering and medical institutions working in the areas of applied affective computing, sentiment analysis, and emotion recognition.

Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications, including fraud detection and prevention, self-driving cars, recommendation systems, facial recognition technology, and intelligent computing. This book helps beginners learn the art and science of machine learning. It presens real-world examples that leverage the popular Python machine learning ecosystem, The topics covered in this book include machine learning basics: supervised and unsupervised learning, linear regression and logistic regression, Support Vector Machines (SVMs). It also delves into special topics such as neural networks, theory of generalisation, and bias and fairness in machine learning. After reading this book, computer science and engineering students - at college and university levels - will receive a complete understanding of machine learning fundamentals and will be able to implement neural network solutions in information systems, and also extend them to their advantage.

In the last two decades, many new fractional operators have appeared, often defined using integrals with special functions in the kernel as well as their extended or multivariable forms. Modern operators in fractional calculus have different properties which are comparable to those of classical operators. These have been intensively studied for modelling and analysing real-world phenomena. There is now a growing body of research on new methods to understand natural occurrences and tackle different problems. This book presents ten reviews of recent fractional operators split over three sections: 1- Chaotic Systems and Control (covers the Caputo fractional derivative, and a chaotic fractional-order financial system) 2- Heat Conduction (covers the Duhamel theorem for time-dependent source terms, and the Cattaneo-Hristov model for oscillatory heat transfer) 3- Computational Methods and Their Illustrative Applications (covers mathematical analysis for understanding 5 real-word phenomena: HTLV-1 infection of CD4+ T-cells, traveling waves, rumor-spreading, biochemical reactions, and the computational fluid dynamics of a non-powered floating object navigating in an approach channel) This volume is a resource for researchers in physics, biology, behavioral sciences, and mathematics who are interested in new applications of fractional calculus in the study of nonlinear phenomena.

This book is a quick review of machine learning methods for engineering applications. It provides an introduction to the principles of machine learning and common algorithms in the first section. Proceeding chapters summarize and analyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying machine learning methods to software engineering tasks. Finally, it gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general. Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book. Key Features: - Describes real-world problems that can be solved using machine learning - Explains methods for directly applying machine learning techniques to concrete real-world problems - Explains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). - It does not require prior knowledge of the machine learning. This book is meant to be an introduction to artificial intelligence (AI), machine earning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a background in advanced mathematics.