This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: - Basic application of machine learning and deep learning in recommendation process and the evaluation metrics - Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 - Tensor factorization in different types of recommendation system - Ranking framework and topic modeling to recommend author specialization based on content. - Movie recommendation systems - Point of interest recommendations - Mobile tourism recommendation systems for visually disabled persons - Automation of fashion retail outlets - Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.

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.

This book presents research focused on the design of fractal antennas using bio-inspired computing techniques. The authors present designs for fractal antennas having desirable features like size reduction characteristics, enhanced gain, and improved bandwidths. The research is summarized in six chapters which highlight the important issues related to fractal antenna design and the mentioned computing techniques. Chapters demonstrate several applied concepts and techniques used in the process such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The work aims to provide cost-effective and efficient solutions to the demand for compact antennas due to the increasing demand for reduced sizes of components in modern wireless communication devices. A key feature of the book includes an extensive literature survey to understand the concept of fractal antennas, their features, and design approaches. Another key feature is the systematic approach to antenna design. The book explains how the IE3D software is used to simulate various fractal antennas, and how the results can be used to select a design. This is followed by ANN model development and testing for optimization, and an exploration of ANN ensemble models for the design of fractal antennas. The bio-inspired computing techniques based on GA, PSO, and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the given computing techniques is also explained to demonstrate how to select the best algorithm for a given bio-inspired design. Finally, the book explains how to evaluate antenna designs. This book is a valuable resource for students (from UG to PG levels) and research scholars undertaking learning modules or projects on microstrip and patch antenna design in communications or electronics engineering courses.

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.

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.