Reinventing Technological Innovations with Artificial Intelligence delves into the transformative impact of Augmented and Virtual Reality (AVR) technology across industries. The book explores the merging of real and digital worlds, paving the way for personalized experiences in areas such as tourism, marketing, education, and more. With the potential to redefine business practices and societal norms in the era of Industry 4.0, AVR technologies hold untapped potential beyond gaming and entertainment. This volume presents a comprehensive overview of the current landscape, challenges, and prospects of integrating AVR with Artificial Intelligence (AI) for innovation and sustainability in various domains. The book presents 11 edited chapters contributed by technology and innovation experts that explore applications of AI, AR and VR technologies in different sectors in both public and private sectors. The editors have included reviews of technologies that impact human resource management, corporate social responsibility, healthcare, supply chain and criminal investigation. The reviews also highlight the role of AI in sustainable agriculture and smart cities. Key Features: Unveils the role of AVR in transforming real surroundings into digitally enhanced personal experiences. Explores AVR's applications beyond gaming in diverse sectors like marketing, construction, education, and more. Discusses challenges such as technical limitations, high costs, and resistance to adopting AVR. Addresses the need to enhance the reliability and effectiveness of AVR technologies in various industries. Provides a comprehensive perspective on AI innovations, AR, and VR technologies with real-world examples. The book is an informative reference for researchers, professionals, and experts in technology, innovation, who are interested in the convergence of Augmented and Virtual Reality with AI for practical applications in diverse industries.

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI) and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 of which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery: AI's Impact on Biology and Energy Management AI and CI in Physical Sciences and Predictive Modeling Computational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers.

Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems. The book also presents solutions for analyzing complex patterns in user data and improving productivity. Readers will be able to understand the technology that drives big data solutions in HCI projects and understand its capacity in transforming an organization.The book also helps the reader to understand HCI system design and explains how to evaluate an application portfolio that can be used when selecting pilot projects. This book is a resource for researchers, students, and professionals interested in the fields of HCI, artificial intelligence, data analytics, and computer engineering.

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.

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.