Intelligent Computational Systems presents current and future developments in intelligent computational systems in a multi-disciplinary context. Readers will learn about the pervasive and ubiquitous roles of artificial intelligence (AI) and gain a perspective about the need for intelligent systems to behave rationally when interacting with humans in complex and realistic domains. This reference covers widespread applications of AI discussed in 11 chapters which cover topics such as AI and behavioral simulations, AI schools, automated negotiation, language analysis and learning, financial prediction, sensor management, Multi-agent systems, and much more. This reference work is will assist researchers, advanced-level students and practitioners in information technology and computer science fields interested in the broad applications of AI.

This book gives an overview of innovative approaches in telehealth and telemedicine. The Goal of the content is to inform readers about recent computer applications in e-health, including Internet of Things (IoT) and Internet of Medical Things (IoMT) technology. The 9 chapters will guide readers to determine the urgency to intervene in specific medical cases, and to assess risk to healthcare workers. The focus on telehealth along with telemedicine, encompasses a broader spectrum of remote healthcare services for the reader to understand. Chapters cover the following topics: - A COVID-19 care system for virus precaution, prevention, and treatment - The Internet of Things (IoT) in Telemedicine, - Artificial Intelligence for Remote Patient Monitoring systems - Machine Learning in Telemedicine - Convolutional Neural Networks for the detection and prediction of melanoma in skin lesions - COVID-19 virus contact tracing via mobile apps - IoT and Cloud convergence in healthcare - Lung cancer classification and detection using deep learning - Telemedicine in India This book will assist students, academics, and medical professionals in learning about cutting-edge telemedicine technologies. It will also inform beginner researchers in medicine about upcoming trends, problems, and future research paths in telehealth and telemedicine for infectious disease control and cancer diagnosis.

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.

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.

Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

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.

The book gives a comprehensive overview of the evolving landscape of cyber-physical systems (CPS) with a primary focus on security challenges and innovative solutions. CPS, encompassing a wide array of applications from e-Health to smart grid and industry automation, is explored in depth through eight edited reviews. The book starts with an exploration of various threat detection and prevention techniques in IoT environments, followed by discussions on security in smart grid cyber-physical systems, and the integration of cyber-physical systems with game theory. It also covers important topics such as cyber-physical systems in healthcare, augmented reality challenges, network and computer forensic frameworks, and a review of industrial critical infrastructure perspectives. The journey from traditional data warehouses to data lakes is thoroughly examined, shedding light on the evolution of data storage methods. The final chapter explains intrusion detection in industrial critical infrastructure, reviewing feature selection and classification models. By navigating through these topics, the book equips readers with a comprehensive understanding of cybersecurity challenges and solutions in an era of automation and IoT technologies. This book is intended for a diverse readership, including professionals, researchers, and technology enthusiasts keen on exploring the intricacies of CPS, IoT security, data storage evolution, and industrial infrastructure protection. Key Features: -Analytical insights into cyber-physical systems security. -Thorough exploration of threat detection and prevention techniques. -Application-focused chapters covering smart grid, healthcare, and more. -Integration of game theory and augmented reality in cyber-physical systems. -Comprehensive overview on network and computer forensic frameworks.

Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.

Optimization Techniques for Decision-making and Information Security is a scholarly compilation that has been edited by experts with specialized knowledge in the fields of decision theory and cybersecurity. Through the synthesis of an extensive array of information, this edited volume presents novel methodologies and approaches that forge a link between the critical domain of information security and the realm of decision-making processes. The publication commences with a fundamental investigation that establishes the theoretical foundations of information security-relevant decision-making models. The subsequent chapters present comprehensive evaluations of real-world applications, showcasing an assortment of optimization techniques. The book offers a wide range of perspectives on the practical implementation of data analysis in various domains, including but not limited to power generation and optimization, solid transportation problems, soft computing techniques, wireless sensor networks, parametric set-valued optimization problems, data aggregation optimization techniques, fuzzy linear programming problems, and nonlinear chaotic systems. The anthology concludes with a comprehensive summary of the most noteworthy observations and ramifications extracted from the projects of all contributors. Key features - Presents a wide variety of sophisticated optimization methodologies - Explores the intricate intersection of decision theory and the safeguarding of confidential information. - Emphasizes effectiveness in improving decision-making processes designed to strengthen information security measures. - Showcases practical examples in different industrial domains through case studies and real-world problems. - Provides guidance and contemplations on strengthening information security environments. - Includes scientific references for advanced reading This book serves as an essential reference for policymakers, researchers, and professionals who are learning about or working in information security roles.

This reference reviews the architectural requirements of IT systems that are designed to digitally transform business operations. It is a compilation of 7 timely reviews that demonstrate how adopting emerging technologies and examining the security-based concerns can lead to innovation in the business sector. The aim of the book is to guide scholars and business consultants on IT and business frameworks that can help new and existing organizations navigate the challenges posed by disruptive technologies to create a competitive advantage. The reviews are contributed by experts in business and information technology. The chapters cover diverse topics related to technological advancements and digital security measures. Chapter 1 offers insights into accessing and securing patient medical records through a blockchain-based framework, detailing research methodology, scalability, and standards. Chapter 2 discusses cyber threats in IoT-connected cars, addressing vulnerabilities, attack methods, and defense strategies. Chapter 3 focuses on malware analysis and detection using machine learning techniques. Chapter 4 emphasizes on securing IoT-based home automation. Chapter 5 presents an IoT policy and governance reference architecture to ensure integrity and security across devices. Chapter 6 explores organizational security improvements to prevent deepfake ransomware. Finally, Chapter 7 examines the use of machine learning in credit card fraud detection, discussing challenges and control layers.