This book shares new methodologies, technologies, and practices for resolving issues associated with leveraging AI-centric modeling, data analytics, machine learning-aided models, Internet of Things-driven applications, and cybersecurity techniques in the era of Industrial Revolution 4.0. AI-Centric Modeling and Analytics: Concepts, Technologies, and Applications focuses on how to implement solutions using models and techniques to gain insights, predict outcomes, and make informed decisions. This book presents advanced AI-centric modeling and analysis techniques that facilitate data analytics and learning in various applications. It offers fundamental concepts of advanced techniques, technologies, and tools along with the concept of real-time analysis systems. It also includes AI-centric approaches for the overall innovation, development, and implementation of business development and management systems along with a discussion of AI-centric robotic process automation systems that are useful in many government and private industries. This reference book targets a mixed audience of engineers and business analysts, researchers, professionals, and students from various fields. <!----> <!----> <!----> <!---->

The rise of the metaverse, a virtual shared space where users interact collectively, has generated significant interest in examining the dynamics of human-machine interaction within this digital environment. This study presents an extensive overview of current research on human-machine interaction (HMI) in the metaverse, encompassing a variety of technologies, methodologies, and consequences. By drawing upon a diverse array of academic references, this study delves into the fundamental aspects of HMI in the metaverse, such as gesture and motion tracking, voice recognition, virtual and augmented reality interfaces, haptic feedback, brain-computer interfaces, avatars, personalization, and collaborative engagement. Furthermore, this study addresses the obstacles and possibilities linked to HMI in the metaverse and provides a framework for swift integration of HMI in the metaverse.

With the rapidly changing technological landscape, automating daily routine tasks for accurate, reliable, and consistent outputs has become a priority for many companies. Robotic process automation (RPA) can be understood as a software bot used to automate the daily and repetitive processes performed by humans. Its applications include information technology (IT), human resource, and finance. Robotic process automation can successfully handle processes that have high volumes, are structured, and are repetitive. Robotic process automation differs from IT projects in the sense that it is a more business-centric and tailored solution. Process automation requires a mature process with a standard process in its built. Cognitive automation has emerged as an effective tool for optimizing and reconfiguring service delivery models in human-centric processes. It has also become a useful tool for decision-making, whereas RPA is used to enter, capture, create, and update the data. Purpose: This chapter explores RPA applications in data management. The remainder of this chapter emphasizes and explains the different RPA tools and technologies used for effective and efficient data management. This chapter also discusses use cases from the industry as practical application examples of RPA. Methodology: Robotic process automation is currently understood to be a very effective tool for managing numerous repetitive tasks in the aggregation and curation of data. Robotic process automation is generally applied to large repositories for tasks, such as data cleansing, normalization, data wrangling, and creation and updating of metadata, more efficiently and effectively. A systematic explanation of these concepts and how RPA can be used to effectively manage data can provide clear insights to numerous RPA users. Therefore, researchers have attempted to bridge this research gap. Outcomes: Robotic process automation is an effective technology that has a tremendous impact on industries and society, and it intends to cut short daily and repetitive tasks, thereby helping people to focus more on further productive occupations. The model proposed in this chapter will assist learners working with structured or unstructured data and handling RPA processes to increase their productivity, as it will not only provide essential concepts but also practical illustrations of the uses of RPA in data management. Originality/Value: Insightful knowledge on the use of RPA tools and techniques for data management, along with a discussion of use cases, is not readily available. We intend to provide the same, which can be used as a reference for professionals. Moreover, as we use an inquiry-based learning approach with ample illustrations for presentation, it would help readers understand the fundamentals of RPA applications in data management more easily. Industry use cases are also discussed for better understanding. <!----> <!----> <!----> <!---->

The study on artificial intelligence (AI)-centric applications and models for business systems indicates the potential for AI-based solutions and their hierarchies with the aid of business intelligence. Purpose: Businesses use AI-based applications and models to gain a competitive advantage, optimize prizes, enter new markets, establish new ventures, meet customer expectations, and solve supplier problems. These AI-centric models and applications currently in use by businesses are fundamental components of AI with limited learning capabilities, which are necessary to build more advanced AI applications and models, reduce resource gaps, and prevent data overload. Methodology: An AI-powered approach that combines business intelligence and data-driven techniques is used to create complex applications and models that can solve integrated issues faced by businesses, resulting in increased profitability, competitiveness, and cost-effectiveness. Outcomes: A systematic approach and model are developed to address business problems and provide suggested models or applications that promote reliable decision-making and problem-solving, ultimately fulfilling customer expectations. Originality/Value: This study analyzes and resolves the issue with the aid of AI, business intelligence, machine learning, use cases, algorithms, and flowcharts, which will serve as support systems in the decision-making process. <!----> <!----> <!----> <!---->

Digital healthcare represents the fusion of life sciences and technology, showcasing a remarkable surge in growth. The acceleration of digital healthcare is primarily propelled by the integration of cutting-edge technologies. The integration of cutting-edge technologies and frameworks such as artificial intelligence, machine learning, computer vision, robotics, internet of things, augmented reality and virtual reality, blockchain, and deep leaning frameworks has revolutionized the way healthcare services are delivered and managed. This chapter explores the roles, challenges, and use cases of cutting-edge technologies and modern deep learning frameworks within the digital healthcare landscape. Subsequently, the chapter delves into the convergence of these technologies and frameworks, with the overarching goal of advancing healthcare practices. This chapter explores the various applications of emerging technologies and modern deep learning frameworks, including personalized medicine, remote patient monitoring, predictive analytics, and secure health data management.