This edited book describes how newly emerging Artificial Intelligence (AI) technologies will provide unprecedented opportunities to penetrate technology and automation into everything we do, and at the same time, provide a huge playing field for businesses to develop newer models to capture market share. It establishes a milestone in understanding global transformational changes occurring in the manufacturing and corporate world due to AI and tries to find powerful and sophisticated solutions that will improve and streamline operations. Reinventing Manufacturing and Business Processes Through Artificial Intelligence will be of interest to students, researchers, and professionals of the AI community as well as interdisciplinary researchers.

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.

This book covers the need for Internet of Things (IoT) technologies and artificial intelligence (AI)–aided IoT solutions for business and production. It shows how IoT-based technology uses algorithms and AI models to bring out the desired results. AI-Aided IoT Technologies and Applications for Smart Business and Production shows how a variety of IoT technologies can be used toward integrating data fabric solutions and how intelligent applications can be used to greater effect in business and production operations. The book also covers the integration of IoT data-driven financial technology (fintech) applications to fulfill the goals of trusted AI-aided IoT solutions. Next, the authors show how IoT-based technology uses algorithms and AI models to bring out the desired results across various industries including smart cities, buildings, hospitals, hotels, homes, factories, agriculture, transportation, and more. The last part focuses on AI-aided IoT techniques, data analytics, and visualization tools. This book targets a mixed audience of specialists, analysts, engineers, scholars, researchers, academics, and professionals. It will be useful to engineering officers, IoT and AI engineers, engineering and industrial management students, and research scholars looking for new ideas, methodologies, technologies, models, frameworks, theories, and practices to resolve the challenging issues associated with leveraging IoT technologies, data-driven analytics, AI-aided models, IoT cybersecurity, 5G, sensors, and augmented and virtual reality techniques for developing smart systems in the era of Industrial Revolution 4.0.

This book discusses the basic principles of sustainable development in a smart city ecosystem to better serve the life of citizens. It examines smart city systems driven by emerging IoT-powered technologies and the other dependent platforms. Smart Cities: AI, IoT Technologies, Big Data Solutions, Cloud Platforms, and Cybersecurity Techniques discusses the design and implementation of the core components of the smart city ecosystem. The editors discuss the effective management and development of smart city infrastructures, starting with planning and integrating complex models and diverse frameworks into an ecosystem. Specifically the chapters examine the core infrastructure elements, including activities of the public and private services as well as innovative ICT solutions, computer vision, IoT technologies, data tools, cloud services, AR/VR technologies, cybersecurity techniques, treatment solution of the environmental water pollution, and other intelligent devices for supporting sustainable living in the smart environment. The chapters also discuss machine vision models and implementation as well as real-time robotic applications. Upon reading the book, users will be able to handle the challenges and improvements of security for smart systems, and will have the know-how to analyze and visualize data using big data tools and visualization applications. The book will provide the technologies, solutions as well as designs of smart cities with advanced tools and techniques for students, researchers, engineers, and academics.

This book brings insight to the HR management system and offers data-centric approaches and AI-enabled applications for the design and implementation strategies used for workforce development and management. Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI-Enabled Approaches focuses on the mechanisms of proposing solutions along with architectural concepts, design principles, smart solutions, and intelligent predictions with visualization simulation. Data visualization for the metrics of management systems and robotic process automation applications and tools are also offered. This book is also useful as a reference for those involved in AI-enabled applications, data analytics, data visualization, as well as systems engineering and systems designing.

The book offers insight into the healthcare system by exploring emerging technologies and AI-based applications and implementation strategies. It includes current developments for future directions as well as covering the concept of the healthcare system along with its ecosystem. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem focuses on the mechanisms of proposing and incorporating solutions along with architectural concepts, design principles, smart solutions, decision-making process, and intelligent predictions. It offers state-of-the-art approaches for overall innovations, developments, and implementation of the smart healthcare ecosystem and highlights medical signal and image processing algorithms, healthcare-based computer vision systems, and discusses explainable AI (XAI) techniques for healthcare. This book will be useful to researchers involved in AI, IoT, Data, and emerging technologies in the medical industry. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

In the digital-driven economy era, an AI-oriented competency framework (AIoCF) is a collection to identify AI-oriented knowledge, attributes, efforts, skills, and experiences (AKASE) that directly and positively affect the success of employees and the organization. The application of skills-based competency analytics and AI-equipped systems is gradually becoming accepted by business and production organizations as an effective tool for automating several managerial activities consistently and efficiently in developing and moving the capacity of a company up to a world-class level. AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation discusses all the points of an AIoCF, which includes predictive analytics, advisory services, predictive maintenance, and automated processes, which help to make the operations of project management, personnel management, or administration more efficient, profitable, and safe. The book includes the functionality of emerging career pathways, hybrid learning models, and learning paths related to the learning and development of employees in the production or delivery fields. It also presents the relationship between skills taxonomy and competency framework with interactive methods using datasets, processing workflow diagrams, and architectural diagrams for easy understanding of the application of intelligent functions in role-based competency systems. By also covering upcoming areas of AI and data science in many government and private organizations, the book not only focuses on managing big data and cloud resources of the talent management system but also provides cybersecurity techniques to ensure that systems and employee competency data are secure. This book targets a mixed audience of students, engineers, scholars, researchers, academics, and professionals who are learning, researching, and working in the field of workforce training, human resources, talent management systems, requirement, headhunting, outsourcing, and manpower consultant services from different cultures and industries in the era of digital economy.