The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare problems. It will serve as an ideal reference text for graduate students and academic researchers in diverse fields of engineering including electrical, electronics and communication, computer, and biomedical. Present explainable artificial intelligence (XAI) based machine analytics and deep learning in medical science. Discusses explainable artificial intelligence (XA)I with the Internet of Medical Things (IoMT) for healthcare applications. Covers algorithms, tools, and frameworks for explainable artificial intelligence on medical data. Explores the concepts of natural language processing and explainable artificial intelligence (XAI) on medical data processing. Discusses machine learning and deep learning scalability models in healthcare systems. This text focusses on data driven analysis and processing of advanced methods and techniques with the help of explainable artificial intelligence (XAI) algorithms. It covers machine learning, Internet of Things (IoT), and deep learning algorithms based on XAI techniques for medical data analysis and processing. The text will present different dimensions of XAI based computational intelligence applications. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities. Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers. Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing Offers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformation Discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure Covers the effects that the 4th Industrial Revolution has on industrial infrastructures Looks at industry change patterns and innovations that are speeding up industrial transformation activities Om Prakash Jena is currently working as an associate professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Sabyasachi Pramanik is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. Ahmed A. Elngar is an associate professor in the Faculty of Computers & Artificial Intelligence, Beni-Suef University, Egypt. He is also an associate professor in the College of Computer Information Technology, chair of the Scientific Innovation Research Group (SIRG), and director of the Technological and Informatics Studies Center (TISC), American University in the Emirates, United Arab Emirates.

Covers the fundamentals of Machine Learning and Deep Learning in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in Machine Learning/Deep Learning models Integrates several aspects of AI-based Computational Intelligence like Machine Learning and Deep Learning from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphazises feature selection as an important step in any accurate model simulation, ML/DL methods are used to help train the system and extract the positive solution implicitly

Escalating urbanization and energy consumption have increased the demand for green engineering solutions and intelligent systems to mitigate environmental hazards and offer a more sustainable future. Green engineering technologies help to create sustainable, eco-friendly designs and solutions with the aid of updated tools, methods, designs, and innovations. These technologies play a significant role in optimizing sustainability in various areas of energy, agriculture, waste management, and bioremediation and include green computing and artificial intelligence (AI) applications. Green Engineering and Technology: Innovations, Design, and Architectural Implementation examines the most recent advancements in green technology, across multiple industries, and outlines the opportunities of emerging and future innovations, as well as practical real-world implementation. Features: Provides different models capable of fulfilling the criteria of energy efficiency, health and safety, renewable resources, and more Examines recycling, waste management, and bioremediation techniques as well as waste-to-energy technologies Presents business cases for adopting green technologies including electronics, manufacturing, and infrastructure projects Reviews green technologies for applications such as energy production, building construction, transportation, and industrialization Green Engineering and Technology: Innovations, Design, and Architectural Implementation serves as a useful and practical guide for practicing engineers, researchers, and students alike.

Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real world biomedical and healthcare problems Provides guidance in developing intelligence based diagnostic systems, efficient models and cost effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real test-beds, and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigatse risk in healthcare with advanced analytics

Discusses the industry transformation aligned with computerization of manufacturing and the concept of required skills needed to build the new workforce Covers the role of Artificial Intelligence in the management of resource flow and decision-making in the industrial transformation of operations and supply chain management Presents sustainability efficiency for industrial transformation with IoT, machine learning, data analysis and blockchain technologies Focuses on industrial development, design, implementation, and transformation using technologies such as Big Data Analytics, Blockchain among others Showcases the incorporation of complex processes and functions as one central component of digital systems Explores current trends that are accelerating the industrial transformation and influencing the transition of the fourth industrial revolution related to industrial infrastructure, biodiversity, and production enhancement