Om Prakash Jena – författare

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:

Green Engineering and Technology: Innovations, Design, and Architectural Implementation serves as a useful and practical guide for practicing engineers, researchers, and students alike.

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:

Green Engineering and Technology: Innovations, Design, and Architectural Implementation serves as a useful and practical guide for practicing engineers, researchers, and students alike.

The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications.

FEATURES

This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications.

FEATURES

This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.

This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems.

Features:

This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.

Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.

Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.

Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.

This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems.

Features:

This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.

Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.

Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India.

Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

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.

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.

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

This text focuses 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.

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

This text focuses 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.

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.

Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.

This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.

Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.

This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments.

This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis.

Audience

The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.