Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

This book addresses the problem of waste management by using multi-criteria decision-making (MCDM) methods. The authors discuss how to apply MCDM, a complex decision-making tool that involves both quantitative and qualitative factors, to develop strategies for effective waste management using various optimization models to rank alternatives, while also incorporating the concerns and needs of multiple stakeholders to find the most optimal decisions for various types of wastes. Typically, there does not exist a single optimal solution to waste problems; with help of MCDM, far better solutions can often be found and utilized to facilitate sustainable waste management techniques in various industries. This book provides unique, effective, and quick decision-making strategies for waste management. With the ever-increasing population and continuing human development, the problem of managing waste becomes increasingly essential, and this volume helps lead the way to finding sustainable solutions.

Provides real-life reliability studies on industrial operations along with solutions Discusses modelling and optimization of reliability and safety aspects in industry Covers reliability and maintenance issues in process industries Presents cost optimization and life-cycle costing analysis Offers MCDM application for risk and Safety analysis

This new volume offers a variety of perspectives from investigators, industry professionals, stakeholders, and economic strategists that look at new ways of solving optimization problems related to different industrial sectors. Case studies relay how optimization methods deal with both real operative conditions in process industries and in service industries. The volume also explores emerging research areas toward the implementation of optimization algorithms for enhancement of system performance as well as system effectiveness. The book explores the role of optimization methods in engineering applications in industrial and mechanical engineering as well as in the fields of healthcare/medicine, food production, oil, textiles, energy, and agriculture. The volume offers new ways of solving optimization problems related to different industrial sectors, incorporating mathematical formulation for particular design problems and thus aiding the selection of the optimal design among many alternatives. It shows optimization methods that deal with actual operative conditions both in process and in service industries. A unique advantage of this volume is its wide range of topics in different engineering domains using novel mathematical modeling-based optimization methods for solving the real-life problems. The array of examples and case studies of the effective use of optimization in diverse areas of engineering include healthcare analysis and monitoring (fetal phonocardiography), medical device design (3D printing design for protheses), agriculture/farming (monitoring climate conditions), environmental science (waste management), automotive and aeronautic design, industrial manufacturing, solar energy, and more. Key features: Presents case studies on optimization problems related to industry Discusses case studies on operations management practices optimization Provides an overview of design optimization Highlights case studies on process optimization Assesses different techniques for handling engineering problems This valuable book will be useful for researchers, scientists, faculty, and students involved or interested in the field of optimization engineering in industrial design.

This volume helps to address the genuine 21st century need for advances in data science and computing technology. It provides an abundance of new research and studies on progressive and innovative technologies, including artificial intelligence, communication systems, cyber security applications, data analytics, Internet of Things (IoT), machine learning, power systems, VLSI, embedded systems, and much more. The book presents a variety of interesting and important aspects of data science and computing technologies and methodologies in a wide range of applications, including deep learning, DNA cryptography, classy fuzzy MPPT controller, driving assistance, and safety systems. Novel algorithms and their applications for solving cutting-edge computational and data science problems are included also for an interdisciplinary research perspective. The book addresses recent applications of deep learning and ANN paradigms, the role and impact of big data in the e-commerce and retail sectors, algorithms for load balancing in cloud computing, advances in embedded system based applications, optimization techniques using a MATLAB platform, and techniques for improving information and network security. Advances in Data Science and Computing Technology: Methodology and Applications provides a wealth of valuable information and food for thought on many important issues for data scientists and researchers, industry professionals, and faculty and students in the data and computing sciences.

Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.

As transactions and other business functions move online and grow more popular every year, the finance and banking industries face increasingly complex data management and identity theft and fraud issues. AI can bring many financial and business functions to the next level, as systems using deep learning technologies are able to analyze patterns and spot suspicious behavior and potential fraud. In this volume, the focus is on the application of artificial intelligence in finance, business, and related areas. The book presents a selection of chapters presenting cutting-edge research on current business practices in finance and management. Topics cover the use of AI in e-commerce systems, financial services, fraud prevention, identifying loan-eligible customers, online business, Facebook social commerce, insurance industry, online marketing, and more.

Provides a comprehensive guide about how to use machine vision for Industry 4.0 applications like analysis of images for automated inspections, object detection, object tracking etc. Includes case studies of Robotics Internet of Things with its current and future applications in Healthcare, Agriculture, Transportation, etc. It highlights the inclusion of impaired people in industry, like intelligent assistant that helps deaf-mute people to transmit instructions and warnings in a manufacturing process. It examines the significant technological advancements in machine vision for industrial Internet of things and explores the commercial benefits using the real world applications from healthcare to transportation. Provides a conceptual framework of Machine vision for the various Industrial applications. Addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and post-graduate students, researchers, and anyone else interested in the trends, development, and opportunities for the Machine Vision for Industry 4.0 applications.

This book addresses the problem of waste management by using multi-criteria decision-making (MCDM) methods. The authors discuss how to apply MCDM, a complex decision-making tool that involves both quantitative and qualitative factors, to develop strategies for effective waste management using various optimization models to rank alternatives, while also incorporating the concerns and needs of multiple stakeholders to find the most optimal decisions for various types of wastes. Typically, there does not exist a single optimal solution to waste problems; with help of MCDM, far better solutions can often be found and utilized to facilitate sustainable waste management techniques in various industries. This book provides unique, effective, and quick decision-making strategies for waste management. With the ever-increasing population and continuing human development, the problem of managing waste becomes increasingly essential, and this volume helps lead the way to finding sustainable solutions.

Blockchain is new-age technology used to track every transaction using cryptocurrency across servers linked in a peer-to-peer network, enabling transactions to be secure, transparent and reliable. Retaining an efficient, secure and patient-centric healthcare industry has never been so important, especially due to the damaging effects of the Covid-19 pandemic. The applicability of Blockchain in the healthcare domain can be seen as a remarkable opportunity for researchers and scientists to solve real-world problems. This book focuses on the fundamentals of Blockchain technology along with the methods of its integration with the healthcare industry. It also provides an enhanced understanding of Blockchain technology, AI and IoT across the various application areas of the healthcare industry. Furthermore, throughout the book, areas of relevant applications, such as patient data privacy protection, pharmaceutical supply chains and genomics are discussed.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

This book explores the significance, challenges and benefits of digital twin technologies; it focuses in particular on various architectures, applications and challenges in the implementation of digital twins to Machine Learning and Internet of Things capabilities. Through the analysis of smart city and smart manufacturing case studies, the book explores the benefits of digital technologies in the Industry 4.0 Era.

This book covers the emerging applications of different computational and optimization techniques in order to achieve a sustainable agriculture. A sustainable agricultural management requires tools in providing integrated, area-specifi c, and interpreted prediction or forecasting and guidance in every aspect in agriculture.

Given the increasing need to optimize resources sustainably, decision-makers face challenges in analyzing and considering the numerous factors involved. This book makes an effort to present and concentrate on the challenges in decision-making processes for green and sustainable engineering. Through a collection of case studies such as evaluation of waste assessment and drainage system, sustainable building assessment, renewable energy selection, materials and manufacturing process optimization, and crop pattern influence in environmental and economic conditions, readers can learn how to apply cutting-edge Multiple-Criteria Decision Making (MCDM) methods in addressing complexities involved in the decision-making process.

Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.

HYBRID MICROMACHINING and MICROFABRICATION TECHNOLOGIES The book aims to provide a thorough understanding of numerous advanced hybrid micromachining and microfabrication techniques as well as future directions, providing researchers and engineers who work in hybrid micromachining with a much-appreciated orientation. The book is dedicated to advanced hybrid micromachining and microfabrication technologies by detailing principals, techniques, processes, conditions, research advances, research challenges, and opportunities for various types of advanced hybrid micromachining and microfabrication. It discusses the mechanisms of material removal supported by experimental validation. Constructional features of hybrid micromachining setup suitable for industrial micromachining applications are explained. Separate chapters are devoted to different advanced hybrid micromachining and microfabrication to design and development of micro-tools, which is one of the most vital components in advanced hybrid micromachining, and which can also be used for various micro and nano applications. Power supply, and other major factors which influence advanced hybrid micromachining processes, are covered and research findings concerning the improvement of machining accuracy and efficiency are reported.