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This book pioneers the synergy between state-of-the-art edge computing technologies and the power of operations research. It comprehensively explores real-world applications, demonstrating how various operations'research techniques enhance edge computing's efficiency, reliability and resource allocation. Innovative solutions for dynamic task scheduling, load balancing and data management, all tailored to the unique challenges of edge environments, are displayed. Starting with operation research methodologies with foundations, applications and research challenges in edge computing and an overview of digital education, this book continues with an exploration of applications in the health sector using IoT, intelligent payment procedures and performance measurement of edge computing, using edge computing and operation research. Smart or AI-based applications are also explored further on and the book ends with insight into ultralightweight and security protocols with solutions for IoT using blockchain.

Bridging the gap between the need for micro elements and the profitable microfabrication of goods, this new book provides an informative overview of the electro-micromachining and microfabrication processes, varieties, and important applications. Opening with an overview of a variety of micromachining technologies, with an emphasis on nontraditional approaches and recent advances in each, the volume discusses the ultrasonic micromachining processes for producing a variety of micro-shapes, such as micro-holes, micro-slots, and micro-walls, as well as assisted hybrid micromachining with ultrasonic vibration of the tool or workpiece, all which help to improve precision and to advance research. Computer-aided design and computer-aided manufacturing dental micromachining technologies are discussed. Micro-electrical discharge machining, laser micro grooving, and laser micromachining are among the advanced micro-manufacturing processes addressed as well. The volume also covers the use of an electrochemical micromachining method to improve micro texturing and the use of nano-additives to enhance MQL and micromachining process optimization.

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.

COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERING The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering. This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations. Audience Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.

This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022). This book emphasizes applications of computational intelligence including machine intelligence, data analytics, and optimization algorithms for solving fundamental and advanced engineering and management problems. This book serves as a valuable resource for researchers, industry professionals, academicians, and doctoral scholars in engineering, production, thermal, materials, design, computer engineering, natural sciences, and management who work on computational intelligence. The book also serves researchers who are willing to use computational intelligence algorithms in real-time applications.

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.

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.

Multi-criteria decision-making (MCDM) has gained vast popularity for its ability to help make decisions in the presence of various similar and conflicting choices.This new volume applies the MCDM theory to solving problems and challenges in manufacturing environments. It discusses using MCDM computational methods to evaluate and select the most optimal solution or method for real-world, real-time manufacutring engineering issues. It details the decision-making process in relation materials selection; identification, assessment, and evaluation of risk; sustainability assessment; selection of green suppliers; and more.The chapter authors demonstrate the application of myriad MCDM techniques in decision-making, including MADM (multiple attribute decision-making), DEA (data envelopment analysis), fuzzy TOPSIS (technique for order preference by similarities to ideal solution), fuzzy-VIKOR (multicriteria optimization and compromise solution); MOORA (multi-objective optimization on the basis of ratio analysis), EWM (entropy weight method), (AHP) analytic hierarchy process, TODIM (TOmada de Decisao Interativa Multicriterio), and others. The volume illustrates these MCDM models in several industries and industrial processes, including for experimental analysis and optimization of drilling of glass fiber reinforced plastic, in the textile industries, for selection of refrigerants for domestic applications, and others.

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 addresses the reliability, risk, and safety issues of real industrial systems with application of the latest reliability and risk-based modeling. Related topics such as maintenance decision-making and risk and safety modeling are also addressed with the implementation of decision-making techniques. The book provides real-life studies on industrial operations along with solutions. It discusses modeling and optimization of reliability and safety aspects in industry and covers reliability maintenance issues in process industries. The book goes on to present cost optimization, life cycle costing analysis, and multi-criteria decision making (MCDM) application for risk and safety analysis. Academic institutions, students, professionals, large companies involved in engineering sciences, research scholars, and investigators working in the domain of Reliability and Safety Engineering and its allied domains will find this book useful.

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.

FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

This book introduces the step-by-step processes involved in using MCDM methods, starting from problem formulation, model development, and criteria weighting to the final ranking of the alternatives. The authors explain the different MCDM methods that can be used in specific manufacturing environments. The book explains the conceptual frameworks of how these methods are applied with special focus on their applicability and usefulness. The authors begin with an introduction to multi-criteria decision-making, followed by explanations of 29 MCDM methods and their applications. The final sections of the book describe helpful normalization techniques and criteria weight measurement techniques. The collection of diverse range of manufacturing applications and case studies presented here will aid readers in applying cutting-edge MCDM methods to their own manufacturing projects. As both a research and teaching tool, this book encourages critical and logical thinking when applying MCDM methods for solving complex manufacturing decision-making problems.

OPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; An in-depth treatment of contributions to optimal learning and optimizing engineering systems; Maps out the relations between optimization and other mathematical topics and disciplines; A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Audience Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations. The book redefines the computational intelligence foundations, the three pillars - neural networks, evolutionary computation, and fuzzy systems. It also discusses emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, and chaotic systems. The other areas have also been demystified in the book to strengthen the range of computational intelligence techniques such as expert systems, knowledge-based systems, and genetic algorithms. Therefore, this book will redefine the role of computational intelligence techniques in modern business system operations such as marketing, finance & accounts, operations, personnel management, supply chain management, and logistics. Besides, this book guides the readers through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone in various business system operations. This book reveals how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. The book will provide insights into research gaps, open challenges, and unsolved computational intelligence problems. The book will act as a premier reference and instant material for all the users who are contributing/practicing the adaptation of computational intelligence modern techniques in business systems.

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. Indexed in SCOPUS.

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.

INTELLIGENT DECISION SUPPORT SYSTEMS FOR SMART CITY APPLICATIONS This book provides smart city frameworks to address new difficulties by adding new features and allowing the city environment to react to collected data and information to increase the efficiency and sustainability of services for inhabitants. Making a smart city is an emerging strategy to mitigate the problems generated by urban population growth and rapid urbanization. This book aims to provide a better understanding of the concept of smart cities and the application of an intelligent decision support system. Based on the analysis of existing information there are eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. This book will focus on the application of the decision support system in managing these eight crucial aspects of smart cities. The intent in writing this book was also to provide a source that covers the stage-by-stage integration of the four key areas involving planning, physical infrastructure, ICT infrastructure, and deploying the smart solutions necessary for city transformation. With this as the motivation, “Decision Support Systems for Smart City Applications” provides the application of an intelligent decision support system for effectively and efficiently managing the transformation process, which can aid various supply chain stakeholders, academic researchers, and related professionals in building smart cities. Various chapters of this book are expected to support practicing managers during the implementation of smart solutions for city transformation. Audience This book is aimed at both academics and practitioners alike in the fields of intelligent computing, decision support systems, the manufacturing industry, supply chain managers, stakeholders, policymakers, and other technical and administrative personnel.

Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.

This book addresses the different problems, practices, challenges and opportunities in sustainable resource management with the help of decision-making techniques to showcase the relevance of computational modelling approaches in sustainable management and Industry 4.0. It aims to address the inherent complexity of managing ecosystems, particularly with respect to involvement of multi-stakeholders, lack of information and uncertainties. Critical analyses are made to point out the need for, and propose a call to, a new way of thinking about sustainable resource management. This book will be useful for academicians, researchers, and industrialists in the field of industrial and production engineering.

This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications. Industry 4.0, the Fourth Industrial Revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in the industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance. Features Presents a comprehensive guide on how to use machine vision for Industry 4.0 applications, such as analysis of images for automated inspections, object detection, object tracking, and more Includes case studies of Robotics Internet of Things with its current and future applications in healthcare, agriculture, and transportation Highlights the inclusion of impaired people in the industry, for example, an intelligent assistant that helps deaf-mute individuals to transmit instructions and warnings in a manufacturing process Examines the significant technological advancements in machine vision for Industrial Internet of Things and explores the commercial benefits using real-world applications from healthcare to transportation Discusses a conceptual framework of machine vision for various industrial applications The book addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone interested in the trends, development, and opportunities for machine vision for Industry 4.0 applications.

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.

The world is experiencing an unprecedented period of change and growth through all the electronic and technilogical developments and everyone on the planet has been impacted. What was once ‘science fiction', today it is a reality. This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail. Each chapter focuses on a different aspect - Machine Vision, Pattern Analysis and Image Processing - Advanced Trends in Computational Intelligence and Data Analytics - Futuristic Communication Technologies - Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

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.

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.