Intelligent Data-Driven Systems and Artificial Intelligence – serie

This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing."Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies"Emphasizes the impact of a data-driven supply chain on quality management"Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing"Highlights the barriers to implementing artificial intelligence in small and medium enterprisesPresents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risksThe book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book:Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:an exclusive section on essential preprocessing approaches for the data-driven modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book:Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

The text presents aspects of virtual worlds and highlights the emerging trends in simulation and modeling, comprising machine learning, artificial intelligence, deep learning, robotics, cloud computing, and data mining algorithms. It further discusses concepts including multimedia for the Internet of Things, graphical modeling using emerging technologies, and securing communication with secure data transmission in the modeling of virtual worlds.This book:Discusses secure data transmission in the modeling of virtual worlds in the Internet of Things environment.Covers the integration of concepts and technical know-how about multiple technologies in visual world modeling, system configurations, and hardware issues.Explores the use of next-generation technologies such as deep learning, blockchain, and artificial intelligence in visual world modeling scenarios.Presents architectures and system models for the Internet of Things based visual world modeling systems.Provides real-time case scenarios, highlighting emerging challenges and issues.The text is primarily written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer engineering, and information technology.

This book discusses the importance of using industrial intelligence in collaboration with computational intelligence in forming a smart system for diverse applications. It further illustrates the challenges and deployment issues in industrial resolution. The text highlights innovation and applications of computational agents and the industrial intelligence era to automate the requirements as per Industry 4.0.This book:Discusses computational agents for handling automation issues and the role of ethics in industrial resolutionPresents intelligence approaches for products, operations, systems, and servicesIllustrates the fundamentals of computational intelligence to forecast and analyze the requirements of society for automation as well as recent innovations and applicationsHighlights computation intelligence approaches in reducing human effort and automating the analysis of the production unitShowcases current innovation and applications of computational agents and industrial intelligence as per Industry 4.0The text is primarily written for senior undergraduate and graduate students, and academic researchers in diverse fields including electrical engineering, electronics, and communication engineering, industrial engineering, manufacturing engineering and computer science, and engineering.

This book comprehensively discusses the role of cloud computing in artificial intelligence‑based data‑driven systems and hybrid cloud computing for large data‑driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‑based frameworks and advanced computing techniques to deal with online/virtual systems.This book:• Covers the aspects of security, authentication, and prediction for data‑driven systems in heterogeneous environments.• Provides data‑driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‑making for real‑time problems.• Showcases deep learning‑based computer vision algorithms for enhanced pattern detection in different domains based on data‑centric approaches.• Examines the role of the Internet of Things and machine learning algorithms for data‑driven systems.• Highlights the applications of data‑driven systems and cloud computing in enhancing network performance.This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.

Zadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education.This book:Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets.Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality.It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.

The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities' concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic.This book:Provides a better understanding of artificial intelligence-based applications for future smart citiesPresents a detailed understanding of artificial intelligence tools for intelligent technologiesShowcases intelligent computing technologies in obtaining optimal solutions using artificial intelligenceDiscusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs)Covers machine learning-based Intrusion detection system (IDS) for smart gridIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing."Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies"Emphasizes the impact of a data-driven supply chain on quality management"Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing"Highlights the barriers to implementing artificial intelligence in small and medium enterprisesPresents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risksThe book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.

This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help.This book:Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector. Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management.Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care.Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression.The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

This book comprehensively discusses nature‑inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‑objective optimization under Fermatean hesitant fuzzy and uncertain environment.This book:Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problemPresents an overview of artificial intelligence (AI) and explainable AI decision‑making (XAIDM) and illustrates a data‑driven optimization concept for modeling environmental and economic sustainabilityDiscusses machine learning‑based multi‑objective optimization technique for load balancing in integrated fog‑cloud environmentExplains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goalsDiscusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‑estimation of functional regression operator, and intuitionistic fuzzy sets applicationsThe text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems. This book:Presents data-driven intelligent heuristics for improving and advancing environmental sustainability in both eco-cities and smart cities. Discusses various efficient control systems for distributed and hybrid renewable energy sources and enhance the scope of smart energy management systems for developing even intelligent systems. Showcases how distributed energy systems improve the data-driven robots in the Internet of Medical Things. Highlights practical approaches to optimize power generation, reduce costs through efficient energy, and reduce greenhouse gas emissions to the possible minimum. Covers bio-inspired swarm models, smart data-driven sensing to combat environmental issues, and futuristic data-driven enabled schemes in blockchain-fog-cloud assisted medical eny ecosystem.The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing.This book:Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond.Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data.Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks.Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security.Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence.It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

The book will help the readers in the field of the Internet of Things, and especially its convergence with artificial intelligence, which has given rise to the new paradigm of artificial intelligence of things (AIoT). It covers important concepts such as intelligent space and human-centered robotics and its effect on human wellbeing, and human-centered aviation automation.This book:Supports the advancement in artificial intelligence and the Internet of Things used in societal applications. Discusses the role of modeling human factors in designing smart systems as highlighted in Industry 4.0. Covers big data scheduling and the global standard method applied to smart maintenance. Presents human-centered aviation automation, human-centered processes, and decision support systems. Highlights the importance of data privacy and secure communication in society 5.0.The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:an exclusive section on essential preprocessing approaches for the data-driven modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

The book covers the role of emerging technologies such as blockchain technology, machine learning, IoT, cryptography, etc., in digital asset management. It further discusses digital asset management applications in different domains such as healthcare, travel industry, image processing, and our daily life activities to maintain privacy and confidentiality.This book:• Discusses techniques for securing and protecting digital assets in collaborative environments, where multiple organizations need access to the same resources.• Explores how artificial intelligence can be used to automate the management of digital assets, and how it can be used to improve security and privacy.• Explains the role of emerging technology such as blockchain technology for transforming conventional business models.• Highlights the importance of machine learning techniques in maintaining the privacy and security of data.• Covers encryption and decryption techniques, their advantages and role in improving the privacy of data.The text is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, information technology, and business management.

Zadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education.This book:Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets.Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality.It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.

To provide for a sustainable future, the potential synergies at the dynamic intersection of renewable energy (RE) incorporated with smart energy and artificial intelligence (AI) must be exploited. RE is crucial to preserve the environment. Energy involving various systems must be optimized and assessed to provide better performance. However, the design and development of RE systems remains a challenge. Advanced optimization techniques, AI, and machine learning (ML) plays a crucial role in implementing the latest innovative research in the field of renewable energy-integrated electrical systems. This book also describes the practical challenges encountered, and the solutions and future scope to be adopted. Applications of a variety of advanced optimization and AI techniques in the design and development of RE-integrated systems are discussed to provide new solutions in the RE domain.Key features:Discusses modern modeling/control approaches for improving renewable energy integrating artificial intelligence-driven power systemsDescribes the principles and methods of renewable energy generation technologies, and an analysis of their implementation, management, and optimization, and related economic advantagesPresents critical information on the technological design and policy issues that must be taken into considered while implementing a smart gridExplains of the metaheuristic optimization algorithm for complex electrical systems, and the whale optimization algorithm-based multi-objective hydrothermal schedulingCovers the electric vehicle charging station in the distribution network, and transient stability constraint optimal power flow problem using chaotic quasi-oppositional chemical reaction optimizationThe topics covered including microgrids, wind power, solar photo voltaic (PV), optimal power flow (OPF), grid connected inverter, electric vehicle, combined heat and power economic dispatch, FACTS tools for smart energy, harmonic impedance of a salient pole synchronous generator (HI), maximum power point tracking (MPPT) and advanced optimization techniques. Next Generation Artificial Intelligence-Driven Smart and Renewable Energy is ideal for academicians, practitioners, teachers, engineers, industry professionals, researchers, and students in diverse fields, including electrical engineering, electronics and communications engineering, energy, and environmental engineering.

This book discusses the importance of using industrial intelligence in collaboration with computational intelligence in forming a smart system for diverse applications. It further illustrates the challenges and deployment issues in industrial resolution. The text highlights innovation and applications of computational agents and the industrial intelligence era to automate the requirements as per Industry 4.0.This book:Discusses computational agents for handling automation issues and the role of ethics in industrial resolutionPresents intelligence approaches for products, operations, systems, and servicesIllustrates the fundamentals of computational intelligence to forecast and analyze the requirements of society for automation as well as recent innovations and applicationsHighlights computation intelligence approaches in reducing human effort and automating the analysis of the production unitShowcases current innovation and applications of computational agents and industrial intelligence as per Industry 4.0The text is primarily written for senior undergraduate and graduate students, and academic researchers in diverse fields including electrical engineering, electronics, and communication engineering, industrial engineering, manufacturing engineering and computer science, and engineering.

The text presents aspects of virtual worlds and highlights the emerging trends in simulation and modeling, comprising machine learning, artificial intelligence, deep learning, robotics, cloud computing, and data mining algorithms. It further discusses concepts including multimedia for the Internet of Things, graphical modeling using emerging technologies, and securing communication with secure data transmission in the modeling of virtual worlds.This book:Discusses secure data transmission in the modeling of virtual worlds in the Internet of Things environment.Covers the integration of concepts and technical know-how about multiple technologies in visual world modeling, system configurations, and hardware issues.Explores the use of next-generation technologies such as deep learning, blockchain, and artificial intelligence in visual world modeling scenarios.Presents architectures and system models for the Internet of Things based visual world modeling systems.Provides real-time case scenarios, highlighting emerging challenges and issues.The text is primarily written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer engineering, and information technology.

This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility.This book:Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computingPresents a comparative analysis of machine learning algorithms for power consumption predictionDiscusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environmentIllustrates a data-driven optimization concept for modeling environmental and economic sustainabilityExplains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goalsThe text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.

Fuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets. In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening. This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields. This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions. Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains. The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets. The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance.This bookpresents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft setsshowcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity indexcovers scientific evaluation of student academic performance using single-valued neutrosophic Markov chainillustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessmentexamines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching qualityWith its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.

Fuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets. In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening. This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields. This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions. Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains. The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets. The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance.This bookpresents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft setsshowcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity indexcovers scientific evaluation of student academic performance using single-valued neutrosophic Markov chainillustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessmentexamines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching qualityWith its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.

The aim of this book is to highlight the most promising lines of research, using new enabling technologies and methods based on AI/ML techniques to solve issues and challenges related to intelligent and computing systems. Intelligent computing easily collects data using smart technological applications like IoT-based wireless networks, digital healthcare, transportation, blockchain, 5.0 industry and deep learning for better decision making. AI enabled networks will be integrated in smart cities' concept for interconnectivity. Wireless networks will play an important role. The digital era of computational intelligence will change the dynamics and lifestyle of human beings. Future networks will be introduced with the help of AI technology to implement cognition in real-world applications. Cyber threats are dangerous to encode information from network. Therefore, AI-Intrusion detection systems need to be designed for identification of unwanted data traffic.This book:Provides a better understanding of artificial intelligence-based applications for future smart citiesPresents a detailed understanding of artificial intelligence tools for intelligent technologiesShowcases intelligent computing technologies in obtaining optimal solutions using artificial intelligenceDiscusses energy-efficient routing protocols using artificial intelligence for Flying ad-hoc networks (FANETs)Covers machine learning-based Intrusion detection system (IDS) for smart gridIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This book comprehensively discusses the role of cloud computing in artificial intelligence‑based data‑driven systems and hybrid cloud computing for large data‑driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‑based frameworks and advanced computing techniques to deal with online/virtual systems.This book:• Covers the aspects of security, authentication, and prediction for data‑driven systems in heterogeneous environments.• Provides data‑driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‑making for real‑time problems.• Showcases deep learning‑based computer vision algorithms for enhanced pattern detection in different domains based on data‑centric approaches.• Examines the role of the Internet of Things and machine learning algorithms for data‑driven systems.• Highlights the applications of data‑driven systems and cloud computing in enhancing network performance.This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.

Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing.This book:Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond.Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data.Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks.Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security.Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence.It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility.This book:Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computingPresents a comparative analysis of machine learning algorithms for power consumption predictionDiscusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environmentIllustrates a data-driven optimization concept for modeling environmental and economic sustainabilityExplains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goalsThe text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.

This book covers the security challenges of software-defined networks and proposes innovative solutions, such as the decentralization of security services and integration with blockchain technology, to enhance software-defined network security. It includes multiple case studies demonstrating the effectiveness of decentralized software-defined network security with three-layered architectures including all application programming interfaces. This book:• Offers in-depth analysis of software-defined network protocols including OpenFlow, Border Gateway Protocol-Link State, and Network Configuration Protocol (NETCONF). • Provides practical guidelines for implementing security measures within software-defined network environments, with a focus on technical aspects like firewall rules, intrusion detection, and access control policies. • Explores how blockchain can be integrated into software-defined networks to enhance security, providing step-by-step technical guidance for implementation. • Discusses supply chain transparency with integration of blockchain and software-defined networks. • Explains blockchain-enhanced network monitoring and control, and implementation of blockchain-secured network communication. It is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.