Future Generation Information Systems - Böcker

This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.• Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things.• Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe.• Explores the importance of blockchain technology in the Internet of Things security issues.• Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy.• Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications.This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It furtherdiscusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics. It presents practical scenarios from the industry for the application of the internet of things in various domains. Next Generation Communication Networks for Industrial Internet of Things Systems presents concepts and research challenges in communication networking for Industrial internet of things systems. Features: Discusses process monitoring, environmental monitoring, control, and maintenance monitoring. Covers data collection and communication protocols in a comprehensive manner. Highlights the internet of things industrial applications, and industrial revolution 4.0. Presents 5G-enabled internet of things technology and architecture. Showcases artificial intelligence techniques in the IoT networks. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas of electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

In the past decades, cyber-physical systems (CPSs) have been widely applied to fields such as smart grids, environment monitoring, aerospace, smart transportation, and industrial automation. Great strides have been made in CPSs to improve the computing mechanism, communication, and quality of service by applying optimization algorithms. Currently, these efforts are integrated with the applications of machine learning (ML) and artificial intelligence (AI). To maintain system reliability and stability, CPSs such as smart grids face numerous challenges, including large-scale Internet-of-Things (IoT) device adaptation, ever-increasing demands of electrical energy, and the rise of a wide range of security threats. These challenges bring forth the need to find sustainable and advanced solutions to guarantee reliable and secure operations in these systems.The goal of this book is to foster transformative, multidisciplinary, and novel approaches that ensure CPS security by taking into consideration the unique security challenges present in the environment. This book attracts contributions in all aspects pertaining to this multidisciplinary paradigm, which includes the development and implementation of Smart CPS, Supervisory Control and Data Acquisition (SCADA) systems, CPS for Industry 4.0, CPS architecture for IoT applications, and CPS forensics.This book:Discusses concepts including wireless sensor networks (WSNs), CPSs, and the IoT in a comprehensive manner.Covers routing protocols in sensor networks, attacks, and vulnerabilities in WSNs, the Internet of Cyber-Physical Things, and CPSs for industrial applications.Highlights technological advances, practical solutions, emerging trends, and prototypes related to privacy in CPSs and the IoT.Presents a pathway and architecture for proactive security schemes in CPSs to counter vulnerabilities, including phishing attacks, malware injection, internal stealing of data, and hacking.Discusses the most recent research and development on the enabling technologies for IoT-based CPSs.Owing to the scope and diversity of topics covered, the book will be of interest not only to researchers and theorists but also to professionals, material developers, technology specialists, and methodologists dealing with the multifarious aspects of data privacy and security enhancement in CPSs. The book will provide these professionals an overview of CPS security and privacy design, as well as enlighten them to promising solutions to research problems such as cyberattacks in CPS, risk identification and management in CPS, ML-based trust computational models for CPSs, nature-inspired algorithms for CPSs, and distributed consensus algorithms for event detection in CPSs. The secondary target audience of this book includes legal practitioners, hackers, cyber law policymakers, cyber forensic analysts, and global security consortiums who may use it to further their research exposure to pertinent topics in cybersecurity.

This book covers issues associated with smart systems due to the presenceof onboard nonlinear components. It discusses the advanced architectureof smart systems for power management units. It explores issues of powermanagement and identifies hazardous signals in the power managementunits of smart devices. It• Presents adaptive artificial intelligence and machine learning-basedcontrol strategies.• Discusses advanced simulations and data synthesis for various powermanagement issues.• Showcases solutions to the uncertainty and reliability issues in powermanagement units.• Identifies new power quality challenges in smart devices.• Explains hybrid active power filters, shunt hybrid active power filters,and the industrial internet of things in power quality management.This book comprehensively discusses advancements of traditional electricalgrids, the benefits of smart grids to customers and stakeholders, propertiesof smart grids, smart grid architecture, smart grid communication, andsmart grid security. It further covers the architecture of advance power managementunits (PMU) of smart devices, and the identification of harmonicdistortions with respect to various sensor-based technology. It will serve asan ideal reference text for senior undergraduate and graduate students, andacademic researchers in fields including electrical engineering, electronics,communications engineering, and computer engineering.

This book presents Internet of Things (IoT) technology and security-related solutions that employ intelligent data processing technologies and machine learning (ML) approaches for data analytics. It presents practical scenarios from the industry for the application of the internet of things in various domains. Next Generation Communication Networks for Industrial Internet of Things Systems presents concepts and research challenges in communication networking for Industrial internet of things systems. Features: Discusses process monitoring, environmental monitoring, control, and maintenance monitoring. Covers data collection and communication protocols in a comprehensive manner. Highlights the internet of things industrial applications, and industrial revolution 4.0. Presents 5G-enabled internet of things technology and architecture. Showcases artificial intelligence techniques in the IoT networks. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas of electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

This book covers advances and applications of smart technologies including the Internet of Things (IoT), artificial intelligence, and deep learning in areas such as manufacturing, production, renewable energy, and healthcare. It also covers wearable and implantable biomedical devices for healthcare monitoring, smart surveillance, and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.• Covers concepts, theories, and applications of artificial intelligence and deep learning, from the perspective of the Internet of Things.• Discusses powers predictive analysis, predictive maintenance, and automated processes for making manufacturing plants more efficient, profitable, and safe.• Explores the importance of blockchain technology in the Internet of Things security issues.• Discusses key deep learning concepts including trust management, identity management, security threats, access control, and privacy.• Showcases the importance of intelligent algorithms for cloud-based Internet of Things applications.This text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the Internet of Things. It furtherdiscusses applications of smart technologies in diverse sectors such as agriculture, smart home, production, manufacturing, transport, and healthcare.

This book covers issues associated with smart systems due to the presenceof onboard nonlinear components. It discusses the advanced architectureof smart systems for power management units. It explores issues of powermanagement and identifies hazardous signals in the power managementunits of smart devices. It• Presents adaptive artificial intelligence and machine learning-basedcontrol strategies.• Discusses advanced simulations and data synthesis for various powermanagement issues.• Showcases solutions to the uncertainty and reliability issues in powermanagement units.• Identifies new power quality challenges in smart devices.• Explains hybrid active power filters, shunt hybrid active power filters,and the industrial internet of things in power quality management.This book comprehensively discusses advancements of traditional electricalgrids, the benefits of smart grids to customers and stakeholders, propertiesof smart grids, smart grid architecture, smart grid communication, andsmart grid security. It further covers the architecture of advance power managementunits (PMU) of smart devices, and the identification of harmonicdistortions with respect to various sensor-based technology. It will serve asan ideal reference text for senior undergraduate and graduate students, andacademic researchers in fields including electrical engineering, electronics,communications engineering, and computer engineering.

This reference text addresses the importance of smart crop management for increasing yield and presents a framework for smart monitoring and regulation of crop observation. Further, it comprehensively covers important topics such as spatial decision support systems for precision farming, swarm intelligence in the optimal management of aquaculture farms, and intelligent harvesting algorithms for improving productivity.This book:• Presents meta-heuristic algorithms for optimization, economic crop planning, and use of effective water resource management.• Discusses spatial decision support systems for crop productivity management, watershed management, and precision farming.• Illustrates swarm intelligence-based optimization techniques, data mining, and machine learning methods for aquaculture operations.• Highlights artificial intelligence and machine learning-based harvesting algorithms for improving productivity.• Explains the use of green Internet of Things security solutions for agriculture, plant condition management, and greenhouse simulation.It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

The text comprehensively focusses on the use of artificial intelligence and optimization techniques for creating smart information systems.Focuses on extracting information from blockchain repository using artificial intelligence and machine learning algorithmsPresents deep learning models to identify and locate objects within images and videos, making it possible for machines to perform tasks such as self-driving cars, surveillance, and roboticsDiscusses artificial intelligence and optimization techniques for geographic information system (GIS) generation such as spatial data processingCovers artificial intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, and linear regressionIllustrates topics such as image recognition, natural language processing, fraud detection, information system security, and intrusion detection system

This book focuses on unearthing nuances of leveraging integrative and cohesive technologies in the Industry 5.0 paradigm for solving problems of business and social enterprises, which need not only be competitive but also sustain value creation for people at large.Highlights the technological evolution and its benefits to industry and societyShowcases the push for industrial digitization and focuses on innovative approaches toward the human operational resilience and true sustainability objectives of Industry 5.0Covers manufacturing, financial, marketing, and human resource techniques to resuscitate enterprises that would boost their acceptance among policymakers of multiple sectorsDiscusses topics such as additive manufacturing, smart manufacturing and intelligence, smart logistics, and resilient manufacturing networksPresents case studies on smart manufacturing networks, smart cities, sustainable urban development, and health monitoring systemsThe text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of industrial engineering, manufacturing engineering, production engineering, business management, mechanical engineering, and engineering management.

The text comprehensively discusses the representation of visual data and design principles of interactive and dynamic dashboards. It further covers the theoretical concept of inference and machine learning algorithms for making the concepts clear to the reader. The book illustrates important topics such as data testing a parametric hypothesis, data testing a non-parametric hypothesis, exploratory data analysis, outlier detection and interpretation.This book: Covers various data analysis tools such as KNIME, RapidMiner, Rstudio, Grafana, and Redash Discusses the theoretical concept of inference and machine learning algorithms for designing dynamic dashboards Presents statistical modelling techniques with an emphasis on pattern mining, and pattern relationships Explains the problem of efficient retrieval of similar time series in large databases to enrich the knowledge of the readers to effectively handle various real-time datasets Illustrates dimensionality reduction techniques such as principal component analysis, linear discriminant analysis, singular value decomposition, and piecewise vector quantized approximationIt is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

In the past decades, cyber-physical systems (CPSs) have been widely applied to fields such as smart grids, environment monitoring, aerospace, smart transportation, and industrial automation. Great strides have been made in CPSs to improve the computing mechanism, communication, and quality of service by applying optimization algorithms. Currently, these efforts are integrated with the applications of machine learning (ML) and artificial intelligence (AI). To maintain system reliability and stability, CPSs such as smart grids face numerous challenges, including large-scale Internet-of-Things (IoT) device adaptation, ever-increasing demands of electrical energy, and the rise of a wide range of security threats. These challenges bring forth the need to find sustainable and advanced solutions to guarantee reliable and secure operations in these systems.The goal of this book is to foster transformative, multidisciplinary, and novel approaches that ensure CPS security by taking into consideration the unique security challenges present in the environment. This book attracts contributions in all aspects pertaining to this multidisciplinary paradigm, which includes the development and implementation of Smart CPS, Supervisory Control and Data Acquisition (SCADA) systems, CPS for Industry 4.0, CPS architecture for IoT applications, and CPS forensics.This book:Discusses concepts including wireless sensor networks (WSNs), CPSs, and the IoT in a comprehensive manner.Covers routing protocols in sensor networks, attacks, and vulnerabilities in WSNs, the Internet of Cyber-Physical Things, and CPSs for industrial applications.Highlights technological advances, practical solutions, emerging trends, and prototypes related to privacy in CPSs and the IoT.Presents a pathway and architecture for proactive security schemes in CPSs to counter vulnerabilities, including phishing attacks, malware injection, internal stealing of data, and hacking.Discusses the most recent research and development on the enabling technologies for IoT-based CPSs.Owing to the scope and diversity of topics covered, the book will be of interest not only to researchers and theorists but also to professionals, material developers, technology specialists, and methodologists dealing with the multifarious aspects of data privacy and security enhancement in CPSs. The book will provide these professionals an overview of CPS security and privacy design, as well as enlighten them to promising solutions to research problems such as cyberattacks in CPS, risk identification and management in CPS, ML-based trust computational models for CPSs, nature-inspired algorithms for CPSs, and distributed consensus algorithms for event detection in CPSs. The secondary target audience of this book includes legal practitioners, hackers, cyber law policymakers, cyber forensic analysts, and global security consortiums who may use it to further their research exposure to pertinent topics in cybersecurity.

The text employs computational techniques and large-scale data analysis to study complex social phenomena and human behavior. It discusses diverse methodologies, including agent-based modeling, network analysis, natural language processing, and machine learning, to gain insights into topics ranging from social network dynamics and opinion formation to economic trends and public health crises.Features:Discusses the theoretical background of each algorithm in detail and presents the applications of each method.Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.Explains the use of predictive modeling in computational social science and applications of computational social science.Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.Covers topics such as density-based spatial clustering of applications with noise, the role of clustering in computational social science, and clustering in network structure.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Advances in Artificial Intelligence for Healthcare Applications comprehensively covers the theoretical foundations, applications, and research potential of artificial intelligence in the healthcare domain.Features:Discusses advanced concepts such as biomedical large language models, and natural language processing applicationsCovers machine vision applications for robotics in healthcare, challenges, and trends in rehabilitation devices in healthcare, and robotic interactions and control for wearable devicesPresents the Internet of Things-based disease monitoring systems, Internet of nano-things for healthcare applications, and wearable Medical Internet of Things devices for accessible healthcare servicesExplains the use of artificial intelligence in bone and brain imaging, molecular imaging using artificial intelligence, and medical image segmentationIllustrates the importance of using generative artificial intelligence for clinical documentation, and medical imaging applications using generative artificial intelligenceThe text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

In the ever-evolving landscape of cancer treatment, the fusion of artificial intelligence (AI) with medical science marks a groundbreaking shift toward more precise, efficient, and personalized healthcare. Artificial Intelligence Revolutionizing Cancer Care: Precision Diagnosis and Patient-Centric Healthcare delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management. This edited volume brings together leading experts and researchers who illuminate the latest advancements in AI technologies applied to oncology. From machine learning algorithms that predict cancer progression to sophisticated imaging techniques that improve diagnostic accuracy, this book covers a spectrum of innovations reshaping cancer care. Key highlights include precision diagnosis, uncovering how AI-driven tools are revolutionizing the early detection and accurate classification of various cancer types, leading to better patient outcomes; patient-centric approaches, exploring the shift toward personalized medicine, where AI tailors treatment protocols to individual patient profiles, ensuring more effective and targeted therapies; and ethical and practical considerations, gaining insights into the ethical, practical, and regulatory challenges of integrating AI in healthcare, emphasizing the need for patient privacy and data security. Additionally, the book looks ahead to the potential future applications of AI in oncology, including predictive analytics, robotic surgery, and beyond. Artificial Intelligence Revolutionizing Cancer Care is an essential resource for medical professionals, researchers, and students seeking to understand the intersection of AI and oncology. It offers a visionary perspective on how cutting-edge technology is poised to enhance patient care and transform the fight against cancer.This bookfocuses on the critical intersection of artificial intelligence and cancer diagnosis within the healthcare sectoremphasizes the real-world impact of artificial intelligence in improving cancer detection, treatment, and overall patient carecovers artificial intelligence algorithms, machine learning techniques, medical image analysis, predictive modeling, and patient care applicationsexplores how artificial intelligence technologies enhance the patient’s experience, resulting in better outcomes and reduced healthcare disparitiesprovides readers with an understanding of the mathematics underpinning machine learning models, including decision trees, support vector machines, and deep neural networksIt is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, and information technology.

What are the means to create a paradigm shift from conventional to intelligent companies? Industry 6.0: Technology, Practices, Challenges and Applications shows how integrating Industry 6.0 technology with data creates a framework for that shift. The book discusses the limitations, pitfalls, and open research questions in Industry 6.0, as well as the most recent advances, architectures, frameworks, applications, and novel practices, methods, and techniques. These are vital for resolving intelligent Internet of Things issues. There is a special focus on sustainable growth, humanization and environmentally friendly intelligent system applications, and an emphasis on the latest innovations in intelligent systems in classical machine learning, deep learning, Internet of Things (IoT), Industrial Internet of Things (IIoT), blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP).Features: Presents the latest trends in the fields of intelligent systems, machine intelligence, deep learning, and Industrial Internet of Things for smart environments. Discusses securing the mobile ad hoc network (MANET) by detecting the Intrusions using CSO and XGBoost model. Highlights the methods of smart things in collaborative autonomous fleets and platforms for integrating applications across different business and industry domains. Focuses on intelligent process manufacturing, automation using robotics, development of robotic appliances, and smart manufacturing. Covers data-driven agriculture, crop disease prediction, drip irrigation systems, pesticide and fertilizer sprinkling using the Industrial Internet of Things, and water estimation systems.With many contemporary articles from both scientists and practitioners working in many fields where intelligent systems and the IIoT can break new ground, the text is assembled to aim at a readership that includes researchers, statisticians, practitioners, scientists, and developers.

This reference text presents industrial and real-case scenarios of the various businesses from the edge points to the interior manufacturing units where the data is smartly generated using the Internet of Things and processed. It further discusses topics such as virtual data security, protocols for next-generation transactions, the framework of Web 4.0, and the Internet of Things in cloud grids and eco-energy.This book:Discusses automatic segregated application of enhancing the sizing of sustainable eco-energy management in cloud gridsCovers intelligent blockchain-based Internet of Things framework for sustainable secure transportation, and resdesign of blockchain with Web 4.0 architectureIllustrates non-fungible token methods for the unique identification with blockchain, and principles of confidentiality in Internet of Things eco-energyExplains the scope of intelligent and secure channels in manufacturing and logistics business sectorsIncludes the practical implications of blockchain paradigms which drive the interoperability features in end-to-end mode that aids in reducing the energy in inter-data-driven businessIt is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics, and communication engineering, computer science and engineering, information technology, and business management.

This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.

This book integrates the concepts of explainable artificial intelligence, and generative explainable artificial intelligence, showcasing how explainability can be incorporated into generative models. It highlights the importance of making generative models transparent, offering strategies to achieve this through interpretability techniques.Focuses on the principles and methodologies of explainable artificial intelligence, providing readers with a thorough understanding of how artificial intelligence models can be made transparent and interpretable.Explores explainable artificial intelligence techniques, including feature importance, SHAP values, LIME, and counterfactual explanations.Offers an in-depth examination of generative artificial intelligence, covering cutting-edge advancements in generative models like GANs, VAEs, and transformer-based architectures.Bridges the gap between theoretical concepts and their practical applications, making advanced artificial intelligence technologies accessible to the readers.Includes numerous case studies and real-world examples that demonstrate the successful application of generative artificial intelligence, and explainable artificial intelligence.The text is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, biomedical engineering, and information technology.