Advances in Computational Collective Intelligence – serie

The Internet of Things (IoT) is a transformative technology reshaping the way data is collected, processed, and analyzed. By seamlessly connecting devices, edge platforms, and cloud infrastructures, IoT creates data-driven “ecosystems” that enable intelligent decision-making, advanced automation, and enhanced operational efficiency. IoT ecosystems, however, present significant challenges in integration, data management, cybersecurity, and resource optimization, thereby necessitating innovative frameworks to ensure reliability, privacy, and performance across these complex systems.Data-Driven IoT Ecosystems: Integrating Device, Edge, and Cloud explores advances in IoT technology and emphasizes the role of data science and machine learning in bridging device, edge, and cloud layers. The book covers a wide range of applications, including industrial robotic systems, health IoT analytics, sustainable green IoT solutions, environmental control and safety management, and cyber-physical systems. It also presents strategies for integration, security and deploying intelligent IoT applications in real-world environments.By integrating theoretical perspectives with applied research, the book is a resource for academics, researchers, practitioners, and students seeking to harness the full potential of data-driven IoT ecosystems. It examines how intelligent integration across devices and edge and cloud platforms can enhance performance, optimize resource management, strengthen cybersecurity, and support sustainable development. The book charts a pathway toward the next generation of IoT ecosystems by demonstrating how data-driven intelligence can enable smarter, safer, and more resilient technological infrastructures.

Edge computational intelligence is an interface between edge computing and artificial intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy. Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems.Together, these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways.The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems.

The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.Highlights include:Artificial Intelligence in treating and diagnosing schizophreniaAn analysis of ML’s and DL’s financial effect on healthcareAn XGBoost-based classification method for breast cancer classificationUsing ML to predict squamous diseasesML and DL applications in genomics and proteomicsApplying ML and DL to biological data

Digital technologies are influencing the way we learn, live, work, and exist in different contexts of society in the digital age. There are a variety of learning systems that support innovative digital approaches, and universities and organizations around the world are investing in building their own e-learning platforms. Digital technologies are enabling wider access to education and new markets for student recruitment, resulting in increased income prospects for global higher education institutions. Technology enables numerous data and information sources, which give greater access to information and data. It also enables highly virtual environments, which impact teaching and the classroom. The widespread use and application of digital technologies in the teaching and learning process provoke pedagogical inquiry and mediation. It is in this context that Technologies for Sustainable Global Higher Education focuses on analyzing the application of digital technologies in the teaching–learning process.The chapters in this edited collection seek to answer questions relevant to the context of higher education, such as:What is the concept of digital technologies?How is digital technology used to mediate the learning process?What technologies are used to qualify education in higher education?This book provides answers to these questions by focusing on issues central to improving education through digital technologies, digital learning, and pedagogical practices in digital education. It also provides case studies of higher education institutions.

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties.Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies.Highlights include: Power quality enhancement by PV-UPQC for non-linear load Energy management of a nanogrid through flair of deep learning from IoT environments Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis AC power optimization techniques Artificial intelligence and machine learning techniques in power systems automation

An electrical power system consists of a large number of generation, transmission, and distribution subsystems. It is a very large and complex system; hence, its installation and management are very difficult tasks. An electrical system is essentially a very large network with very large data sets. Handling these data sets can require much time to analyze and subsequently implement. An electrical system is necessary but also potentially very dangerous if not operated and controlled properly. The demand for electricity is ever increasing, so maintaining load demand without overloading the system poses challenges and difficulties.Thus, planning, installing, operating, and controlling such a large system requires new technology. Artificial intelligence (AI) applications have many key features that can support a power system and handle overall power system operations. AI-based applications can manage the large data sets related to a power system. They can also help design power plants, model installation layouts, optimize load dispatch, and quickly respond to control apparatus. These applications and their techniques have been successful in many areas of power system engineering.Artificial Intelligence Techniques in Power Systems Operations and Analysis focuses on the various challenges arising in power systems and how AI techniques help to overcome these challenges. It examines important areas of power system analysis and the implementation of AI-driven analysis techniques. The book helps academicians and researchers understand how AI can be used for more efficient operation. Multiple AI techniques and their application are explained. Also featured are relevant data sets and case studies.Highlights include: Power quality enhancement by PV-UPQC for non-linear load Energy management of a nanogrid through flair of deep learning from IoT environments Role of artificial intelligence and machine learning in power systems with fault detection and diagnosis AC power optimization techniques Artificial intelligence and machine learning techniques in power systems automation

The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.Highlights include:Artificial Intelligence in treating and diagnosing schizophreniaAn analysis of ML’s and DL’s financial effect on healthcareAn XGBoost-based classification method for breast cancer classificationUsing ML to predict squamous diseasesML and DL applications in genomics and proteomicsApplying ML and DL to biological data

Social media and crowdsourcing are important tools for solving complex problems. The benefit of crowdsourcing is that it leverages the power of human intelligence cost effectively and with less time. Social Media and Crowdsourcing: Application and Analytics examines the concepts of social media and crowdsourcing as well as their analytical aspects. It explores how these technologies contribute to the real world and examines such applications as promoting social good, agriculture, healthcare, tourism, disaster management, education, crime control, and cultural heritage. The book also looks at ethical issues in crowdsourcing and future scenarios and challenges for policy. Highlights of the book include the following:A crowdsourcing application in agricultureCrowdsourcing outline for a contemporary aided medicinal backup systemCrowdsourcing-based recommendation in the tourism industryCrowdsourcing mechanisms for reviving cultural heritageExpanding the overarching concept of utilizing social media and crowdsourcing to solve various real-life problems, this book discusses how to bring together the wisdom of crowds for various decision-making problems in agriculture, disaster management, and healthcare. It addresses the various ethical issues arising out of various crowdsourcing-based applications. It puts forward diverse methodologies to involve crowdsourcing in education to implement new strategies to enhance learning outcomes. This book also addresses various problem-solving techniques for recommender applications in the travel and tourism industry. Providing a systematic discussion of the many sectors using crowdsourcing as an essential part of social innovation, this book is a theoretical and methodological look at the application of social media.

Social media and crowdsourcing are important tools for solving complex problems. The benefit of crowdsourcing is that it leverages the power of human intelligence cost effectively and with less time. Social Media and Crowdsourcing: Application and Analytics examines the concepts of social media and crowdsourcing as well as their analytical aspects. It explores how these technologies contribute to the real world and examines such applications as promoting social good, agriculture, healthcare, tourism, disaster management, education, crime control, and cultural heritage. The book also looks at ethical issues in crowdsourcing and future scenarios and challenges for policy. Highlights of the book include the following:A crowdsourcing application in agricultureCrowdsourcing outline for a contemporary aided medicinal backup systemCrowdsourcing-based recommendation in the tourism industryCrowdsourcing mechanisms for reviving cultural heritageExpanding the overarching concept of utilizing social media and crowdsourcing to solve various real-life problems, this book discusses how to bring together the wisdom of crowds for various decision-making problems in agriculture, disaster management, and healthcare. It addresses the various ethical issues arising out of various crowdsourcing-based applications. It puts forward diverse methodologies to involve crowdsourcing in education to implement new strategies to enhance learning outcomes. This book also addresses various problem-solving techniques for recommender applications in the travel and tourism industry. Providing a systematic discussion of the many sectors using crowdsourcing as an essential part of social innovation, this book is a theoretical and methodological look at the application of social media.

Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency.Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective.Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.

Parallel and High-Performance Computing in Artificial Intelligence explores high-performance architectures for data-intensive applications as well as efficient analytical strategies to speed up data processing and applications in automation, machine learning, deep learning, healthcare, bioinformatics, natural language processing (NLP), and vision intelligence.The book’s two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include:HPC use cases, application programming interfaces (APIs), and applicationsParallelization techniquesHPC for machine learningImplementation of parallel computing with AI in big data analyticsHPC with AI in healthcare systemsAI in industrial automationCoverage of HPC architecture and techniques includes multicore architectures, parallel-computing techniques, and APIs, as well as dependence analysis for parallel computing. The book also covers hardware acceleration techniques, including those for GPU acceleration to power big data systems.As AI is increasingly being integrated into HPC applications, the book explores emerging and practical applications in such domains as healthcare, agriculture, bioinformatics, and industrial automation. It illustrates technologies and methodologies to boost the velocity and scale of AI analysis for fast discovery. Data scientists and researchers can benefit from the book’s discussion on AI-based HPC applications that can process higher volumes of data, provide more realistic simulations, and guide more accurate predictions. The book also focuses on deep learning and edge computing methodologies with HPC and presents recent research on methodologies and applications of HPC in AI.

Digital technologies are influencing the way we learn, live, work, and exist in different contexts of society in the digital age. There are a variety of learning systems that support innovative digital approaches, and universities and organizations around the world are investing in building their own e-learning platforms. Digital technologies are enabling wider access to education and new markets for student recruitment, resulting in increased income prospects for global higher education institutions. Technology enables numerous data and information sources, which give greater access to information and data. It also enables highly virtual environments, which impact teaching and the classroom. The widespread use and application of digital technologies in the teaching and learning process provoke pedagogical inquiry and mediation. It is in this context that Technologies for Sustainable Global Higher Education focuses on analyzing the application of digital technologies in the teaching–learning process.The chapters in this edited collection seek to answer questions relevant to the context of higher education, such as:What is the concept of digital technologies?How is digital technology used to mediate the learning process?What technologies are used to qualify education in higher education?This book provides answers to these questions by focusing on issues central to improving education through digital technologies, digital learning, and pedagogical practices in digital education. It also provides case studies of higher education institutions.

Parallel and High-Performance Computing in Artificial Intelligence explores high-performance architectures for data-intensive applications as well as efficient analytical strategies to speed up data processing and applications in automation, machine learning, deep learning, healthcare, bioinformatics, natural language processing (NLP), and vision intelligence.The book’s two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include:HPC use cases, application programming interfaces (APIs), and applicationsParallelization techniquesHPC for machine learningImplementation of parallel computing with AI in big data analyticsHPC with AI in healthcare systemsAI in industrial automationCoverage of HPC architecture and techniques includes multicore architectures, parallel-computing techniques, and APIs, as well as dependence analysis for parallel computing. The book also covers hardware acceleration techniques, including those for GPU acceleration to power big data systems.As AI is increasingly being integrated into HPC applications, the book explores emerging and practical applications in such domains as healthcare, agriculture, bioinformatics, and industrial automation. It illustrates technologies and methodologies to boost the velocity and scale of AI analysis for fast discovery. Data scientists and researchers can benefit from the book’s discussion on AI-based HPC applications that can process higher volumes of data, provide more realistic simulations, and guide more accurate predictions. The book also focuses on deep learning and edge computing methodologies with HPC and presents recent research on methodologies and applications of HPC in AI.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The linguistics and language education world has recently experienced a rapidly proliferating interest in applying technology. This interdisciplinary area bridges the gap between two previously separate fields.Innovations and Applications of Technology in Language Education is a collection of 12 chapters by an international group of language and linguistics education experts. Although technology in language education is a global interest, its practices should be contextualized. This book covers how language educational technology is currently applied, discusses how it should be applied, and gives directions for its future development.Providing a critical review of respective current practices and perspectives, this book begins by presenting a set of research‑based principles for developing second language teachers’ professionalism. It then examines the use of technology to enhance students’ English language skills. Acknowledging the advantages and disadvantages of AI‑mediated communication, this book argues for the use of AI to facilitate communication in language education. It also proposes the use of AI to develop and administer language tests and suggests guidelines for practitioners to deploy AI in developing and administering language tests efficiently. This book concludes by discussing technology for specific purposes in second language education and the potential of computer‑mediated communication (CMC) to enhance interaction between students.

As climate change is affecting economies and markets globally, environmental sustainability is becoming more crucial. Sustainability emphasizes the equality of future generations and is typically seen to have three components: an environmental, an economic, and a social component. Because attaining environmental sustainability also involves risks that might influence corporate operations and prospects in a strongly competitive market, thriving markets and environmental deterioration cannot coexist.Business practices are being drastically altered and improved using AI and machine learning (ML), a branch of AI that enables autonomous machines. Big data can assist AI to automatically learn and acquire knowledge to assist managers in achieving their practical and technical objectives. AI brings new methods for conducting business, finding new chances in the global market, and executing work.Using AI to Develop Sustainability Strategies for a Changing Global Economy focuses on novel ideas and research for providing a broader outlook on sustainability in business. The book Focuses on how to reduce environmentally destructive investment patterns and activities. Discusses sustainable recovery policies using AI to build back better. Presents how businesses of the future operate, augmenting industry resources with the alliance between AI and sustainable business outcomes.Presenting novel ideas for creating robust sustainability strategies, the book explores answers to the moral and ethical questions about using AI in decision-making, cyber-security, and resource management. With insight into developing efficient mechanisms to facilitate a circular economy, the book provides solutions that link the needs of the present generation without disadvantaging coming generations.

Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.

Edge computational intelligence is an interface between edge computing and artificial intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy. Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems.Together, these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways.The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems.

The Asia-Pacific region, home to some of the world’s fastest-growing economies, faces a range of complex challenges, including environmental degradation, the increasing frequency of natural hazards, and rapid urbanization. Addressing these issues, which many countries across the globe are facing, requires innovative, interdisciplinary approaches to promote sustainable development and enhance resilience. Geographic information systems (GIS), when combined with multi-criteria decision-making (MCDM) techniques and advanced technologies such as artificial intelligence (AI), offer powerful tools to tackle these multifaceted problems. AIGIS integrates AI with GIS to derive insights from geospatial data. The fusion of AI techniques with GIS enhances data analysis, visualization, and decision-making. Artificial Intelligence, Geographic Information Systems, and Multi-Criteria Decision-Making for Improving Sustainable Development explore how these integrated tools can support decision-making processes aimed at advancing sustainable development.Drawing on research and insights from diverse disciplines, the book looks at how GIS, MCDM, and AI can provide solutions for disaster risk reduction, environmental monitoring, urban planning, and natural resource management. Through diverse case studies and theoretical explorations, this book highlights the value of integrated geospatial tools in facilitating informed decision-making and fostering resilient societies in the face of evolving challenges. It covers a wide range of topics, including the following:Site-soil-geology assessments in FijiFlood risk analysis in Hong KongAir quality management in Delhi during the COVID-19 pandemicVegetation health monitoring in ThailandBringing together the work of academicians, practitioners, and decision-makers, the book reflects the growing recognition towards effective and sustainable solutions to complex problems, which require a multidimensional approach, integrating scientific, economic, and social considerations. By providing the latest research and practical applications of MCDM, AI, and GIS, it contributes to ongoing efforts to build a more sustainable and resilient future for the Asia-Pacific region, as well as for the world.

In recent years, the application of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart healthcare has been increasing. We are approaching a world where connected smart devices tell people when they need to visit a doctor because these devices will be able to detect health problems and discover symptoms of illness that may need medical care. AI-collaborative IoT technologies can help medical professionals with decision-making. These technologies can also help develop a sustainable and smart healthcare system.AI and IoT Technology and Applications for Smart Healthcare Systems helps readers understand complex scientific topics in a simple and accessible way. It introduces the world of AI-collaborative IoT physics, explaining how this technology behaves at the smallest level and how this can revolutionize healthcare. The book shows how IoT technology and AI can work together to make computers more powerful and capable of solving complex problems in the healthcare sector. Exploring the effect of AI-collaborative technology on IoT technologies, the book discusses how IoT can benefit from AI algorithms to enable machines to learn, make decisions, and process information more efficiently. Because smart machines create more perceptive devices and systems, the application of this technology raises important ethical questions about privacy, security, and the responsible development of healthcare IoT technology, which this book covers. The book also provides insight into the potential applications of these technologies not only in the healthcare industry but also in related fields, such as smart transportation, smart manufacturing, and smart cities.

In recent years, the application of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart healthcare has been increasing. We are approaching a world where connected smart devices tell people when they need to visit a doctor because these devices will be able to detect health problems and discover symptoms of illness that may need medical care. AI-collaborative IoT technologies can help medical professionals with decision-making. These technologies can also help develop a sustainable and smart healthcare system.AI and IoT Technology and Applications for Smart Healthcare Systems helps readers understand complex scientific topics in a simple and accessible way. It introduces the world of AI-collaborative IoT physics, explaining how this technology behaves at the smallest level and how this can revolutionize healthcare. The book shows how IoT technology and AI can work together to make computers more powerful and capable of solving complex problems in the healthcare sector. Exploring the effect of AI-collaborative technology on IoT technologies, the book discusses how IoT can benefit from AI algorithms to enable machines to learn, make decisions, and process information more efficiently. Because smart machines create more perceptive devices and systems, the application of this technology raises important ethical questions about privacy, security, and the responsible development of healthcare IoT technology, which this book covers. The book also provides insight into the potential applications of these technologies not only in the healthcare industry but also in related fields, such as smart transportation, smart manufacturing, and smart cities.

In an era defined by rapid technological advancements, the convergence of artificial intelligence (AI) with cloud, edge, fog, and mobile computing is transforming the landscape of computing and data processing. These emerging technologies are not only enhancing computational capabilities but also paving the way for innovative applications across diverse industries, from healthcare and finance to transportation and entertainment.Leveraging Artificial Intelligence in Cloud, Edge, Fog and Mobile Computing explores the symbiotic relationship between AI and these computing paradigms. As AI continues to evolve, its integration with cloud, edge, fog, and mobile computing platforms is unlocking new potentials and driving efficiencies and enabling real-time, intelligent decision-making processes.The book begins with an in-depth examination of the foundational principles of cloud, edge, fog, and mobile computing, followed by a detailed analysis of how AI technologies are being embedded within these frameworks. It then delves into the unique advantages and challenges of each paradigm, highlighting their roles in facilitating seamless, decentralized data processing and enhancing user experiences.The book is structured to provide a comprehensive understanding of the current state and future directions of AI in these computing environments.The book is intended to serve as a resource and inspiration for those seeking to explore the vast potential of AI in the realms of cloud, edge, fog, and mobile computing. Its goal is to spark new ideas, foster innovation, and contribute to the ongoing dialogue on the future of intelligent computing.

With research perspectives on elements of psychology and technology, Emotional Intelligence in the Digital Era: Concepts, Frameworks, and Applications provides insight into the intersection of emotional intelligence and digital technologies, including artificial intelligence (AI). It explains how technology has the potential to enhance emotional intelligence and promote positive psychological outcomes. It also discusses the role of technology in facilitating social connections that support mental health and promote self-reflection and personal growth.The main themes explored in the book revolve around understanding human psychology in the context of digital technology in the post-pandemic environment. The book delves into how emotional intelligence plays a crucial role in adapting to virtual environments. It also examines how AI and other digital technologies can enhance engagement and effectiveness. Emphasizing the need for a balanced approach to integrating technology in the field of psychology, the book highlights the potential benefits of technology in promoting emotional intelligence and positive psychological outcomes while also acknowledging the potential risks and ethical considerations associated with its use.Covering such aspects of human psychology as cognitive and emotional processing, personality, motivation, and decision-making, the book discusses the role of technology in improving human psychological perspectives and emotional intelligence, especially within a post-pandemic context. It also examines the use of virtual reality, teletherapy, and other digital tools in mental health interventions. Written for researchers, academics, and mental health professionals, the book is a deep dive into research investigating human psychology, emotional intelligence, and technology.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.