AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.

Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.

The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Internet of Things and Machine Learning for?Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

Medical Robotics and Intelligent Healthcare Technologies: AI Applications to Improve Healthcare in Developing Countries is an essential reference for professionals and researchers in fields such as biomedical engineering, health informatics, bioelectronics, medical electronics, physiology, and robotics. This book presents the latest advancements in AI and machine learning-based robotics applications, enabling readers to quickly grasp effective methods and compare various approaches. Addressing critical research areas that directly impact human life and health fills a significant market gap, providing valuable insights into AI, deep learning, machine learning, and big data analytics methods as they relate to emerging robotics research models in the biomedical and healthcare domains.It offers a comprehensive understanding of the concepts, themes, and tools in medical robotics, IoT-based applications, edge computing, and artificial intelligence. With a specific focus on developing countries, this book catalyzes healthcare progress by showcasing the potential of medical robotics and intelligent healthcare technologies. It aims to empower researchers and practitioners to harness these state-of-the-art technologies to improve healthcare systems worldwide.

Wireless Sensor Networks and the Internet of Things: Future Directions and Applications explores a wide range of important and real-time issues and applications in this ever-advancing field. Different types of WSN and IoT technologies are discussed in order to provide a strong framework of reference, and the volume places an emphasis on solutions to the challenges of protection, conservation, evaluation, and implementation of WSN and IoT that lead to low-cost products, energy savings, low carbon usage, higher quality, and global competitiveness.

The volume is divided into four sections that cover:

This collection of chapters on an important and diverse range of issues presents case studies and applications of cutting-edge technologies of WSN and IoT that will be valuable for academic communities in computer science, information technology, and electronics, including cyber security, monitoring, and data collection. The informative material presented here can be applied to many sectors, including agriculture, energy and power, resource management, biomedical and health care, business management, and others.

Wireless Sensor Networks and the Internet of Things: Future Directions and Applications explores a wide range of important and real-time issues and applications in this ever-advancing field. Different types of WSN and IoT technologies are discussed in order to provide a strong framework of reference, and the volume places an emphasis on solutions to the challenges of protection, conservation, evaluation, and implementation of WSN and IoT that lead to low-cost products, energy savings, low carbon usage, higher quality, and global competitiveness.

The volume is divided into four sections that cover:

This collection of chapters on an important and diverse range of issues presents case studies and applications of cutting-edge technologies of WSN and IoT that will be valuable for academic communities in computer science, information technology, and electronics, including cyber security, monitoring, and data collection. The informative material presented here can be applied to many sectors, including agriculture, energy and power, resource management, biomedical and health care, business management, and others.

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.

This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.

This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Emphasizing a sustainable and green approach, this new book presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. The book examines modern AI technologies such as IoT, cloud computing, machine learning, and neural networking in the context of fully automated transportation that meets current requirements.

The volume provides an informative review of the difficulties and recent developments in smart mobility and transportation, encompassing technical, algorithmic, and social elements. The volume examines innovative service and platform concepts for future mobility. Artificial intelligence principles are examined as well as their implementation in modern hardware architecture. New machine learning issues for autonomous vehicles and fleets are investigated in the framework of artificial intelligence. In addition, the book investigates the human dynamics and social implications of future mobility concepts.

Highlighting the research directions in this field, Artificial Intelligence for Future Intelligent Transportation: Smarter and Greener Infrastructure Design will be of value for researchers in cybersecurity, machine learning, data analysis, and artificial intelligence. Ethical hackers, students, and faculty will find useful information here as well.

Emphasizing a sustainable and green approach, this new book presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. The book examines modern AI technologies such as IoT, cloud computing, machine learning, and neural networking in the context of fully automated transportation that meets current requirements.

The volume provides an informative review of the difficulties and recent developments in smart mobility and transportation, encompassing technical, algorithmic, and social elements. The volume examines innovative service and platform concepts for future mobility. Artificial intelligence principles are examined as well as their implementation in modern hardware architecture. New machine learning issues for autonomous vehicles and fleets are investigated in the framework of artificial intelligence. In addition, the book investigates the human dynamics and social implications of future mobility concepts.

Highlighting the research directions in this field, Artificial Intelligence for Future Intelligent Transportation: Smarter and Greener Infrastructure Design will be of value for researchers in cybersecurity, machine learning, data analysis, and artificial intelligence. Ethical hackers, students, and faculty will find useful information here as well.

With the ever-increasing threat of cyber-attacks, especially as the COVID-19 pandemic helped to ramp up the use of digital communications technology, there is a continued need to find new ways to maintain and improve cybersecurity. This new volume investigates the advances in artificial intelligence and soft computing techniques in cybersecurity. It specifically looks at cybersecurity during the COVID-19 pandemic, the use of cybersecurity for cloud intelligent systems, applications of cybersecurity techniques for web applications, and cybersecurity for cyber-physical systems. A diverse array of technologies and techniques are explored for cybersecurity applications, such as the Internet of Things, edge computing, cloud computing, artificial intelligence, soft computing, machine learning, cross-site scripting in web-based services, neural gas (GNG) clustering technique, and more.

AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.

Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.

The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Internet of Things and Machine Learning for?Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

Medical Robotics and Intelligent Healthcare Technologies: AI Applications to Improve Healthcare in Developing Countries is an essential reference for professionals and researchers in fields such as biomedical engineering, health informatics, bioelectronics, medical electronics, physiology, and robotics. This book presents the latest advancements in AI and machine learning-based robotics applications, enabling readers to quickly grasp effective methods and compare various approaches. Addressing critical research areas that directly impact human life and health fills a significant market gap, providing valuable insights into AI, deep learning, machine learning, and big data analytics methods as they relate to emerging robotics research models in the biomedical and healthcare domains.It offers a comprehensive understanding of the concepts, themes, and tools in medical robotics, IoT-based applications, edge computing, and artificial intelligence. With a specific focus on developing countries, this book catalyzes healthcare progress by showcasing the potential of medical robotics and intelligent healthcare technologies. It aims to empower researchers and practitioners to harness these state-of-the-art technologies to improve healthcare systems worldwide.

Wireless Sensor Networks and the Internet of Things: Future Directions and Applications explores a wide range of important and real-time issues and applications in this ever-advancing field. Different types of WSN and IoT technologies are discussed in order to provide a strong framework of reference, and the volume places an emphasis on solutions to the challenges of protection, conservation, evaluation, and implementation of WSN and IoT that lead to low-cost products, energy savings, low carbon usage, higher quality, and global competitiveness.

The volume is divided into four sections that cover:

This collection of chapters on an important and diverse range of issues presents case studies and applications of cutting-edge technologies of WSN and IoT that will be valuable for academic communities in computer science, information technology, and electronics, including cyber security, monitoring, and data collection. The informative material presented here can be applied to many sectors, including agriculture, energy and power, resource management, biomedical and health care, business management, and others.

Wireless Sensor Networks and the Internet of Things: Future Directions and Applications explores a wide range of important and real-time issues and applications in this ever-advancing field. Different types of WSN and IoT technologies are discussed in order to provide a strong framework of reference, and the volume places an emphasis on solutions to the challenges of protection, conservation, evaluation, and implementation of WSN and IoT that lead to low-cost products, energy savings, low carbon usage, higher quality, and global competitiveness.

The volume is divided into four sections that cover:

This collection of chapters on an important and diverse range of issues presents case studies and applications of cutting-edge technologies of WSN and IoT that will be valuable for academic communities in computer science, information technology, and electronics, including cyber security, monitoring, and data collection. The informative material presented here can be applied to many sectors, including agriculture, energy and power, resource management, biomedical and health care, business management, and others.

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others.

• Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications

• Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches

• Discusses various techniques of IoT systems for healthcare data analytics

• Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics

• Focuses more on the application of algorithms in various real life biomedical and engineering problems

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

With a need to find alternatives to the traditional face-to-face teaching and learning during the pandemic, this topical volume discusses the best use of technologies in higher education, addressing the growing role of AI in the digital transformation of higher education systems. With chapters from eminent professors, researchers, and others involved in higher education from a selection of different countries, the volume looks at blockchain and IoT technologies to create smart universities now and for the future.

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.

This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions.

This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Emphasizing a sustainable and green approach, this new book presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. The book examines modern AI technologies such as IoT, cloud computing, machine learning, and neural networking in the context of fully automated transportation that meets current requirements.

The volume provides an informative review of the difficulties and recent developments in smart mobility and transportation, encompassing technical, algorithmic, and social elements. The volume examines innovative service and platform concepts for future mobility. Artificial intelligence principles are examined as well as their implementation in modern hardware architecture. New machine learning issues for autonomous vehicles and fleets are investigated in the framework of artificial intelligence. In addition, the book investigates the human dynamics and social implications of future mobility concepts.

Highlighting the research directions in this field, Artificial Intelligence for Future Intelligent Transportation: Smarter and Greener Infrastructure Design will be of value for researchers in cybersecurity, machine learning, data analysis, and artificial intelligence. Ethical hackers, students, and faculty will find useful information here as well.

Emphasizing a sustainable and green approach, this new book presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. The book examines modern AI technologies such as IoT, cloud computing, machine learning, and neural networking in the context of fully automated transportation that meets current requirements.

The volume provides an informative review of the difficulties and recent developments in smart mobility and transportation, encompassing technical, algorithmic, and social elements. The volume examines innovative service and platform concepts for future mobility. Artificial intelligence principles are examined as well as their implementation in modern hardware architecture. New machine learning issues for autonomous vehicles and fleets are investigated in the framework of artificial intelligence. In addition, the book investigates the human dynamics and social implications of future mobility concepts.

Highlighting the research directions in this field, Artificial Intelligence for Future Intelligent Transportation: Smarter and Greener Infrastructure Design will be of value for researchers in cybersecurity, machine learning, data analysis, and artificial intelligence. Ethical hackers, students, and faculty will find useful information here as well.

With the ever-increasing threat of cyber-attacks, especially as the COVID-19 pandemic helped to ramp up the use of digital communications technology, there is a continued need to find new ways to maintain and improve cybersecurity. This new volume investigates the advances in artificial intelligence and soft computing techniques in cybersecurity. It specifically looks at cybersecurity during the COVID-19 pandemic, the use of cybersecurity for cloud intelligent systems, applications of cybersecurity techniques for web applications, and cybersecurity for cyber-physical systems. A diverse array of technologies and techniques are explored for cybersecurity applications, such as the Internet of Things, edge computing, cloud computing, artificial intelligence, soft computing, machine learning, cross-site scripting in web-based services, neural gas (GNG) clustering technique, and more.