Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.

Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.

Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.

Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.

Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.

With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.

Some unique features of this book include:

This book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.

Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.

With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.

Some unique features of this book include:

This book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.

Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.

Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.

This book:

This book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.

This book:

This book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

The concept of the metaverse signifies the forthcoming stage of development of the Internet, wherein it will facilitate the creation of virtual worlds that are enduring, decentralized, and capable of providing immersive experiences in real time. The metaverse has vast potential for utilization in the domains of life sciences and healthcare, hence motivating investigations in contemporary trends, early adoption use cases, and the forthcoming opportunities it presents. The metaverse also possesses the capacity to fundamentally transform decentralized clinical trials through the elimination of physical and geographical constraints. This change in thinking entails the relocation of clinical trials from conventional settings to the comfort and convenience of patients’ residences, resulting in improvements in health behavior, medication adherence, remote monitoring, and other associated factors.

Applying Metaverse Technologies to Human-Computer Interaction for Healthcare focuses on the current developments in the metaverse, investigates its applications in the life sciences and healthcare industry based on metaverse powered human−computer interactions (HCI), analyzes early adoption use cases that provide measurable commercial benefits, and anticipates prospects in this rapidly evolving domain.

The book examines the treatment, management, and prevention of illnesses with the use of immersive therapeutics that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). It examines applications in cognitive therapy, support groups, psychiatric examinations, rehabilitation, and even physical therapy The book covers how healthcare practitioners have the capability to provide such services as diagnosis, treatment, monitoring, and care in remote settings, through the utilization of AR headsets and wearable devices. It concludes by discussing the continuous development of technology to facilitate the growth and maturation of the metaverse, hence enabling substantial business benefits for the life sciences and healthcare industries.

The concept of the metaverse signifies the forthcoming stage of development of the Internet, wherein it will facilitate the creation of virtual worlds that are enduring, decentralized, and capable of providing immersive experiences in real time. The metaverse has vast potential for utilization in the domains of life sciences and healthcare, hence motivating investigations in contemporary trends, early adoption use cases, and the forthcoming opportunities it presents. The metaverse also possesses the capacity to fundamentally transform decentralized clinical trials through the elimination of physical and geographical constraints. This change in thinking entails the relocation of clinical trials from conventional settings to the comfort and convenience of patients’ residences, resulting in improvements in health behavior, medication adherence, remote monitoring, and other associated factors.

Applying Metaverse Technologies to Human-Computer Interaction for Healthcare focuses on the current developments in the metaverse, investigates its applications in the life sciences and healthcare industry based on metaverse powered human−computer interactions (HCI), analyzes early adoption use cases that provide measurable commercial benefits, and anticipates prospects in this rapidly evolving domain.

The book examines the treatment, management, and prevention of illnesses with the use of immersive therapeutics that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). It examines applications in cognitive therapy, support groups, psychiatric examinations, rehabilitation, and even physical therapy The book covers how healthcare practitioners have the capability to provide such services as diagnosis, treatment, monitoring, and care in remote settings, through the utilization of AR headsets and wearable devices. It concludes by discussing the continuous development of technology to facilitate the growth and maturation of the metaverse, hence enabling substantial business benefits for the life sciences and healthcare industries.

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands‑on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands‑on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.

Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.

Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.

Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.

Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.

With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.

Some unique features of this book include:

This book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.

Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.

With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.

Some unique features of this book include:

This book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.

Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.

Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.

This book:

This book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.

This book:

This book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

The concept of the metaverse signifies the forthcoming stage of development of the Internet, wherein it will facilitate the creation of virtual worlds that are enduring, decentralized, and capable of providing immersive experiences in real time. The metaverse has vast potential for utilization in the domains of life sciences and healthcare, hence motivating investigations in contemporary trends, early adoption use cases, and the forthcoming opportunities it presents. The metaverse also possesses the capacity to fundamentally transform decentralized clinical trials through the elimination of physical and geographical constraints. This change in thinking entails the relocation of clinical trials from conventional settings to the comfort and convenience of patients’ residences, resulting in improvements in health behavior, medication adherence, remote monitoring, and other associated factors.

Applying Metaverse Technologies to Human-Computer Interaction for Healthcare focuses on the current developments in the metaverse, investigates its applications in the life sciences and healthcare industry based on metaverse powered human−computer interactions (HCI), analyzes early adoption use cases that provide measurable commercial benefits, and anticipates prospects in this rapidly evolving domain.

The book examines the treatment, management, and prevention of illnesses with the use of immersive therapeutics that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). It examines applications in cognitive therapy, support groups, psychiatric examinations, rehabilitation, and even physical therapy The book covers how healthcare practitioners have the capability to provide such services as diagnosis, treatment, monitoring, and care in remote settings, through the utilization of AR headsets and wearable devices. It concludes by discussing the continuous development of technology to facilitate the growth and maturation of the metaverse, hence enabling substantial business benefits for the life sciences and healthcare industries.

The concept of the metaverse signifies the forthcoming stage of development of the Internet, wherein it will facilitate the creation of virtual worlds that are enduring, decentralized, and capable of providing immersive experiences in real time. The metaverse has vast potential for utilization in the domains of life sciences and healthcare, hence motivating investigations in contemporary trends, early adoption use cases, and the forthcoming opportunities it presents. The metaverse also possesses the capacity to fundamentally transform decentralized clinical trials through the elimination of physical and geographical constraints. This change in thinking entails the relocation of clinical trials from conventional settings to the comfort and convenience of patients’ residences, resulting in improvements in health behavior, medication adherence, remote monitoring, and other associated factors.

Applying Metaverse Technologies to Human-Computer Interaction for Healthcare focuses on the current developments in the metaverse, investigates its applications in the life sciences and healthcare industry based on metaverse powered human−computer interactions (HCI), analyzes early adoption use cases that provide measurable commercial benefits, and anticipates prospects in this rapidly evolving domain.

The book examines the treatment, management, and prevention of illnesses with the use of immersive therapeutics that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). It examines applications in cognitive therapy, support groups, psychiatric examinations, rehabilitation, and even physical therapy The book covers how healthcare practitioners have the capability to provide such services as diagnosis, treatment, monitoring, and care in remote settings, through the utilization of AR headsets and wearable devices. It concludes by discussing the continuous development of technology to facilitate the growth and maturation of the metaverse, hence enabling substantial business benefits for the life sciences and healthcare industries.

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands‑on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands‑on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.