Archana Patel – författare

Semantic web technologies (SWTs) offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. This book provides a roadmap for semantic web technologies (SWTs) and highlights their role in a wide range of domains including cloud computing, Internet of Things, big data, sensor network, and so forth. It also explores the prospects of these technologies including different data interchange formats, query languages, ontologies, Linked Data, and notations. The role of SWTs in ‘epidemic Covid-19’, ‘e-learning platforms and systems’, ‘block chain’, ‘open online courses’, and ‘visual analytics in healthcare’ is described as well. This book:

This book is aimed at researchers and graduate students in computer science, informatics, web technology, cloud computing, and Internet of Things.

Semantic web technologies (SWTs) offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. This book provides a roadmap for semantic web technologies (SWTs) and highlights their role in a wide range of domains including cloud computing, Internet of Things, big data, sensor network, and so forth. It also explores the prospects of these technologies including different data interchange formats, query languages, ontologies, Linked Data, and notations. The role of SWTs in ‘epidemic Covid-19’, ‘e-learning platforms and systems’, ‘block chain’, ‘open online courses’, and ‘visual analytics in healthcare’ is described as well. This book:

This book is aimed at researchers and graduate students in computer science, informatics, web technology, cloud computing, and Internet of Things.

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies.

Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science?

This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This first volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies.

Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as: Can semantic technologies facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science?

This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and, thus, it is a unique resource for scholars, researchers, professionals, and practitioners in this field.

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do.

Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title.

As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do.

Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title.

As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.

As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge,and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very lose to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter.

This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people’s future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field.

As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge,and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very lose to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter.

This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people’s future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms.

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.

This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms.

Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.

This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

In the past decade, artificial intelligence (AI) has made significant advancements in various sectors of society, such as education, health, e-commerce, media and entertainment, banking and finance, transportation, and defense, among others. Its application has permeated every sector, leaving no area untouched. However, the utilization of AI brings forth crucial legal, ethical, and technical concerns and obstacles that must be appropriately addressed through thoughtful deliberation, discussions, and the implementation of effective regulations.

AI and Emerging Technologies: Automated Decision-Making, Digital Forensics, and Ethical Considerations provides a comprehensive and insightful roadmap for exploring the advancements, challenges, solutions, and implications of AI in three key areas: the legal field, digital forensic, and decision-making. By delving into these topics, this book offers a deep understanding of how AI can be optimally utilized to deliver maximum benefits to users, all within a single comprehensive source. One of the focuses of this book is to shed light on the preictal application of emerging technologies in automated decision-making while also addressing the ethical considerations that arise from their use. By examining the integration of these technologies into digital forensics and their impact on other domains, such as gaming applications deepfake, this book presents valuable insights into the broader implications of AI.

The book serves as an invaluable resource for anyone seeking to understand and navigate the complex world of AI. By offering a comprehensive exploration of its applications, ethical considerations, and data protection techniques, it provides researchers and scholars, graduate students, software engineers, along with data scientists the necessary insights to harness the full potential of AI while ensuring its responsible and ethical use.

In the past decade, artificial intelligence (AI) has made significant advancements in various sectors of society, such as education, health, e-commerce, media and entertainment, banking and finance, transportation, and defense, among others. Its application has permeated every sector, leaving no area untouched. However, the utilization of AI brings forth crucial legal, ethical, and technical concerns and obstacles that must be appropriately addressed through thoughtful deliberation, discussions, and the implementation of effective regulations.

AI and Emerging Technologies: Automated Decision-Making, Digital Forensics, and Ethical Considerations provides a comprehensive and insightful roadmap for exploring the advancements, challenges, solutions, and implications of AI in three key areas: the legal field, digital forensic, and decision-making. By delving into these topics, this book offers a deep understanding of how AI can be optimally utilized to deliver maximum benefits to users, all within a single comprehensive source. One of the focuses of this book is to shed light on the preictal application of emerging technologies in automated decision-making while also addressing the ethical considerations that arise from their use. By examining the integration of these technologies into digital forensics and their impact on other domains, such as gaming applications deepfake, this book presents valuable insights into the broader implications of AI.

The book serves as an invaluable resource for anyone seeking to understand and navigate the complex world of AI. By offering a comprehensive exploration of its applications, ethical considerations, and data protection techniques, it provides researchers and scholars, graduate students, software engineers, along with data scientists the necessary insights to harness the full potential of AI while ensuring its responsible and ethical use.

Artificial Intelligence (AI) has revolutionized several sectors, with digital forensics being one that has been notably affected by its rapid advancement. AI brings previously unheard-of powers to this field, helping authorities examine large datasets, identify developments, and uncover digital evidence that is essential to cracking cybercrimes. Despite these promising developments, using AI in digital forensics presents problems. The complexity and dynamic nature of cyber-attacks are a significant challenge, demanding the ongoing adaptation of AI models to new attack strategies. This changing environment makes it difficult to create reliable and future-proof solutions. This book explores the advancements, applications, challenges, and solutions that AI brings to the realm of digital forensics.

Artificial Intelligence and Digital Forensics: Advancements, Applications, Challenges, and Solutions includes the latest applications and examples with real data making the book meaningful for readers. It is written in very simple language with each technology having its dedicated chapter that explains how it works and provides an example of a real-world application. Key points and a summary are provided at the end of each chapter to enable the readers to quickly review the major concepts as it presents a practical understanding, so the readers can be better equipped to handle AI-based tools with ease and efficiency. The book provides unconditional support to those who are making decisions by using massive data from their organization and applying the findings to real-world current business scenarios addressing the challenges associated with integrating AI into digital forensics and offering practical solutions. It also offers insights into the ethical use of AI technologies and guidance on navigating legal requirements and ensuring responsible and compliant practices.

This book caters to industry professionals from diverse backgrounds, including engineering, data science, computer science, law enforcement, and legal experts, fostering a holistic understanding of the subject along with providing practical insights and real-world examples.

Artificial Intelligence (AI) has revolutionized several sectors, with digital forensics being one that has been notably affected by its rapid advancement. AI brings previously unheard-of powers to this field, helping authorities examine large datasets, identify developments, and uncover digital evidence that is essential to cracking cybercrimes. Despite these promising developments, using AI in digital forensics presents problems. The complexity and dynamic nature of cyber-attacks are a significant challenge, demanding the ongoing adaptation of AI models to new attack strategies. This changing environment makes it difficult to create reliable and future-proof solutions. This book explores the advancements, applications, challenges, and solutions that AI brings to the realm of digital forensics.

Artificial Intelligence and Digital Forensics: Advancements, Applications, Challenges, and Solutions includes the latest applications and examples with real data making the book meaningful for readers. It is written in very simple language with each technology having its dedicated chapter that explains how it works and provides an example of a real-world application. Key points and a summary are provided at the end of each chapter to enable the readers to quickly review the major concepts as it presents a practical understanding, so the readers can be better equipped to handle AI-based tools with ease and efficiency. The book provides unconditional support to those who are making decisions by using massive data from their organization and applying the findings to real-world current business scenarios addressing the challenges associated with integrating AI into digital forensics and offering practical solutions. It also offers insights into the ethical use of AI technologies and guidance on navigating legal requirements and ensuring responsible and compliant practices.

This book caters to industry professionals from diverse backgrounds, including engineering, data science, computer science, law enforcement, and legal experts, fostering a holistic understanding of the subject along with providing practical insights and real-world examples.