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:Explores all the critical aspects of semantic web technologies (SWTs) Discusses the impact of SWTs on cloud computing, Internet of Things, big data, and sensor network Offers a comprehensive examination of the emerging research in the areas of SWTs and their related domains Provides a template to develop a wide range of smart and intelligent applications Includes latest applications and examples with real data 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:Explores all the critical aspects of semantic web technologies (SWTs) Discusses the impact of SWTs on cloud computing, Internet of Things, big data, and sensor network Offers a comprehensive examination of the emerging research in the areas of SWTs and their related domains Provides a template to develop a wide range of smart and intelligent applications Includes latest applications and examples with real data This book is aimed at researchers and graduate students in computer science, informatics, web technology, cloud computing, and Internet of Things.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics.Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source.This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics.Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source.This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the 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.

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.

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.

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.

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations, demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practices is packed with up-to-date examples and data, ensuring it is both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts toward sustainable development, empowering readers to enhance their decision-making abilities, and also discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

This book explores the multifaceted implications of technological innovation on legal systems, addressing how emerging technologies enhance, challenge, and necessitate reform in legal practice and governance, with a focus on artificial intelligence, big data, and blockchain.Technological advancements have profoundly reshaped every facet of human life, including the realm of law and governance. From automating legal research and drafting to revolutionizing court proceedings through e-court automation, AI-driven systems are transforming the legal landscape. These advancements bring unprecedented efficiency but also pose critical legal, ethical, and technical challenges. By delving into applications such as predictive justice, document mining, online dispute resolution, and AI governance, this book provides insights into the opportunities and complexities of integrating technology into law; and addresses key topics such as automating legal decision-making, safeguarding data privacy, and the ethical use of AI in the justice system. Through in-depth analysis and case studies, this book bridges the gap between technological innovation and regulatory systems, ensuring that the future of law incorporates robust mechanisms to meet emerging challenges.The book will be of interest to researchers in the field of the law of emerging technologies, regulation, governance, artificial intelligence, and data science.

DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science 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 provides answers to various questions like: Can semantic technologies be able to 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 knowledge data science? How does knowledge data science relate to other domains? 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 researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.

Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.