Archana Patel

Naveen Kumar Chaudhary

Abinash Mishra

Chothmal Kumawat

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.

Advancements, Applications, Challenges, and Solutions

Artificial Intelligence and Digital Forensics

Narayan C. Debnath

Ambrish Kumar Mishra

Shweta Anand

Purvi Pokhariyal

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.

Artificial Intelligence for Risk Mitigation in the Financial Industry

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.

Semantic Technologies for Intelligent Industry 4.0 Applications

Bharat Bhushan

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.

Archana Patel

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