Jyoti Sekhar Banerjee – författare

The Global Conference on Artificial Intelligence and Applications (GCAIA 2021) provides a prominent venue for researchers, engineers, entrepreneurs, and scholar students to share their research ideas in the area of AI. Academic researchers would reveal the results and conclusions of laboratory based investigations via the GCAIA 21 platform, which bridges the gap between academia, industry, and government ethics. The GCAIA 21 platform will also bring together regional and worldwide issues to explore current advancements in contemporary Computation Intelligence.

This Conference Proceedings volume contains the written versions of most of the contributions presented during the conference of GCAIA 2021. The conference has provided an excellent chance for researchers from diverse locations to present and debate their work in the field of artificial intelligence and its applications. It includes a selection of 62 papers. All accepted papers were subjected to strict peer-review by 2–4 expert referees. The papers have been selected for this volume because of their quality and their relevance to the theme of the conference.

The Global Conference on Artificial Intelligence and Applications (GCAIA 2021) provides a prominent venue for researchers, engineers, entrepreneurs, and scholar students to share their research ideas in the area of AI. Academic researchers would reveal the results and conclusions of laboratory based investigations via the GCAIA 21 platform, which bridges the gap between academia, industry, and government ethics. The GCAIA 21 platform will also bring together regional and worldwide issues to explore current advancements in contemporary Computation Intelligence.

This Conference Proceedings volume contains the written versions of most of the contributions presented during the conference of GCAIA 2021. The conference has provided an excellent chance for researchers from diverse locations to present and debate their work in the field of artificial intelligence and its applications. It includes a selection of 62 papers. All accepted papers were subjected to strict peer-review by 2–4 expert referees. The papers have been selected for this volume because of their quality and their relevance to the theme of the conference.

Intelligent Cyber-Physical Systems Security for Industry 4.0: Applications, Challenges and Management presents new cyber-physical security findings for Industry 4.0 using emerging technologies like artificial intelligence (with machine/deep learning), data mining, applied mathematics. All these are the essential components for processing data, recognizing patterns, modeling new techniques, and improving the advantages of data science.

Features

• Presents an integrated approach with Cyber-Physical Systems, CPS security, and Industry 4.0 in one place

• Exposes the necessity of security initiatives, standards, security policies, and procedures in the context of industry 4.0

• Suggests solutions for enhancing the protection of 5G and the Internet of Things (IoT) security

• Promotes how optimization or intelligent techniques envisage the role of artificial intelligence-machine/deep learning (AI-ML/DL) in cyberphysical systems security for industry 4.0

This book is primarily aimed at graduates, researchers and professionals working in the field of security. Executives concerned with security management, knowledge dissemination, information, and policy development for data and network security in different educational, government, and non-government organizations will also find this book useful.

Intelligent Cyber-Physical Systems Security for Industry 4.0: Applications, Challenges and Management presents new cyber-physical security findings for Industry 4.0 using emerging technologies like artificial intelligence (with machine/deep learning), data mining, applied mathematics. All these are the essential components for processing data, recognizing patterns, modeling new techniques, and improving the advantages of data science.

Features

• Presents an integrated approach with Cyber-Physical Systems, CPS security, and Industry 4.0 in one place

• Exposes the necessity of security initiatives, standards, security policies, and procedures in the context of industry 4.0

• Suggests solutions for enhancing the protection of 5G and the Internet of Things (IoT) security

• Promotes how optimization or intelligent techniques envisage the role of artificial intelligence-machine/deep learning (AI-ML/DL) in cyberphysical systems security for industry 4.0

This book is primarily aimed at graduates, researchers and professionals working in the field of security. Executives concerned with security management, knowledge dissemination, information, and policy development for data and network security in different educational, government, and non-government organizations will also find this book useful.

The text provides a comprehensive study of the application of advanced artificial intelligence (AI) in next-generation wireless communications with a focus on theory, standardization, and core development. It further highlights AI-enabled intelligent architecture for sixth-generation (6G) networks to realize smart resource management, automatic network adjustment, and intelligent service layers. The book covers artificially assisted non-orthogonal multiple access schemes for 6G communication.

This book:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

The text provides a comprehensive study of the application of advanced artificial intelligence (AI) in next-generation wireless communications with a focus on theory, standardization, and core development. It further highlights AI-enabled intelligent architecture for sixth-generation (6G) networks to realize smart resource management, automatic network adjustment, and intelligent service layers. The book covers artificially assisted non-orthogonal multiple access schemes for 6G communication.

This book:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

This book covers a wide range of topics such as mathematical modeling, computational methods, simulation techniques in engineering, and their applications in environmental sciences. It begins with an introduction to the fundamental concepts and principles of mathematical modeling with subsequent sections delving into various simulation techniques, including numerical methods, statistical modeling, and bio-mathematical modeling. It also explores the application of these techniques in addressing complex environmental problems.

Features:

This book is aimed at researchers and graduate students in computer engineering, applied mathematics, and applied ecology.

This book covers a wide range of topics such as mathematical modeling, computational methods, simulation techniques in engineering, and their applications in environmental sciences. It begins with an introduction to the fundamental concepts and principles of mathematical modeling with subsequent sections delving into various simulation techniques, including numerical methods, statistical modeling, and bio-mathematical modeling. It also explores the application of these techniques in addressing complex environmental problems.

Features:

This book is aimed at researchers and graduate students in computer engineering, applied mathematics, and applied ecology.

AIEST is a leading conference focused on providing a platform to researchers, scholars, engineers, scientists and industrial professionals to gather knowledge and bridge the gap between academia and its industrial aspects, around the world. This conference will be an immersive experience primarily focusing on the latest advancements and researchers in various fields of engineering, including but not limited to Mechanical Engineering, Civil Engineering, Electrical Engineering, Electronics and Communications Engineering, Computer Science Engineering, Information Technology and other interdisciplinary areas. AIEST will cater to the transitional practices where industrial knowledge would be conveyed to academia regarding real-time scenarios and practical findings, thus fostering collaboration and the development of innovative solutions to counter contemporary challenges in engineering and technology.

AIEST is a leading conference focused on providing a platform to researchers, scholars, engineers, scientists and industrial professionals to gather knowledge and bridge the gap between academia and its industrial aspects, around the world. This conference will be an immersive experience primarily focusing on the latest advancements and researchers in various fields of engineering, including but not limited to Mechanical Engineering, Civil Engineering, Electrical Engineering, Electronics and Communications Engineering, Computer Science Engineering, Information Technology and other interdisciplinary areas. AIEST will cater to the transitional practices where industrial knowledge would be conveyed to academia regarding real-time scenarios and practical findings, thus fostering collaboration and the development of innovative solutions to counter contemporary challenges in engineering and technology.

The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity and are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On the one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and, therefore, hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. The authors focus on the positive potential of GenAI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.

The book is structured around the following key topics:

Finally, this book serves as a platform for the community to share innovative research on the use of GenAI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how GenAI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.

The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity and are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On the one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and, therefore, hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. The authors focus on the positive potential of GenAI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.

The book is structured around the following key topics:

Finally, this book serves as a platform for the community to share innovative research on the use of GenAI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how GenAI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.