Thangavel Murugan – författare

Cybersecurity and Data Science Innovations for Sustainable Development of HEICC: Healthcare, Education, Industry, Cities, and Communities brings together a collection of chapters that explore the intersection of cybersecurity, data science, and sustainable development across key sectors: healthcare, education, industry, cities, and communities. It delves into cybersecurity advancements and examines how innovations in cybersecurity are shaping the landscape of healthcare, education, industry, and urban environments. Data science advancements take center stage, showcasing the transformative power of data analytics in improving outcomes across HEICC sectors. Whether it’s optimizing resource allocation in healthcare, protecting patient privacy, personalizing learning experiences in education, enhancing efficiency in industry, or fostering sustainable development in cities and communities, data science offers unprecedented opportunities for innovation and progress.Key points:Healthcare system security and privacy, protecting patient data, and enabling development of novel healthcare solutionsSecuring educational data, improving online learning security, and harnessing data analytics for tailored education approachesManufacturing, finance, and transportation. Diving into critical infrastructure security, detecting and mitigating cyber threats, and using data‑driven insights for better industrial operationsHelping cities and communities develop sustainably, smart city security challenges, data privacy in urban environments, data analytics for urban planning, and community cybersecurity awarenessThis book serves as a comprehensive guide for researchers, practitioners, policymakers, and stakeholders navigating the complex landscape of cybersecurity and data science in the pursuit of sustainable development across HEICC domains.

Cybersecurity and Data Science Innovations for Sustainable Development of HEICC: Healthcare, Education, Industry, Cities, and Communities brings together a collection of chapters that explore the intersection of cybersecurity, data science, and sustainable development across key sectors: healthcare, education, industry, cities, and communities. It delves into cybersecurity advancements and examines how innovations in cybersecurity are shaping the landscape of healthcare, education, industry, and urban environments. Data science advancements take center stage, showcasing the transformative power of data analytics in improving outcomes across HEICC sectors. Whether it’s optimizing resource allocation in healthcare, protecting patient privacy, personalizing learning experiences in education, enhancing efficiency in industry, or fostering sustainable development in cities and communities, data science offers unprecedented opportunities for innovation and progress.Key points:Healthcare system security and privacy, protecting patient data, and enabling development of novel healthcare solutionsSecuring educational data, improving online learning security, and harnessing data analytics for tailored education approachesManufacturing, finance, and transportation. Diving into critical infrastructure security, detecting and mitigating cyber threats, and using data‑driven insights for better industrial operationsHelping cities and communities develop sustainably, smart city security challenges, data privacy in urban environments, data analytics for urban planning, and community cybersecurity awarenessThis book serves as a comprehensive guide for researchers, practitioners, policymakers, and stakeholders navigating the complex landscape of cybersecurity and data science in the pursuit of sustainable development across HEICC domains.

The book offers a modern exploration of the intersection of technology and education. It examines the prospects of integrating different AI tools into higher education and explores the challenges, opportunities, and innovative solutions for the different issues surrounding the use of AI in higher education.Each chapter discusses a different area where AI can enhance the educational landscape, such as AI Integration in Higher Education and Immersive Virtual Reality-Based Learning Environments. The book also emphasizes Student-Centered Learning, AI-powered frameworks for academic excellence, and Learner-Centric Pedagogies. Furthermore, it delves into the role of AI in Personalized Learning in STEM courses, the development of AI-based Tutoring Systems, and the use of Machine Learning to identify students' mental health status. The volume concludes with "Dynamic Horizon," which examines how AI and gamification are shaping higher education.This book is essential for educators, administrators, researchers, and policymakers who want to leverage AI to create an adaptive, personalized, and engaging learning environment. "Adopting Artificial Intelligence Tools in Higher Education" provides valuable insights into the future of education, paving the way for a more empowered and enlightened academic world.

This book offers a modern exploration of the intersection of technology and education. It examines the prospects of integrating different AI tools into higher education and explores the challenges, opportunities, and innovative solutions for the different issues surrounding the use of AI in higher education.Each chapter discusses a different area where AI can enhance the educational landscape, such as AI integration in higher education and immersive virtual reality-based learning environments. The book also emphasizes student-centered learning, AI-powered frameworks for academic excellence, and learner-centric pedagogies. Furthermore, it delves into the role of AI in personalized learning in STEM courses, the development of AI-based tutoring systems, and the use of machine learning to identify students’ mental health status. The volume concludes with "Dynamic Horizon," which examines how AI and gamification are shaping higher education.This book is essential for educators, administrators, researchers, and policymakers who want to leverage AI to create an adaptive, personalized, and engaging learning environment. Adopting Artificial Intelligence Tools in Higher Education provides valuable insights into the future of education, paving the way for a more empowered and enlightened academic world.

This edited volume explores the adoption of artificial intelligence (AI) tools in higher education, specifically focusing on student assessment. It examines the integration of various AI tools within higher education, discussing the challenges and opportunities they present and the innovative solutions they offer.The chapters explore various issues surrounding the use of AI in higher education and propose potential solutions. The book begins with a systematic exploration of AI's potential, presenting innovative ways to ensure fair and accurate assessments that enhance the overall quality of education. It highlights the benefits of AI-powered grading systems that streamline assessment processes, provide timely feedback, and promote fair evaluations. The text discusses how machine learning algorithms can revolutionize assessment methods, allowing individualized, adaptive testing tailored to each student's unique needs. Furthermore, it examines natural language processing (NLP) techniques for evaluating student essays by analyzing linguistic features such as grammar and semantic coherence. Moreover, it highlights AI-powered virtual assistants offering personalized feedback and learning recommendations. Case studies illustrate successful AI assessment implementations and methods for improving AI-based evaluations. The book also addresses engagement and success in the post-COVID-19 context. It raises concerns about plagiarism and academic integrity, comparing AI solutions to traditional methods while exploring the challenges associated with adopting AI in education. Educators, administrators, academics, and technology experts working in institutes of higher learning will find this volume compelling. It is also suitable for students taking courses in educational technology, e-learning, and digital learning.

This edited volume explores the adoption of artificial intelligence (AI) tools in higher education, specifically focusing on student assessment. It examines the integration of various AI tools within higher education, discussing the challenges and opportunities they present and the innovative solutions they offer.The chapters explore various issues surrounding the use of AI in higher education and propose potential solutions. The book begins with a systematic exploration of AI's potential, presenting innovative ways to ensure fair and accurate assessments that enhance the overall quality of education. It highlights the benefits of AI-powered grading systems that streamline assessment processes, provide timely feedback, and promote fair evaluations. The text discusses how machine learning algorithms can revolutionize assessment methods, allowing individualized, adaptive testing tailored to each student's unique needs. Furthermore, it examines natural language processing (NLP) techniques for evaluating student essays by analyzing linguistic features such as grammar and semantic coherence. Moreover, it highlights AI-powered virtual assistants offering personalized feedback and learning recommendations. Case studies illustrate successful AI assessment implementations and methods for improving AI-based evaluations. The book also addresses engagement and success in the post-COVID-19 context. It raises concerns about plagiarism and academic integrity, comparing AI solutions to traditional methods while exploring the challenges associated with adopting AI in education. Educators, administrators, academics, and technology experts working in institutes of higher learning will find this volume compelling. It is also suitable for students taking courses in educational technology, e-learning, and digital learning.

Today's generative AI has been marked by the advent of neural networks, inspired by the human brain, which are trained to recognize patterns in a dataset. Once the network is trained, it can make decisions or predictions without being programmed to perform tasks. Generative AI learns from a set of data without explicit instructions and can create and generate new digital content such as text, audio and art. Recent models are beginning to overcome challenges such as computational power, data quality and training stability.This edited book on generative AI presents novel perspectives, approaches and methodologies, as well as security, ethical and legal considerations, and future trends. Topics and technologies covered include generative transformers and text generation models, generative models for human-like speech synthesis, generative AI for image synthesis, data synthesis for privacy protection, and exploration of the impact of generative AI in fields including industry 4.0, astronomy, and brain tumour detection. Two chapters offer perspectives on ethics and legality.Generative AI Unleashed: Advancements, transformative applications and future frontiers will serve as a valuable resource for researchers, engineers, advanced students and lecturers operating in the domains that are significantly impacted by generative AI.