This book explores the foundational principles and emerging advancements shaping modern computing, bridging theory and practice across technologies, transforming todays world. It begins with an introduction to modern computer science before covering key topics such as generative AI, creative content generation, quantum computing, data analytics, and machine learning. Ethical aspects of AI are highlighted to encourage responsible innovation. Chapters on big data and data science show how large datasets drive insights. The book also examines blockchain, edge computing, and IoT, with applications in finance, health care, and logistics. Additional sections include robotics, cybersecurity, humancomputer interaction, and cloud computing, emphasizing their roles in building secure, connected systems. Blending core and emerging concepts of computer science with case studies, the book equips students, researchers, professionals, and enthusiasts to navigate an evolving technological landscape.
Introduction to Modern Computer Science.- Generative AI and Creative
Content Generation.- Quantum Computing: Beyond Classical Bits.- Data
Analytics and Machine Learning Insights.- Artificial Intelligence and
Ethics.- Big Data and Data Science.- Blockchain and Cryptocurrencies.-
Cybersecurity and Ethical Hacking.- Internet of Things (IoT): Connecting the
World.- Cloud Computing and Virtualization.- Human-Computer Interaction
(HCI).- Robotics and Automation.- Edge Computing: Processing at the Edge.
Dr. Tanvir Habib Sardar is Associate Professor in the Department of Computer Science and Engineering, School of Engineering, at Dayananda Sagar University, Bengaluru, India. With over 17 years of academic and research experience, he specializes in big data, machine learning, distributed computing, artificial intelligence, and emerging computing paradigms. He has published 62 Scopus-indexed research articles, authored and edited over ten books, and holds more than a dozen patents.
Dr. Sardars current research focuses on scalable machine learning models, including quantum computing, quantum-optimized intelligent systems, neuro-fuzzy computing, and deep learning, alongside distributed frameworks such as Hadoop. His work bridges theoretical foundations with real-world application, fostering innovation, problem-solving, and critical thinking among students and researchers. Beyond his scholarly contributions, he actively engages in curriculum development, accreditation processes, and mentoring initiatives that support future-ready engineering education.
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