Next-Generation AI and ML: Trends, Challenges, and Opportunities

About The Book

Next-Generation AI and ML: Trends, Challenges, and Opportunities serves as a comprehensive roadmap to the rapidly changing world of artificial intelligence and machine learning. Covering foundational concepts, emerging technologies, and future possibilities, the book provides an in-depth look at how AI and ML are transforming every major sector.

It begins with the historical development of AI and ML, then moves into modern advancements including deep learning, transformers, reinforcement learning, edge–fog–cloud computing, quantum paradigms, big data training, and AI-driven automation. The book also critically examines challenges around algorithmic bias, data privacy, transparency, accountability, and the socio-economic consequences of AI adoption.

Real-world applications—including medical diagnostics, drug discovery, autonomous vehicles, predictive analytics, smart cities, cybersecurity, and content creation—are discussed with clarity and contemporary relevance. The concluding chapters highlight future trends such as responsible AI, explainable systems, human-AI collaboration, sustainable AI, and the research directions shaping the coming decades.

Written with academic depth and practical insight, this book is an essential resource for B.Tech, M.Tech, MCA, B.Sc./M.Sc. students, researchers, educators, professionals, and anyone interested in understanding how next-generation AI and ML will define the future of technology and society.

About The Author

Dr. Shalini Ninoria is a distinguished academic and administrator with over 16 years of experience in higher education, specializing in Computer Science and Engineering. She is currently an Associate Professor at Teerthanker Mahaveer University, Moradabad, with a significant portfolio of administrative responsibilities including NAAC and NBA coordination, IQAC Deputy Coordinator, and Program Coordinator for BCA and CDOE.
She holds a Ph.D. in Computer Science, M.Phil., MCA, and has qualified UGC-NET. Dr. Ninoria is also a research supervisor for Ph.D. and M.Tech scholars, with expertise in AI, Data Mining, Machine Learning, and Soft Computing. She has contributed extensively to SCOPUS and SCI-indexed publications, served as a reviewer and technical chair for IEEE conferences, and has guided multiple research and development initiatives.
Dr. Ninoria is a prolific researcher with patents, published books, and several journal and conference papers to her credit.

Deepak Pandita is an Assistant Professor at PCET’s Pimpri Chinchwad University, School of Computer Applications, MCA Department. He holds both Master's and a Bachelor's degree in Computer Applications and brings over 17 years of full-time teaching experience. His areas of specialization include Cloud Computing, Data Warehousing and Data Mining, Advanced Database Management Systems, Artificial Intelligence, Software Project Management, Web Technologies, Machine Learning, Database Management Systems, Operating Systems, Mobile Computing, and Object-Oriented Technologies.
Deepak Pandita has presented and published four research papers at various national and international conferences. Additionally, he has published seven research papers in UGC care-listed journals with high impact factors. He has also contributed to Savitribai Phule Pune University in various roles such as Paper Setter, Examiner, Moderator, and in syllabus revision for the MCA program.

Dr. Nazir Ahmad Ahengar is an Assistant Professor in the Department of Mathematics, School of Engineering and Technology, Pimpri Chinchwad University, Pune. He holds a Ph.D. in Mathematics from Rani Durgavati University, Jabalpur, and has submitted his D.Sc. thesis at Manipur International University. He is also pursuing an MBA in Business Analytics and Data Science from the University of Mysore.
With over sixteen years of teaching experience, Dr. Ahengar has taught a wide range of subjects including Numerical Techniques, Transform Techniques, Graph Theory, Discrete Mathematics, and Calculus. He has published 33 research papers, including 18 in Scopus-indexed journals and 4 in Web of Science, and has presented his work at several national and international conferences.
His research interests include fuzzy sets, generalized mappings, and intuitionistic fuzzy structures, with a focus on their applications in artificial intelligence and data science. He remains dedicated to advancing mathematical education and research through quality academic contributions.

About The Book

Next-Generation AI and ML: Trends, Challenges, and Opportunities serves as a comprehensive roadmap to the rapidly changing world of artificial intelligence and machine learning. Covering foundational concepts, emerging technologies, and future possibilities, the book provides an in-depth look at how AI and ML are transforming every major sector.

It begins with the historical development of AI and ML, then moves into modern advancements including deep learning, transformers, reinforcement learning, edge–fog–cloud computing, quantum paradigms, big data training, and AI-driven automation. The book also critically examines challenges around algorithmic bias, data privacy, transparency, accountability, and the socio-economic consequences of AI adoption.

Real-world applications—including medical diagnostics, drug discovery, autonomous vehicles, predictive analytics, smart cities, cybersecurity, and content creation—are discussed with clarity and contemporary relevance. The concluding chapters highlight future trends such as responsible AI, explainable systems, human-AI collaboration, sustainable AI, and the research directions shaping the coming decades.

Written with academic depth and practical insight, this book is an essential resource for B.Tech, M.Tech, MCA, B.Sc./M.Sc. students, researchers, educators, professionals, and anyone interested in understanding how next-generation AI and ML will define the future of technology and society.

About The Author

Dr. Shalini Ninoria is a distinguished academic and administrator with over 16 years of experience in higher education, specializing in Computer Science and Engineering. She is currently an Associate Professor at Teerthanker Mahaveer University, Moradabad, with a significant portfolio of administrative responsibilities including NAAC and NBA coordination, IQAC Deputy Coordinator, and Program Coordinator for BCA and CDOE.
She holds a Ph.D. in Computer Science, M.Phil., MCA, and has qualified UGC-NET. Dr. Ninoria is also a research supervisor for Ph.D. and M.Tech scholars, with expertise in AI, Data Mining, Machine Learning, and Soft Computing. She has contributed extensively to SCOPUS and SCI-indexed publications, served as a reviewer and technical chair for IEEE conferences, and has guided multiple research and development initiatives.
Dr. Ninoria is a prolific researcher with patents, published books, and several journal and conference papers to her credit.

Deepak Pandita is an Assistant Professor at PCET’s Pimpri Chinchwad University, School of Computer Applications, MCA Department. He holds both Master's and a Bachelor's degree in Computer Applications and brings over 17 years of full-time teaching experience. His areas of specialization include Cloud Computing, Data Warehousing and Data Mining, Advanced Database Management Systems, Artificial Intelligence, Software Project Management, Web Technologies, Machine Learning, Database Management Systems, Operating Systems, Mobile Computing, and Object-Oriented Technologies.
Deepak Pandita has presented and published four research papers at various national and international conferences. Additionally, he has published seven research papers in UGC care-listed journals with high impact factors. He has also contributed to Savitribai Phule Pune University in various roles such as Paper Setter, Examiner, Moderator, and in syllabus revision for the MCA program.

Dr. Nazir Ahmad Ahengar is an Assistant Professor in the Department of Mathematics, School of Engineering and Technology, Pimpri Chinchwad University, Pune. He holds a Ph.D. in Mathematics from Rani Durgavati University, Jabalpur, and has submitted his D.Sc. thesis at Manipur International University. He is also pursuing an MBA in Business Analytics and Data Science from the University of Mysore.
With over sixteen years of teaching experience, Dr. Ahengar has taught a wide range of subjects including Numerical Techniques, Transform Techniques, Graph Theory, Discrete Mathematics, and Calculus. He has published 33 research papers, including 18 in Scopus-indexed journals and 4 in Web of Science, and has presented his work at several national and international conferences.
His research interests include fuzzy sets, generalized mappings, and intuitionistic fuzzy structures, with a focus on their applications in artificial intelligence and data science. He remains dedicated to advancing mathematical education and research through quality academic contributions.