Driving Global Economic Transformation Through AI and Machine Learning - Softcover

Softcover

ISBN 13: 9798337372686

Publisher: Business Science Reference, 2026

Synopsis

In an era defined by economic turbulence, global pandemics, climate shocks, geopolitical instability, and recurring financial crises, the world economy stands at a critical turning point. Traditional recovery strategies centered on fiscal policy adjustments, stimulus measures, and market interventions are no longer sufficient on their own to address the scale, speed, and complexity of contemporary disruptions. As these challenges intensify, there is a growing need to rethink how economic resilience and recovery are conceptualized and implemented. This underscores the importance of interdisciplinary research that critically examines the rapid transformations shaping the global macroeconomic landscape. Driving Global Economic Transformation Through AI and Machine Learning examines the transformative potential of intelligent systems and their capacity to predict risks, model crisis scenarios, manage disruptions, and optimize responses across industries and governments. From forecasting financial downturns to addressing supply chain vulnerabilities, artificial intelligence and machine learning are positioned as critical enablers of economic resilience, stability, and innovation. Covering topics such as complex dynamics in financial markets, stock price prediction, and time series forecasting, this book is an excellent academic resource for graduate and doctoral students, economists, data scientists, international development professionals, technology developers, policymakers, and more.

About this title

About the Authors

Ayoub Khan (Senior member IEEE) received his Ph. D (Electrical Engg.) from Jamia Millia Islamia, New Delhi, India, and Master of Technology (Computer Science and Engineering) from Guru Gobind Singh Indraprastha, New Delhi, India. Presently, he is working as Deputy Director, Smart Cyber-Physical System (CPS) Research Centre, University of Bisha, Saudi Arabia with interests in Internet of Things, RFID, Wireless Sensors Networks, Ad Hoc Network, Smart Cities, Industrial IoT, and signal processing, NFC, Routing in Network-on-Chip, Real Time and Embedded Systems. He is a certified Chief Information Security Officer (CISO) and Certified Information Security Manager (CISM). He has more than 14 years of experience in his research and consultancy area.

Pushkar Praveen is an Assistant Professor in the Department of Electronics and Communication Engineering at G.B. Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India. He received his Ph.D. in Electronics and Communication Engineering from Uttarakhand Technical University and M.Tech. in VLSI Design from CDAC Noida. He has over 13 years of academic and industry experience. His research interests include electronic device and circuit design, VLSI systems, embedded systems, Internet of Things (IoT). Dr. Praveen has published several research articles in reputed SCI/SCIE and Scopus- indexed journals and conferences. He has also contributed to multiple edited books and book chapters. He is actively involved in organizing faculty development programs, conferences, and research projects funded by national and international agencies. Dr. Praveen is a member of professional organizations including IEEE, IAENG, and ISTE.

Agya Ram Verma is an Assistant Professor in the Department of Electronics and Communication Engineering at Govind Ballabh Pant Engineering College, Pauri Garhwal, Uttarakhand. He earned his Ph.D. in 2019 on “Filter Design Using Evolutionary Technique for Biomedical Signal Application” and holds an M.Tech in Electronics and Communication Engineering from IIITDM Jabalpur and a B.Tech in Electronics and Instrumentation Engineering from IET Rohilkhand University. Dr. Verma’s research interests include multirate signal processing, filter and filter bank design, biomedical and ECG signal processing, and applications of machine learning in communication systems. He has been awarded several fellowships and recognitions, including the MHRD fellowship, the Summer Faculty Research Fellowship at IIT Delhi, and a Best Paper Award at CCSN 2023. He has successfully completed sponsored research projects funded by MHRD, AICTE, and Asi@ Connect, and currently leads a MeitY- funded project on IoT and AI. His academic contributions include multiple patents, authored books on machine learning and Python programming, and over 25 international journal publications. In addition to research, Dr. Verma has guided numerous M.Tech and B.Tech dissertations, organized national and international workshops, and serves as a reviewer for leading journals such as Elsevier Signal Processing Journal and IET Electronics Letters. He continues to contribute to the advancement of biomedical signal processing, AI applications, and emerging communication technologies.

Rijwan Khan is currently working as Professor & Dean FoCA at Marwadi University, India . He also worked as Dean Emerging Technologies and Training at ABES Institute of Technology, Ghaziabad. He completed his PhD in 2017 from Jamia Millia Islamia University, New Delhi, India. He has more than 20 years’ experience of teaching. He served as Head of Department-CSE in ABESIT and also Dean Research in ABESIT. He is editor in chief of Journal of Engineering, Science and Mathematics (JESM). He is guest editor of more than 15 journals. He edited 10 books in Elsevier, IGI Global, CRC and Bentham Science. He published 12 SCI Indexed Journal Papers, 21 Scopus Indexed Journal Papers and 21 Conference papers and 19 book chapters. He was keynote speaker in an international conference in Muscat, Oman conducted by Ministry of Agriculture, Oman. He has H index 25 and I index 40 and number of citations are more than 2500. He is member of ACM, ISTE, IETE.

Indrajeet Kumar is an Associate Professor at the Birla School of Applied Science, Birla Global University, Bhubaneswar, Odisha. He earned his Ph.D. in Computer Science and Engineering from GB Pant Institute of Engineering & Technology in 2019, specializing in breast density analysis and classification using mammographic images. With over nine years of professional experience in academia and industry, his research focuses on digital image processing, machine learning, deep learning, and biomedical image analysis. Dr. Kumar has published extensively in reputed international journals and conferences, edited books and special issues, filed multiple patents, and serves as a reviewer for leading journals and conferences.

Edition details

• Publisher: Business Science Reference
• Publication date: 2026
• Language: English
• ISBN 13: 9798337372686
• Binding: Paperback
• Number of pages: 584
• Editor: Khan Mohammad Ayoub, Praveen Pushkar, Verma Agya Ram, Khan Rijwan, Kumar Indrajeet