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(ISBA Group of Institutes, India.)
Prof. (Dr.) Kuldeep Agnihotri, Director/Principal at ISBA Group of Institutes, Indore (Madhya Pradesh), is an expert in Accounting, Finance, Corporate Law, and Taxation with over 20 years of experience, including 18 years in academia and 02 years in industry. He holds a Ph.D. in Commerce from Devi Ahilya Vishwavidyalaya, along with an MBA in Finance and Marketing, M.Com in Accounting, B.Ed, and COA from ICWAI Kolkata. With more than 71 publications in reputed national and international journals (UGC Care, Scopus, ABDC), he has also authored 16 textbooks, edited 14 books, and published 33 Indian patents along with UK–British patent grant and 07 copyrights (Including Canadian Copyright). A registered Ph.D. supervisor at Devi Ahilya Vishwavidyalaya, he guides several research scholars. He actively participates in national and international conferences, workshops, and FDPs, including those from IIT Mumbai. He is a member of Academic Councils, Boards of Studies, and editorial boards of various institutions. His contributions have earned him prestigious awards from RFI, WFST California (USA), and other academic bodies. Despite his extensive administrative and research commitments, he remains dedicated to teaching, which he considers his true passion.
The rapid advancement of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) is transforming higher education by enabling intelligent, personalized, and data-driven learning environments. Traditional educational systems often face challenges in monitoring student performance, identifying learning difficulties, promoting student wellbeing, and supporting academic innovation. To address these limitations, this study proposes an AI, ML, and IoT-enabled smart education framework that integrates real-time data collection, predictive analytics, and intelligent decision-support mechanisms to enhance student learning outcomes and institutional effectiveness. The framework utilizes IoT devices and smart sensors to gather data related to student attendance, engagement, learning behavior, and environmental conditions. Machine learning algorithms analyze the collected data to predict academic performance, identify at-risk students, and recommend personalized learning pathways. Artificial intelligence techniques further support adaptive learning, intelligent tutoring, and automated academic interventions. In addition, the proposed system incorporates student wellbeing indicators, including stress levels, participation patterns, and learning satisfaction, to foster a holistic educational experience. The integration of AI-driven analytics enables educational institutions to make informed decisions regarding curriculum design, resource allocation, and student support services. Experimental analysis demonstrates that the proposed framework improves learning efficiency, student engagement, academic achievement, and institutional innovation while facilitating data-driven educational management. The study highlights the potential of AI, ML, and IoT technologies to create sustainable, inclusive, and intelligent higher education ecosystems capable of meeting the evolving demands of digital learning environments and future workforce requirements.
AI-Driven Leadership and Cross-Sector Collaboration: Emerging Technologies for Innovation, Sustainability, and Digital Transformation explores the transformative role of Artificial Intelligence (AI) in reshaping leadership, governance, and collaboration across diverse industries. The book presents contemporary research on AI, Machine Learning, Deep Learning, Internet of Things (IoT), Cloud Computing, Big Data Analytics, Blockchain, Digital Twins, and Explainable AI, demonstrating how these technologies drive innovation, improve decision-making, and support sustainable digital transformation. It covers applications in smart cities, agriculture, energy, finance, manufacturing, governance, tourism, disaster management, cybersecurity, logistics, retail, media, water management, construction, human resources, defense, environmental conservation, aerospace, and community development. Emphasizing ethical AI, transparency, and responsible governance, the book provides practical frameworks, emerging trends, and future directions for researchers, academicians, policymakers, industry professionals, and students seeking to understand and implement AI-enabled leadership in a rapidly evolving digital world.
AI-Driven Fraud Detection and Financial Crime Prevention in Cloud-Enabled IoT and E-Commerce Ecosystems presents a comprehensive exploration of artificial intelligence techniques for securing modern digital financial systems. The book covers Machine Learning, Deep Learning, Explainable AI, Blockchain, Federated Learning, Big Data Analytics, and Behavioral Analytics for detecting and preventing financial fraud across cloud-enabled IoT, banking, and e-commerce environments. It serves as a valuable reference for researchers, academicians, students, and industry professionals interested in AI-driven cybersecurity, financial crime analytics, and intelligent financial security solutions.