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AI-Driven Enhancements in Indian Payment Systems: A Futuristic Perspective on Financial Applications and Modifications
This study explores the application of machine learning (ML) to improve Indian payment systems, with a focus on AI-driven developments for tasks including fraud detection, transaction validation, and consumer behaviour research. We evaluate the effectiveness of several machine learning (ML) systems, including K-Nearest Neighbours (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machine (SVM), and Logistic Regression (LR), using a variety of criteria. The results, which include precision, F1 score, accuracy, recall, and AUC, show how well Random Forest and Logistic Regression work to detect fraudulent transactions. Important elements including transaction amount, payment method, and user behaviour patterns are also revealed by the feature importance evaluation. Significant differences exist between models in terms of training times and hyperparameter optimisation outcomes. All things considered, this study highlights how ML models can spur innovation in Indian payment systems, enhancing security, effectiveness, and consumer satisfaction while offering a thorough assessment structure for potential future implementation in the fintech industry. 2025 IEEE. -
AI-Driven Fleet Management System: Smart Vehicle Directory for Organizational Efficiency
Affected by the increasing need for an effective, automated system that can manage campus entry and parking, it urgently calls for a solution to integrate license plate recognition and visitor management for security and convenience. This will further give allowances to the pedestrian visitors for a pass on their mobiles in exchange for their mobile numbers. This is a proposal for the integration of Automatic License Plate Recognition into an Automated Character Recognition system, which would help extract license plate information on a college portal. It would encompass new vehicle registrations, staff and student registrations against license plates, and would also be able to generate digital passes for visitors. Though the system depends upon the conventional algorithms, these demonstrate good performance under normal applications. This simply means that an institution is bound to ensure that campus security and parking administration run seamlessly, due to the necessity for facilitating easy and prompt vehicle registration processes and issuance of daily passes. Integration of a visitor management system issuing electronic passes via mobile numbers with an ALPR system matched with OCR for extracting license information will greatly improve the campus security in administering the parking facilities. One of the most used methodologies to this effect is You Only Look Once, with very great abilities in object detection. This will be integrated to automate license plate registration, link it to the registered users, and provide digital passes for visitors. The result will be a more organized and secure campus environment. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-driven growth: Addressing legal and economic complexities in the digital era
The character of economic growth is being reshaped by artificial intelligence (AI) as it revolutionizes entire sectors and drives technological progress. This chapter studies the relationship between AI techs and economic growth in digital economies, and explores how AI improves productivity, creates sectoral shifts and new economic possibilities. But with these progress also comes major challenges, such as the threat of job loss and inequality, regulatory dilemmas and data security and privacy problems. It further points out that AI wields significant potential for economic development, but inclusive and sustainable growth requires targeted strategies to be mobilised. The chapter provides case studies, explores economic policy, and identifies direction for further research to increase our understanding of the impact of AI on international arrangements. 2025, IGI Global Scientific Publishing. -
AI-Driven Health Coach for Diabetes Management
Artificial intelligence (AI) is transforming diabetes care through innovative approaches that enhance monitoring, prediction, and treatment. AI-powered health coaches exemplify this progress by automating various aspects of patient care, such as creating personalized dietary plans and managing medication schedules, thereby optimizing resource utilization with minimal human intervention. In India, where diabetes affects over 77 million people and significantly elevates the risk of complications like heart disease and stroke, AI-driven tools offer immense potential. Food recognition and nutritional apps powered by AI can revolutionize diabetes management by tracking dietary intake and providing tailored recommendations. However, widespread adoption faces barriers, including challenges related to localization, cultural relevance, and integration with healthcare systems. This chapter examines the role of AI in diabetes management, evaluating the benefits and limitations of current applications. It also proposes a framework for an AI-driven health coach tailored to the Indian context. The proposed solution aims to bridge existing gaps by delivering accurate, culturally sensitive, and integrated diabetes management tools, ultimately improving long-term health outcomes for Indian patients. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
AI-Driven Home Climate Optimization: The Role of ChatGPT in Enhancing AC Efficiency
The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized home automation, yet traditional air-conditioning (AC) systems still struggle with energy inefficiency. Our research presents a novel solution, integrating AI, IoT, and user-centric design with ChatGPT, to optimize AC systems responsively to occupants' needs. Our methodology employs ChatGPT's capability to analyze historical data, discern patterns, and provide intelligent recommendations for AC operation. This transcends the functions of standard smart thermostats through AI-driven decision-making, optimizing every AC operational moment for both comfort and energy conservation. The system's foundation in data-driven decisions ensures alignment with external and internal conditions, enhancing energy efficiency and user comfort. 2024 IEEE. -
AI-Driven Human Augmentation: Enhancing Productivity, Creativity, and Decision-Making in the Business Process
This study explores how AI augments human productivity, creativity, and decisionmaking across sectors, positioning it as a tool to enhance human potential. Using a qualitative approach with case studies from healthcare, business, and creative industries-such as IBM Watson-it illustrates AI's practical applications. Central to the study are ethical concerns like data privacy, algorithmic bias, and fairness. 2026, IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Image Forensics for Cybercrime Detection: Chain of Custody and Legal Admissibility
Advanced techniques for analyzing and verifying digital images make AI-driven image forensics a crucial tool in detecting cybercrime. With the rise of cybercrime involving altered and forged images, AI-driven techniques like deepfake detection and metadata analysis present hopeful means for uncovering evidence. The incorporation of AI into forensic investigations, however, presents considerable challenges concerning the chain of custody and the legal admissibility of digital evidence. To ensure that digital evidence stays intact and unmodified during the investigation, it is crucial to uphold an uninterrupted chain of custody. To set forth unambiguous criteria regarding the admissibility of AI-processed evidence in court, legal systems must also grapple with the progressing character of AI. This chapter investigates how AI-driven forensics, maintaining the integrity of digital evidence, and the legal structures necessary to guarantee justice in the era of artificial intelligence intersect. 2026 by IGI Global Scientific Publishing. -
AI-Driven Innovations in Soil Management, Crop Productivity, and Autonomous Agricultural Vehicles
The incorporation of Artificial Intelligence (AI) into agriculture is escalating significant transformations in essential areas like soil management, crop yield enhancement, and self-operating farming vehicles. This chapter examines the ways in which AI-driven tools-such as machine learning algorithms, remote sensing technologies, robotics, and autonomous systems-enhance decision-making processes, optimize resource utilization, and contribute to environmental sustainability. With AI, it is possible to carry out accurate diagnostics of soil health, predictive irrigation and fertilization, early detection of diseases, and real-time monitoring of crops. Moreover, AI-enabled self-driving vehicles are transforming the world of mechanized agriculture by providing efficient planting, spraying, and harvesting. This chapter offers a detailed examination of these innovations, assesses their practical uses, and deliberates on the consequences for farmers, researchers, and policymakers. It also underscores the ethical, infrastructural, and socio-economic challenges linked to the use of AI in agriculture. 2026, IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Lead Scoring: Enhancing Real Estate Decisions with Predictive Analytics
Lead optimization remains underutilized in customer acquisition, with businesses often focusing on new models rather than refining existing processes. Many overlook automation, real-time data, and feedback loops that enhance insight into lead behavior. Automated optimization continuously improves lead scoring by fine-tuning models over time. Current approaches rely on basic lead scoring without real-time data integration or continuous updates. This research focuses on machine learning-driven lead optimization to improve scoring accuracy and personalized communication. We propose an AI-enhanced system that integrates CRM data with predictive models using ensemble techniques like Random Forest and XGBoost. Our approach achieves high accuracy in property hotspot and ROI prediction, with R2 values up to 0.99. However, a 5% uncertainty exists, requiring carefully generated synthetic datasets. This methodology improves lead prioritization, decision-making and data-driven strategies, ultimately increasing conversion rates and revenue growth. 2025 IEEE. -
AI-driven load forecasting and energy management in smart grids using hybrid deep models
Modern power systems are becoming more complex, and integrating renewable energy sources (RES) calls for sophisticated solutions for accurate load forecasting and efficient energy management. To improve forecast accuracy and operational efficiency in smart grids, the research suggests a hybrid deep learning (DL) structure that blends convolutional neural networks (CNN) with long short-term memory (LSTM) systems. The LSTM element records sequential connections within historical energy usage, while the CNN element extracts geographical features from environmental variables such as temperature, humidity, and solar radiation. A comprehensive preprocessing pipeline comprising data cleaning, normalization, and feature selection ensures high-quality inputs for model training. The proposed LSTM-bCNN model is evaluated using a publicly available dataset, and its performance is benchmarked against traditional and contemporary models including ARIMA, SVM, RF, and standalone LSTM. According to findings from experiments, the mixture model obtains the highest R-squared (R) value, the lowest Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE), confirming its robustness in capturing complex patterns in energy consumption. This research highlights the possible of hybrid DL models in enabling intelligent, adaptive, and resilient energy management systems (EMS) within next-generation smart grids. 2026 Elsevier B.V. -
AI-Driven Marketing Automation and Hyper-Personalization Strategies for Enhanced Consumer Engagement
The integration of artificial intelligence (AI) into marketing has revolutionized how businesses engage with consumers, allowing for hyper-personalized interactions and marketing automation. This paper explores the role of AI in driving consumer engagement through targeted strategies that leverage data analytics, machine learning, and predictive modelling. By enabling real-time personalization, AI tools optimize customer experiences, increase engagement, and improve conversion rates. The paper also addresses the challenges associated with data privacy and ethical considerations in AI-driven marketing. The findings suggest that AI-driven marketing can significantly enhance consumer loyalty and business performance when used responsibly and strategically. 2026, IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Pedagogies and Learning Environments in Modern Education: A PRISMA-Based Systematic Review
This study examines the role of AI in education, focusing on its impact on teaching effectiveness, student engagement, and institutional adoption. A systematic literature review was conducted, analysed 97 peer-reviewed articles published between 2024 and 2025. The Preferred Reporting Items for Systematic Reviews (PRISMA) framework was employed to ensure the rigorous selection and analysis of relevant studies. The findings indicate that AI enhances learning experiences by enabling adaptive learning environments, automating administrative tasks, and improving assessment accuracy. However, challenges such as algorithmic biases, data privacy concerns, lack of AI literacy among educators, and unequal access to AI tools pose significant barriers. Literature emphasised the need for structured AI training programs for teachers, while ethical considerations in AI-driven assessments. The study recommends the development of AI literacy programs for educators, policy frameworks to ensure ethical AI implementation, and improved digital infrastructure to bridge the accessibility gap. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Policy Frameworks and Decision Support Systems for Invasive Species Management and Biodiversity Conservation
Invasive species pose significant threats to biodiversity and ecosystem health. AI-driven policy frameworks and decision support systems can enhance invasive species management and biodiversity conservation efforts. By integrating machine learning algorithms and big data analysis, these systems provide real-time insights, enabling stakeholders to make informed decisions quickly. AI can identify patterns in species distribution, predict invasion potential, and assess ecological impacts. Additionally, these tools facilitate collaboration among policymakers, scientists, and conservationists, ensuring that strategies are evidence-based and tailored to local contexts. Effective implementation requires considering socio-economic factors and stakeholder engagement. As climate change continues to alter ecosystems, AI-driven systems can adapt to new challenges, promoting resilience. By harnessing technology, we can develop proactive strategies that balance human interests with ecological health, ultimately leading to sustainable environmental management. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI-Driven Predictive Analytics for Sustainable Restaurant Operations and Waste Minimization
There is mounting pressure in the restaurant business to minimize the waste of their operations and use of resources, and still be able to make a profit. Unreasonable forecasting, over-procurement, and poor management of resources are the key causes of environmental and financial waste. As a potential solution to the issue, it presents an AI-based Predictive Analytics Framework (AID-PAF), which combines both a Temporal-Fusion Neural Architecture (TFNA), which is an asset demand prediction framework, and a waste-conscious linear programming model used to solve an inventory and resources allocation problem. Real restaurant operational datasets were used to test the system in a hybrid AnyLogic-MATLAB simulator. Experimental findings show that the proposed framework achieved 40%, 18%, 15%, and 21% reductions in the food waste, energy use, water use, and costs, respectively, and in addition enhanced the accuracy of the forecast, MAPE of 6.5%, the customer fill-rate 96.2%, and the Sustainability Score 78.7. The results prove that predictive analytics based on AI can greatly contribute to the sustainability, efficiency, and profitability of restaurant operations by making intelligent decisions with the assistance of data. 2025 IEEE. -
AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities
Aim and Purpose: This chapter explores the crucial role of artificial intelligence (AI) in enabling real-time decision-making at the edge, particularly within data-intensive applications. It aims to identify and address fundamental challengessuch as latency, limited bandwidth, and scalabilitythat frequently hinder the efficient deployment of AI models near data sources. The objective is to propose a coherent and implementable framework to mitigate these obstacles, thereby facilitating the development of intelligent, responsive systems. The chapter emphasizes the transformative potential of edge AI across three key sectors: healthcare, manufacturing, and smart cities, illustrating how localized intelligence can enhance performance, efficiency, and autonomy in time-sensitive environments. Methodology: We adopt a comprehensive methodological approach that includes studying optimization techniques such as model compression, quantization, and distributed inference. Special attention is given to federated learning, which supports collaborative training without the need to transfer raw dataenhancing both privacy and scalability. The examination of edge-optimized hardware accelerators (e.g., NPUs, FPGAs) and streamlined software frameworks will highlight their role in overcoming processing bottlenecks and ensuring low-latency performance. Limitations: Despite the promise of edge AI, challenges persist. These include limited processing and energy resources, security vulnerabilities, and device heterogeneity. Managing updates and maintaining consistency across distributed systems complicate widespread implementation further. Applications and Novelty: This chapters novelty lies in its integrated focus on practical, real-world applications of edge AI in healthcare, manufacturing, and smart cities. By presenting targeted solutions to known constraints, it contributes a practical, implementation-ready perspective to the growing body of edge AI research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-driven service marketing in accessible tourism: Digitizing hampi for all
This book chapter, which focuses on the historic site of Hampi, India, investigates how artificial intelligence (AI) might be included into service marketing to improve accessible tourism. Hampi presents special accessibility issues because it is a UNESCO World Heritage Site, especially for those with impairments. In addition to introducing AI-driven technologies like machine learning, natural language processing, and augmented reality and discussing how they might be used to enhance tourist experiences at Hampi, the chapter also examines current trends and difficulties in accessible tourism. A focus on personalized service delivery, increased tourist engagement, and inclusivity, case studies and examples from international practices demonstrate effective AI implementations in accessible tourism. In the end, this chapter offers perspectives and suggestions for utilizing AI-driven service marketing to promote universal accessibility and digitize Hampi, improving visitors' encounters with the local cultural heritage. 2025, IGI Global Scientific Publishing. -
AI-Driven Stacking Ensemble for Predicting Total Power Output of Wave Energy Converters: A Data-Driven Approach to Renewable Energy Processes
This study develops and evaluates an AI-driven stacked hybrid machine learning model for predicting the total power output of wave energy converters (WECs) across four Australian coastal locations: Adelaide, Perth, Sydney, and Tasmania. This research enhances prediction accuracy through advanced ensemble learning techniques while addressing spatial variability in wave energy processes. The dataset comprises spatial coordinates and power output readings from 16 fully submerged WECs per location, capturing the variability of wave energy across different coastal regions. Data preprocessing included missing value imputation, duplicate removal, and spatial feature transformation via Euclidean distance calculation. Principal component analysis (PCA) was employed to reduce dimensionality while preserving critical features influencing power generation. To develop an accurate prediction model, we employed a stacking ensemble approach using XGBoost, LightGBM, and CatBoost as base learners, optimized via Optuna hyperparameter tuning with 10-fold cross-validation. A Ridge regression meta-learner combined the outputs of these models, leveraging their complementary strengths to enhance predictive performance. Experimental results demonstrate that the hybrid model consistently outperforms individual models, enhancing predictive accuracy across all locations. Sydney exhibited the highest accuracy (RMSE = 9089.58 W, R2 = 0.8576), while Tasmania posed the greatest challenge (RMSE = 45,032.37 W, R2 = 0.8378). The ensemble approach mitigated overfitting and improved generalization by leveraging the complementary strengths of XGBoost, LightGBM, and CatBoost. By leveraging AI-driven ensemble learning, this study provides a scalable and reliable framework for wave energy forecasting, facilitating more efficient grid integration and resource planning in renewable energy systems. 2025 by the authors.