International Journal of Advanced Research in Artificial Intelligence and Machine Learning Reviews

AI-Driven IT Management: A Framework for Strategic Implementation and Risk Mitigation

Anushka Shashikant Bhamre — 2026-03-10

Artificial Intelligence (AI) has become a significant influence in IT management, altering strategic decision-making, automation, and risk management processes. The incorporation of AI into IT operations equips organisations with sophisticated tools to enhance efficiency, optimise resources, anticipate threats, and improve service delivery. This research examined the viewpoints of 230 participants, comprising IT professionals, managers, and decision-makers, to investigate the adoption, challenges, and risk mitigation strategies related to AI-driven IT management. The research employed demographic profiling in conjunction with four key dimensions of quantitative data analysis: (1) strategic implementation, (2) operational efficiency, (3) risk mitigation, and (4) organisational impact. The findings indicated that a structured framework that includes employee training, governance mechanisms, and scalable AI infrastructure is essential for effective implementation.

The Global Ethics of Foundation Models Trends, Challenges, and Governance Across Disciplines

Ansari Asma Eram Naushad — 2026-03-10

The rapid adoption of foundation models (FMs) such as large language models, multimodal AI systems, and generative AI has raised critical ethical concerns across multiple disciplines. These models, which can execute a variety of tasks with limited oversight, have presented significant risks such as bias amplification, privacy violations, accountability deficiencies, and disparities in access. This paper examines global patterns of ethical application of core models, identifies SIGNIFICANT challenges, and evaluates approaches to governance across academia, industry, and government. A survey was conducted with 185 participants, including AI practitioners, policymakers, academics, and users, to evaluate their awareness, perspectives, and practices concerning the ethics of foundation models. The results indicate a shared agreement on the need for uniform governance structures, improved transparency, and increased interdisciplinary cooperation to support the ethical deployment of AI technology.

Deep Learning-Based User Behavior Analysis: A Neural Network Approach for Predicting Purchasing Patterns

Swati Paliwal — 2026-05-14

The extent of analysis into user behaviour will concern purchase patterns, increase marketing returns, and foster better interaction with customers. The following research extends a deep learning framework predicated on an artificial neural network (ANN) model for predicting user buying behaviour through demographic and behavioural information. This methodology preprocesses the dataset containing various categorical and numerical features with encoding approaches as well as feature normalisation. The experimental results validate that the suggested model attains an accuracy of 92%, which is the testimony of the strength of the model in detecting patterns of user behaviour. The assessment includes precision, recall, F1 score, and confusion matrix analysis which emphasise the strength of the model in classification. The research indicates that deep learning models may largely contribute to predictive analytics for e-commerce and digital marketing solutions. Future work would include more behavioural signals, explainability methods, and hybrid models to gain accuracy and interpretability.

DOI: https://doi.org/10.24321/3117.4809.202609

AI-Driven Innovations for Genetic Risk Prediction in Healthcare

Saptadip Das — 2026-05-14

This research focuses on the contribution of the new deep AI, ML and NLP technology to raise genetic risk estimation in healthcare. Through integrating genomics with information obtained from other unstructured clinical sources, the research proposes a mixed, relatively accurate genetic disorder risk prediction model. The methodology involves the use of sophisticated techniques owing to the utilisation of advanced algorithms such as deep learning and natural language processing that would analyse massive datasets to provide a wealth of valuable data that, when analysed, would reduce chances of early diagnosis, development of appropriate treatment plans as well as anticipatory healthcare processes. This approach yields even better results than previous models because of the ability to combine data and features from different sources – a new strengthening of the precision medicine paradigm. This work creates the foundation for using AI in the healthcare system to improve patient experiences, decrease diagnostic mistakes and improve treatment plans.

DOI: https://doi.org/10.24321/3117.4809.202611

Medical Insurance Price Prediction Using Xai

N Aravindh Raj — 2026-05-14

The need for clear and precise medical insurance pricing has increased, which has prompted research into sophisticated predictive modelling methods. This paper focuses on Medical Insurance Price Prediction using Explainable Artificial Intelligence (XAI) methods, aiming to provide interpretable insights into premium pricing. A dataset comprising ten critical factors, including age, medical history, chronic diseases, surgeries, and family cancer history,serves as the foundation for the model’s training and evaluation. By utilising machine learning models such as Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGB), we hope to accurately and interpretable forecast premium prices. The implementation of XAI techniques is central to this study. The main factors influencing premium pricing are identified by SHapley Additive exPlanations (SHAP), which quantifies the contribution of each feature to the model’s predictions.Partial Dependency Plots (PDP) and Individual Conditional Expectations (ICE) are also used to visualise feature interactions and offer detailed, instance-specific interpretations. Our findings show how well the suggested models predict insurance premiums and provide useful information about the factors that influence premium prices.This study underscores the importance of XAI in fostering trust and accountability in machine learning applications. By combining predictive accuracy with interpretability, the proposed framework has the potential to aid insurers in fair decision-making and enhance customer satisfaction through transparency in pricing.

DOI: https://doi.org/10.24321/3117.4809.202610