Large Language Models (LLMs) are increasingly deployed in web-based medical advice applications, offering scalable and accessible healthcare solutions. However, their outputs often reflect demographic biases, raising concerns about fairness and equity for vulnerable populations. In this work, we propose FairMed, a framework designed to mitigate biases in LLM-generated medical advice through fine-tuning and prompt engineering strategies. We evaluate FairMed using language-based and content-level metrics across demo…

The current study suggests the national framework based on the concept of AI-enabled cyber threat intelligence systems. The increased risk of digital warfare and the shortcomings of the traditional patterns of cybersecurity. The use of artificial intelligence in the early warning of such threats, real-time response, and superior data analysis. It is a crucial concern to national security. The study will determine the contribution of AI-driven CTIS to the improvement of cyber defense capabilities in government and …

Brief Summary of the Research: This investigation studies AI-driven Learning Management Systems which function as educational centers to support employee training at Business Analyst and Quality Assurance levels on projects based in the United States. The research uses Natural Language Processing (NLP), predictive analytics with adaptive learning algorithms to discover methods which resolve expertise deficits and strengthen connections while adhering to industry standards that evolve over time.  Purpose of the St…

<title>Abstract</title> Fairness in machine learning systems is essential for building trustworthy, ethical, and socially responsible AI, particularly in high-stakes domains such as healthcare and human services. This study proposes a comprehensive fairness-preserving framework integrating data bias quantification with model-level fairness evaluation and eliminating its violation. The framework uses Earth Mover’s Distance to quantify the distributional discrepancy between subgroups and the overall population, prov…

Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non…