The digitalization of public procurement presents a critical opportunity to enhance governmental efficiency, transparency, and supply chain performance. However, integrating artificial intelligence into public sector contracting is frequently bottlenecked by legacy institutional architectures and static data silos. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for digital procurement. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests vendor performance telemetry, bidding metadata, and supply chain friction indicators. The system translates these inputs into a dynamic Vendor Integrity and Performance Score, instantly identifying procurement anomalies and corruption risks. Preliminary architectural evaluations demonstrate that decoupling the AI evaluation layer from legacy government databases significantly reduces the latency of contract auditing, providing public management researchers with a deterministic, highly scalable tool for analyzing digital procurement and institutional performance.
The integration of the Internet of Things and Artificial Intelligence into global supply chains presents significant opportunities for operational optimization. However, the computational intensity of traditional machine learning models frequently undermines corporate sustainabil…
Artificial intelligence (AI), machine learning, and explainable AI (XAI) techniques are transforming employee performance evaluation from subjective, periodic appraisals into data-driven, continuous, and transparent systems. While individual technologies offer significant benefit…
The application of machine learning in healthcare presents unprecedented opportunities for optimizing hospital flow and mitigating surgical delays. However, the deployment of clinical decision support systems is frequently bottlenecked by the fragmented, unstructured nature of El…
Global supply chains have been subjected to an unprecedented sequence of shocks over the past several years, from the COVID-19 pandemic to geopolitical conflict, trade policy uncertainty, and extreme-weather events, exposing the fragility of lean, just-in-time operating models an…
Code and result data accompanying a manuscript on AI-driven anomaly detection and post-quantum-secured recovery for the Internet of Medical Things (IoMT), currently under peer review. Includes the leakage-audited anomaly detector, the digital-twin-gated recovery simulation with c…
Abstract—Abstract—Accurate estimation of health insurance premiums is a complex task due to the nonlinear interaction of demographic, lifestyle, and medical factors. Conventional actuarial models rely on generalized risk pools and limited explanatory variables, often resulting in…