Clinton Amponsah

Modelling a dataset-defined skill-retention outcome in generative AI-assisted learning: An exploratory machine-learning and explainability study

Generative artificial intelligence (GenAI) is increasingly used in higher education, but durable learning cannot be inferred from task completion alone. This study presents an exploratory machine-learning benchmark using a public dataset of 50,000 student-like records that is treated here as synthetic/engineered becaus...

Who bears the cost of fairness? Empirical evidence from a cross-regional bias detection and mitigation pipeline in machine learning

Introduction: Machine learning systems deployed across socioeconomically and geopolitically heterogeneous regions frequently generate unequal error distributions while still appearing compliant under aggregate evaluation metrics. This creates major governance concerns for high-stakes applications such as credit scoring...

Runtime detection of attacks and misconfigurations in cloud-native Kubernetes environments using eBPF network telemetry

Safety-aware multi-robot coordination in industrial cyber-physical systems using potential fields and handcrafted communication

The findings demonstrate the limitations of the current heuristic integration and support further research on dynamic task assignment, controlled component evaluation, stronger collision-avoidance mechanisms, learned communication, and end-to-end MARL training.

Toward Transparent Intrusion Detection Systems: An Explainable Ai Approach For Network Security

The growth in use of machine-learning based intrusion detection systems (IDS), however, also raises critical issues of transparency, trust, and accountability due to the fact that most of the top performing models are "black box" models. Lack of ability to provide explanation of detection decisions severely limits the...

Communication-Efficient Networking for Distributed Agentic AI: A Systematic Review of Protocols, Coordination, Latency and Energy Consumption

This systematic review synthesizes peer-reviewed studies published between 2023 and 2026 on communication-efficient networking for distributed agentic AI, multi-agent reinforcement learning and networked autonomous systems concludes that communication efficiency should be treated as a joint optimization problem involvi...