12 citations · 29 across the 10 of their papers we have counts for
5 papers · 1 filter
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
Jordan F. Masakuna, DJeff Kanda Nkashama, Arian Soltani +3
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly un…
Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities
Jean Marie Tshimula, Mitterrand Kalengayi, Dieumerci Makenga +28
Artificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limite…
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani +4
Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learni…
A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs
Christian N. Mayemba, D'Jeff K. Nkashama, Jean Marie Tshimula +12
This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns…
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
Maxime Alvarez, Jean-Charles Verdier, D'Jeff K. Nkashama +3
Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable al…