1 citations · 1 across the 6 of their papers we have counts for
7 papers
A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts
Emmanuel K. Katalay, David O. Dimandja, Jordan F. Masakuna
The performance of machine learning (ML) models often deteriorates when the underlying data distribution changes over time, a phenomenon known as data distribution drift. When this…
Enhanced Pruning for Distributed Closeness Centrality under Multi-Packet Messaging
Patrick D. Manya, Eugene M. Mbuyi, Gothy T. Ngoie +1
Identifying central nodes using closeness centrality is a critical task in analyzing large-scale complex networks, yet its decentralized computation remains challenging due to high…
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…
Distributed Identification of Central Nodes with Less Communication
Jordan F. Masakuna, Steve Kroon
This paper is concerned with distributed detection of central nodes in complex networks using closeness centrality. Closeness centrality plays an essential role in network analysis…
Performance-Agnostic Fusion of Probabilistic Classifier Outputs
Jordan F. Masakuna, Simukai W. Utete, Steve Kroon
We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is a…
A Coordinated Search Strategy for Multiple Solitary Robots: An Extension
Jordan F. Masakuna, Simukai W. Utete, Steve Kroon
The problem of coordination without a priori information about the environment is important in robotics. Applications vary from formation control to search and rescue. This paper c…