Steenwinckel, Bram

FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., Dimou, A., Van Den Bossche, B., De Turck, F., Van Hoecke, S., et al. (2021) · Future Generation Computer Systems

Fault Detection and Control SystemsAnomaly Detection Techniques and Applications

Cited by 102

INK: knowledge graph embeddings for node classification

Steenwinckel, B., Vandewiele, G., Weyns, M., Agozzino, T., Turck, F. D., & Ongenae, F. (2022) · Data Mining and Knowledge Discovery

Advanced Graph Neural NetworksSemantic Web and Ontologies

Cited by 35

mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients

De Brouwer, M., Vandenbussche, N., Steenwinckel, B., Stojchevska, M., Van Der Donckt, J., Degraeve, V., Vaneessen, J., De Turck, F., Volckaert, B., Boon, P., et al. (2022) · BMC Medical Informatics and Decision Making

Migraine and Headache StudiesArtificial Intelligence in Healthcare

Cited by 17

Explainable knowledge graph embeddings for industrial process monitoring & control

Weyns, M., Blyau, T., Steenwinckel, B., De Turck, F., Van Hoecke, S., & Ongenae, F. (2025) · Information Fusion

Fault Detection and Control SystemsAdvanced Graph Neural Networks

Cited by 7

Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs

Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management

Software System Performance and ReliabilityAnomaly Detection Techniques and Applications

Cited by 6