A Systematic Literature Review of Machine Learning Applications for Port's Operations. - Sara El Mekkaoui; Loubna Benabbou; Abdelaziz Berrado (GOL, 2020)

Predicting Ships Estimated Time of Arrival based on AIS Data. - Sara El Mekkaoui; Loubna Benabbou; Abdelaziz Berrado (SITA, 2020)

Neural Networks based Conformal Prediction for Pipeline Structural Response. - Sara El Mekkaoui; Carla J. Ferreira; Juan Camilo Guevara Gómez; Christian Agrell; Nicholas James Vaughan; Hans Olav Heggen (COPA, 2023)

Extension of Regression Tsetlin Machine for Interpretable Uncertainty Assessment. - Kuruge Darshana Abeyrathna; Sara El Mekkaoui; L. Yi Edward; Andreas Hafver; Ole-Christoffer Granmo (RuleML+RR, 2023)

Deep Learning Approaches for Vessel Estimated Time of Arrival Prediction: A Case Study on the Saint Lawrence River. - Ismail Bourzak; Sara El Mekkaoui; Abdelaziz Berrado; Stéphane Caron; Loubna Benabbou (SITA, 2023)

Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review. - Sara El Mekkaoui; Loubna Benabbou; Abdelaziz Berrado (CoRR, 2023)

The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification. - Kuruge Darshana Abeyrathna; Sara El Mekkaoui; Andreas Hafver; Christian Agrell (ICAAI, 2024)

Enhancing Assurance of AI-enabled Systems through Uncertainty Quantification. - Sara El Mekkaoui; Christian Agrell; Andreas Hafver; Harmen Dijkstra (ICAAI, 2024)