Ines Ben Kraiem

Orcid: 0000-0001-5735-289X

According to our database1, Ines Ben Kraiem authored at least 10 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2025
Enhancing Cloud Cost Forecasting with Explainable Artificial Intelligence.
Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence, 2025

2023
An IT projects' conceptual model to facilitate upstream decision-making: project management method selection.
Int. Trans. Oper. Res., November, 2023

A Comparative Study of Machine Learning Algorithm for Predicting Project Management Methodology.
Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 27th International Conference KES-2023, 2023

2022
GreenWebAdvisor: Discovering and Recommending Hidden Eco-Design' Best-Practices.
Proceedings of the International Conference on Computational Science and Computational Intelligence, 2022

2021
Détection d'Anomalies Multiples par Apprentissage Automatique de Règles dans les Séries Temporelles. (Detection of Multiple Anomalies by The Automatic Learning of Rules in Time Series).
PhD thesis, 2021

Human-Interpretable Rules for Anomaly Detection in Time-Series.
Proceedings of the 24th International Conference on Extending Database Technology, 2021

2020
Automatic Classification Rules for Anomaly Detection in Time-Series.
Proceedings of the Research Challenges in Information Science, 2020

2019
Méthode à base de patterns pour la détection d'anomalies]{Méthode basée sur les patterns pour la détection simultanée d'anomalies multiples dans les réseaux de capteurs.
Proceedings of the Actes du XXXVIIème Congrès INFORSID, Paris, France, June 11-14, 2019., 2019

CoRP: A Pattern-Based Anomaly Detection in Time-Series.
Proceedings of the Enterprise Information Systems - 21st International Conference, 2019

Pattern-based Method for Anomaly Detection in Sensor Networks.
Proceedings of the 21st International Conference on Enterprise Information Systems, 2019


  Loading...