Amel Hidouri

Orcid: 0000-0003-1201-8585

According to our database1, Amel Hidouri authored at least 12 papers between 2020 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
SOCXAI: Leveraging CNN and SHAP Analysis for Battery SOC Estimation and Anomaly Detection.
Proceedings of the Computational Science - ICCS 2024, 2024

Enumération des itemsets rares minimaux à partir des bases de données transactionnelles.
Proceedings of the Extraction et Gestion des Connaissances, 2024

2023
Corrigendum to "Mining Closed High Utility Itemsets based on Propositional Satisfiability" [Data Knowl. Eng. 136C (2021) 101927].
Data Knowl. Eng., July, 2023

Targeting Minimal Rare Itemsets from Transaction Databases.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets.
Proceedings of the Information Integration and Web Intelligence, 2023

2022
A Parallel Declarative Framework for Mining High Utility Itemsets.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2022

On the Enumeration of Frequent High Utility Itemsets: A Symbolic AI Approach.
Proceedings of the 28th International Conference on Principles and Practice of Constraint Programming, 2022

2021
Mining Closed High Utility Itemsets based on Propositional Satisfiability.
Data Knowl. Eng., 2021

A Constraint-based Approach for Enumerating Gradual Itemsets.
Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

A Declarative Framework for Mining Top-k High Utility Itemsets.
Proceedings of the Big Data Analytics and Knowledge Discovery, 2021

On Minimal and Maximal High Utility Itemsets Mining using Propositional Satisfiability.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases.
Proceedings of the Big Data Analytics and Knowledge Discovery, 2020


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