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Hajar Hakkoum
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2020 – today
- 2024
- [j5]Houssam Zouhri
, Ali Idri
Assessing the effectiveness of dimensionality reduction on the interpretability of opaque machine learning-based attack detection systems. Comput. Electr. Eng. 120: 109627 (2024) - [j4]Hajar Hakkoum
Global and local interpretability techniques of supervised machine learning black box models for numerical medical data. Eng. Appl. Artif. Intell. 131: 107829 (2024) - [c6]Hajar Hakkoum, Ali Idri:
A Comparative Study on the Impact of Categorical Encoding on Black Box Model Interpretability. DATA 2024: 384-391 - 2023
- [c5]Hajar Hakkoum, Ali Idri, Ibtissam Abnane, José Luis Fernades-Aleman:
Does Categorical Encoding Affect the Interpretability of a Multilayer Perceptron for Breast Cancer Classification? DATA 2023: 351-358 - [c4]Imane El Assari, Hajar Hakkoum, Ali Idri:
Explainability of MLP Based Species Distribution Models: A Case Study. ICAART (3) 2023: 690-697 - 2022
- [j3]Hajar Hakkoum
Interpretability in the medical field: A systematic mapping and review study. Appl. Soft Comput. 117: 108391 (2022) - [j2]Mohamed Zaim Wadghiri
Ensemble blood glucose prediction in diabetes mellitus: A review. Comput. Biol. Medicine 147: 105674 (2022) - [c3]Hajar Hakkoum, Ibtissam Abnane, Ali Idri:
Evaluating Interpretability of Multilayer Perceptron and Support Vector Machines for Breast Cancer Classification. AICCSA 2022: 1-6 - [c2]Hajar Hakkoum
A Systematic Map of Interpretability in Medicine. HEALTHINF 2022: 719-726 - 2021
- [j1]Hajar Hakkoum
Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 9(6): 587-599 (2021) - 2020
- [c1]Hajar Hakkoum
Artificial Neural Networks Interpretation Using LIME for Breast Cancer Diagnosis. WorldCIST (3) 2020: 15-24
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last updated on 2024-10-04 20:59 CEST by the dblp team
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