Artificial Neural Networks Interpretation Using LIME for Breast Cancer Diagnosis. - Hajar Hakkoum; Ali Idri; Ibtissam Abnane (WorldCIST (3), 2020)

Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification. - Hajar Hakkoum; Ali Idri; Ibtissam Abnane (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2021)

Evaluating Interpretability of Multilayer Perceptron and Support Vector Machines for Breast Cancer Classification. - Hajar Hakkoum; Ibtissam Abnane; Ali Idri (AICCSA, 2022)

A Systematic Map of Interpretability in Medicine. - Hajar Hakkoum; Ibtissam Abnane; Ali Idri (HEALTHINF, 2022)

Interpretability in the medical field: A systematic mapping and review study. - Hajar Hakkoum; Ibtissam Abnane; Ali Idri (Appl. Soft Comput., 2022)

Ensemble blood glucose prediction in diabetes mellitus: A review. - Mohamed Zaim Wadghiri; Ali Idri; Touria El Idrissi; Hajar Hakkoum (Comput. Biol. Medicine, 2022)

Does Categorical Encoding Affect the Interpretability of a Multilayer Perceptron for Breast Cancer Classification? - Hajar Hakkoum; Ali Idri; Ibtissam Abnane; José Luis Fernades-Aleman (DATA, 2023)

Explainability of MLP Based Species Distribution Models: A Case Study. - Imane El Assari; Hajar Hakkoum; Ali Idri (ICAART (3), 2023)

A Comparative Study on the Impact of Categorical Encoding on Black Box Model Interpretability. - Hajar Hakkoum; Ali Idri (DATA, 2024)

Assessing the effectiveness of dimensionality reduction on the interpretability of opaque machine learning-based attack detection systems. - Houssam Zouhri; Ali Idri; Hajar Hakkoum (Comput. Electr. Eng., 2024)

Global and local interpretability techniques of supervised machine learning black box models for numerical medical data. - Hajar Hakkoum; Ali Idri; Ibtissam Abnane (Eng. Appl. Artif. Intell., 2024)