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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Supply Chain Management
Azmani, Abdellah
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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Explainable machine learning models applied to predicting customer churn for e-commerce Boukrouh, Ikhlass; Azmani, Abdellah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp286-297

Abstract

Precise identification of customer churn is crucial for e-commerce companies due to the high costs associated with acquiring new customers. In this sector, where revenues are affected by customer churn, the challenge is intensified by the diversity of product choices offered on various marketplaces. Customers can easily switch from one platform to another, emphasizing the need for accurate churn classification to anticipate revenue fluctuations in e-commerce. In this context, this study proposes seven machine learning classification models to predict customer churn, including decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), k-nearest neighbors (K-NN), and artificial neural network (ANN). The performances of the models were evaluated using confusion matrix, accuracy, precision, recall, and F1-score. The results indicated that the ANN model achieves the highest accuracy at 92.09%, closely followed by RF at 91.21%. In contrast, the NB model performed the least favorably with an accuracy of 75.04%. Two explainable artificial intelligence (XAI) methods, shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), were used to explain the models. SHAP provided global explanations for both ANN and RF models through Kernel SHAP and Tree SHAP. LIME, offering local explanations, was applied only to the ANN model which gave better accuracy.
Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection Tayalati, Faouzi; Idiri, Soulaimane; Boukrouh, Ikhlass; Azmani, Abdellah; Azman, Monir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp270-285

Abstract

Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process. 
Proposal Decision Support Process for Urban Goods Delivery Atik El Ftouh, Mouna; Azmani, Abdellah; Fourka, Mohamed
International Journal of Supply Chain Management Vol 8, No 2 (2019): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v8i2.2185

Abstract

This paper proposes a collaborative process to handle demands received by a carrier for urban goods delivery.The purpose of this article is to provide support for carriers analyzing the demands,physical resources,human resources,risks and profitability,in order to decide processing internally or externally a goods delivery demand or rejecting it.Such a process,called CUFP(Collaborative Urban Freight Process),is based on an analysis of urban movements of goods,divided into four stages:Check out of extreme conditions,feasibility study,exploitation study and execution.
A bibliometric analysis of feature selection techniques: trends, innovations, and future directions Semmar, Oumaima; El Habti, Wissal; Wilson, Donalson; Azmani, Abdellah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4403-4414

Abstract

Feature selection techniques have become increasingly important in addressing the challenges of high dimensionality in machine learning and other artificial intelligence domains. In this study, we present a comprehensive bibliometric analysis of research on feature selection techniques over the past decade, focusing on mapping the intellectual structure, identifying emerging trends, and highlighting productive collaborations in the field. Using merged data from Scopus and Web of Science databases, we collected and analyzed 2,079 relevant documents published between 2014 and 2024, applying citation analysis, co-authorship networks, and keyword co-occurrence mapping. Our findings reveal that feature selection methodologies, including supervised, unsupervised, and hybrid approaches across filter, wrapper, and embedded techniques, have been widely applied across various domains. The authors who have most contributed to the development of these methods are primarily affiliated with institutions in China, India, and the USA. The insights provided by this analysis offer researchers and practitioners a valuable foundation for guiding future research directions in feature selection.
Co-Authors Atik El Ftouh, Mouna Azman, Monir Boukrouh, Ikhlass El Habti, Wissal Fourka, Mohamed Idiri, Soulaimane Semmar, Oumaima Tayalati, Faouzi Wilson, Donalson