Publications Scientifiques
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Item Interpreting NAS-Optimized Transformer Models for Remaining Useful Life Prediction Using Gradient Explainer(Warszawa: Polskie Towarzystwo Informatyczne, 2025) Nekkaa, Messaouda; Abdouni, Mohamed; Boughaci, DalilaRemaining Useful Life (RUL) estimation of complex machinery is critical for optimizing maintenance schedules and preventing unexpected failures in safety-critical systems. While Transformer architecture has recently achieved state-of-the-art performance on RUL benchmarks, their design often relies on expert tuning or costly Neural Architecture Search (NAS), and their predictions remain opaque to end users. In this work, we integrate a Transformer whose hyperparameters were discovered via evolutionary NAS with a gradient-based explainability method to deliver both high accuracy and transparent, perprediction insights. Specifically, we adapt the Gradient Explainer algorithm to produce global and local importance scores for each sensor in the C-MAPSS FD001 turbofan dataset. Our analysis shows that the sensors identified as most influential, such as key temperature and pressure measurements, match domain-expert expectations. By illuminating the int ernal decision process of a complex, NAS-derived model, this study paves the way for trustworthy adoption of advanced deep-learning prognostics in industrial settings.Item Application of supervised machine learning methods on the multidimensional knapsack problem(Springer, 2021) Rezoug, Abdellah; Bader-el-den, Mohamed; Boughaci, DalilaMachine Learning (ML) has gained much importance in recent years as many of its effective applications are involved in different fields, healthcare, banking, trading, gaming, etc. Similarly, Combinatorial Optimisation (CO) keeps challenging researchers by new problems with more complex constraints. Merging both fields opens new horizons for development in many areas. This study investigates how effective is to solve CO problems by ML methods. The work considers the Multidimensional Knapsack Problem (MKP) as a study case, which is an np-hard CO problem well-known for its multiple applications. The proposed approach suggests to use solutions of small-size MKP to build models with different ML methods; then, to apply the obtained models on large-size MKP to predict their solutions. The features consist of scores calculated based on information about items while the labels consist of decision variables of optimal solutions calculated from applying CPLEX Solver on small-size MKP. Supervised ML methods build models that help to predict structures of large-size MKP solutions and build them accordingly. A comparison of five ML methods is conducted on standard data set. The experiments showed that the tested methods were able to reach encouraging results. In addition, the study proposes a Genetic Algorithm (GA) that exploits ML outputs essentially in initialisation operator and to repair unfeasible solutions. The algorithm denoted GaPR explores the ML solution neighbourhood as a way of intensification to approach optimal solutions. The carried out experiments indicated that the approach was effective and competitiveItem Guided genetic algorithm for the multidimensional knapsack problem(Springer, 2017) Rezoug, Abdellah; Bader-El-Den, Mohamed; Boughaci, DalilaItem Memetic Algorithm for Solving the 0-1 Multidimensional Knapsack Problem(SPRINGER, 2015) Rezoug, Abdellah; Boughaci, Dalila; Badr-El-Den, MohamedIn this paper, we propose a memetic algorithm for the Multidimensional Knapsack Problem (MKP). First, we propose to combine a genetic algorithm with a stochastic local search (GA-SLS), then with a simulated annealing (GA-SA). The two proposed versions of our approach (GA-SLS and GA-SA) are implemented and evaluated on benchmarks to measure their performance. The experiments show that both GA-SLS and GA-SA are able to find competitive results compared to other well-known hybrid GA based approaches.Item A memetic algorithm with support vector machine for feature selection and classification(Springer, 2015) Nekkaa, Messaouda; Boughaci, DalilaItem Hybrid harmony search combined with stochastic local search for feature selection(Springer, 2015) Nekkaa, Messaouda; Boughaci, Dalila