Publications Scientifiques

Now showing 1 - 6 of 6

Deep Learning Models to Analyze Sentiments of People Regarding New Vaccines
(Institute of Electrical and Electronics Engineers, 2025) Khoudi, Asmaa; Draoui, Yasmine; Aoutou, Nadjet
The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic
Machine learning in the medical field: A comprehensive overview
(Institute of Electrical and Electronics Engineers Inc, 2023) Belgacem, Ali; Khoudi, Asmaa; Boudane, Fatima; Berrichi, Ali
Machine learning utilization in medicine has increased interest over the last few years. With its impressive results in treating diseases and medical conditions, it will be important to understand and analyze how the scientific community has used it. Thus, opening up space for new research and opportunities in medicine. The objective of this study is to review the literature on machine learning applications in the medical sector. Therefore, we conducted an extensive research by reviewing recent studies and surveys on machine-learning health solutions. As a result, we offer, in this paper, a fresh study affirming the foundations and necessities of a machine learning application in the medical field. We also provide a breakdown of current research trends, which highlights future research opportunities.
Branch and bound algorithm for identical parallel machine scheduling problem to maximise system availability
(Inder Science, 2020) Khoudi, Asmaa; Berrichi, Ali
In the majority of production scheduling studies, the objective is to minimise a criterion which is generally, function of completion times of production jobs. However, for some manufacturing systems, the reliability/availability of machines can be the most important performance criteria towards decision makers. In this paper, we deal with a production scheduling problem on identical parallel machines and the objective is to find the best assignment of jobs on machines maximising the system availability. We assume that the production system can be subject to potentially costly failures then PM actions are performed at the end of production jobs. We have proposed a branch and bound algorithm, dominance rules and an efficient upper bound to solve the proposed model optimally. Computational experiments are carried out on randomly generated test problems and results show the efficiency of the proposed upper bound and dominance rules. [Submitted 23 December 2016; Accepted 27 October 2018]
Minimize total tardiness and machine unavailability on single machine scheduling problem: bi-objective branch and bound algorithm
(Springer, 2018) Khoudi, Asmaa; Berrichi, Ali
The joint production scheduling and preventive maintenance problems have recently attracted researchers’ attention given their contribution, both the production and the maintenance functions and their integration, to the firms’ efficiency. In this paper, we deal with production scheduling and preventive maintenance (PM) planning on single machine problem. The aim is to find an appropriate sequencing of production jobs and a PM planning to minimize two objectives simultaneously: total tardiness of jobs and machine unavailability. We propose a bi-objective exact algorithm, that we called BOBB, based on bi-objective branch and bound method to find the efficient set. We introduced several properties and bound sets to enhance the performance of the proposed BOBB algorithm. Furthermore, we propose a hybrid method, that we called GA-BBB, based on genetic algorithm and binary branch and bound algorithm to compute an approximate efficient set to be used as an initial upper bound set in the BOBB algorithm. An experimental study was conducted to show the efficiency of the GA-BBB and the BOBB algorithms
Minimize total tardiness and machine unavailability on single machine scheduling problem : bi-objective branch and bound algorithm
(Springer, 2017) Khoudi, Asmaa; Berrichi, Ali
Heuristics to maximize system availability on parallel machine scheduling problem
(IEEE, 2015) Khoudi, Asmaa; Berrichi, Ali; Yalaoui, Farouk

Publications Scientifiques

Browse

Filters

Settings

Sort By

Results per page

Search Results