@article {ElHaddaoui2022529,
	title = {On the Sensitivity of LSTMs to Hyperparameters and Word Embeddings in the Context of Sentiment Analysis},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {529-542},
	abstract = {Recurrent neural networks are still providing excellent results in sentiment analysis tasks, variants such as LSTM and Bidirectional LSTM have become a reference for building fast and accurate predictive models. However, such performance is difficult to obtain due to the complexity of the models and the hyperparameters choice. LSTM based models can easily overfit to the studied domain, and tuning the hyperparameters to get the desired model is the keystone of the training process. In this work, we provide a study on the sensitivity of a selection of LSTM based models to various hyperparameters and we highlight important aspects to consider while using similar models in the context of sentiment analysis. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_40},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135093996\&doi=10.1007\%2f978-3-031-07969-6_40\&partnerID=40\&md5=695a2003d02e28d6d72df06cca749381},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
@article {Rachih202243,
	title = {Simulation optimization of an inventory control model for a reverse logistics system},
	journal = {Decision Science Letters},
	volume = {11},
	number = {1},
	year = {2022},
	note = {cited By 3},
	pages = {43-54},
	abstract = {Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena{\textregistered} was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest{\textregistered} optimization tool. {\textcopyright} 2022 by the authors; doi:.},
	doi = {10.5267/j.dsl.2021.9.001},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122133768\&doi=10.5267\%2fj.dsl.2021.9.001\&partnerID=40\&md5=d46c9884d1a5aa28b8b6d404febf6820},
	author = {Rachih, H. and Mhada, F.Z. and Chiheb, R.}
}
@article {ElHaddaoui2021336,
	title = {Sentiment analysis: A review and framework foundations},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {4},
	year = {2021},
	note = {cited By 0},
	pages = {336-355},
	abstract = {The rise of social media as a platform for opinion expression and social interactions motivated the need for an automated data analysis technique for business value extraction with optimal investment considerations. In this respect, sentiment analysis (SA) becomes the de facto approach to investigate generated data and retrieve information such as sentiments and emotions, discussed topics, etc., via traditional machine learning and modern neural network-based algorithms. The current techniques achieve reasonable accuracy scores but their performance evolution is depending on the context of application, also most implementations are complex and non-reusable components. Our literature review shows a lack in research studies to unify existing systems under a common framework for SA tasks. This paper also highlights the rending movement of neural networks approaches and pinpoint recent research studies for SA sub tasks. A SA framework design proposition is presented based on key research projects and enhanced with other promising works. {\textcopyright} 2021 Inderscience Publishers. All rights reserved.},
	doi = {10.1504/IJDATS.2021.120112},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122875189\&doi=10.1504\%2fIJDATS.2021.120112\&partnerID=40\&md5=e3d2033b898f101c2ec5d087fdf6472e},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
@conference {Afia2019512,
	title = {A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification},
	booktitle = {Procedia Computer Science},
	volume = {148},
	year = {2019},
	note = {cited By 0},
	pages = {512-521},
	doi = {10.1016/j.procs.2019.01.024},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062665945\&doi=10.1016\%2fj.procs.2019.01.024\&partnerID=40\&md5=7a6b9cd9dfff11052107df70b4b828f0},
	author = {Afia, A.E. and Lalaoui, M. and Chiheb, R.}
}
@conference {Lalaoui20181,
	title = {A self-adaptive very fast simulated annealing based on Hidden Markov model},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-8},
	doi = {10.1109/CloudTech.2017.8284698},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046690728\&doi=10.1109\%2fCloudTech.2017.8284698\&partnerID=40\&md5=019d60ddd6cdf20019791974755a6d33},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@article {Lalaoui2018291,
	title = {A self-tuned simulated annealing algorithm using hidden markov model},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {8},
	number = {1},
	year = {2018},
	pages = {291-298},
	doi = {10.11591/ijece.v8i1.pp291-298},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923643\&doi=10.11591\%2fijece.v8i1.pp291-298\&partnerID=40\&md5=804e27501baca7ca4efeeaa3d1ff0ef1},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@article {Lalaoui2018291,
	title = {A self-tuned simulated annealing algorithm using hidden markov model},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {8},
	number = {1},
	year = {2018},
	note = {cited By 0},
	pages = {291-298},
	abstract = {Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one. {\textcopyright} 2018 Institute of Advanced Engineering and Science.},
	doi = {10.11591/ijece.v8i1.pp291-298},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923643\&doi=10.11591\%2fijece.v8i1.pp291-298\&partnerID=40\&md5=804e27501baca7ca4efeeaa3d1ff0ef1},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@article {Boudad20182479,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	volume = {9},
	number = {4},
	year = {2018},
	pages = {2479-2490},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@article {Boudad2017,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	year = {2017},
	note = {cited By 0; Article in Press},
	abstract = {Within the last couple of years, Sentiment Analysis in Arabic has gained a considerable interest from the research community. In this respect, the objective of this paper is to provide a review of the major works that have dealt with this research area in this language. A thorough investigation of the available literature revealed that the works were mainly concentrated on dealing with specific Sentiment Analysis tasks. To this end, they used three different approaches, namely supervised, unsupervised and hybrid. The results that these studies achieved are interesting but divergent. This divergence is relatively due to the type of approach opted for, the task that is being analysed as well as to the specificities and intricacies of the Arabic variety understudy. {\textcopyright} 2017 Ain Shams University.},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@article {Boudad2017233,
	title = {Sentiment classification of Arabic tweets: A supervised approach},
	journal = {Journal of Mobile Multimedia},
	volume = {13},
	number = {3-4},
	year = {2017},
	note = {cited By 0},
	pages = {233-243},
	abstract = {Social media platforms have proven to be a powerful source of opinion sharing. Thus, mining and analyzing these opinions has an important role in decision-making and product benchmarking. However, the manual processing of the huge amount of content that these web-based applications host is an arduous task. This has led to the emergence of a new field of research known as Sentiment Analysis. In this respect, our objective in this work is to investigate sentiment classification in Arabic tweets using machine learning. Three classifiers namely Na{\"I}ve Bayes, Support Vector Machine and K-Nearest Neighbor were evaluated on an in-house developed dataset using different features. A comparison of these classifiers has revealed that Support Vector Machine outperforms others classifiers and achieves a 78\% accuracy rate. {\textcopyright} Rinton Press.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040241035\&partnerID=40\&md5=76bc92b38d241173585a11bc74ae14d7},
	author = {Boudad, N. and Faizi, R. and Thami, R.O.H. and Chiheb, R.}
}
@article {Faizi201532,
	title = {Students{\textquoteright} perceptions towards using Web 2.0 technologies in education},
	journal = {International Journal of Emerging Technologies in Learning},
	volume = {10},
	number = {6},
	year = {2015},
	note = {cited By 0},
	pages = {32-36},
	abstract = {The purpose of this paper is to evaluate the usefulness of Web 2.0 technologies in education. For this purpose, a survey was carried out to explore the students{\textquoteright} perceptions towards using these tools for learning purposes. Results of the research study revealed that all the respondents are greatly immersed in these social platforms and use them for many reasons. However, it was found out that almost half of the surveyed students (i.e. 47\%) devote more than 40\% of the time they spend on Web 2.0 technologies to enhance their learning in different subjects. Taking these findings into consideration, we can stipulate that Web 2.0 applications present many educational advantages for students, hence, contribute in providing opportunities for further learning. Consequently, these online tools provide schools and universities with more opportunities to go beyond traditional delivery formats and develop learnercentered personalized learning environments.},
	doi = {10.3991/ijet.v10i6.4858},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952788796\&doi=10.3991\%2fijet.v10i6.4858\&partnerID=40\&md5=c22befa5ff5f3e9fcbbfe17e466aeee9},
	author = {Faizi, R. and Chiheb, R. and El Afia, A.}
}
@article {Faizi201464,
	title = {Social media: An optimal virtual environment for learning foreign languages},
	journal = {International Journal of Emerging Technologies in Learning},
	volume = {9},
	number = {5},
	year = {2014},
	note = {cited By 3},
	pages = {64-66},
	abstract = {The present paper aims at exploring the potential role that social media technologies play in learning foreign languages. For this purpose, a survey was carried out to examine students{\textquoteright} and language learners{\textquoteright} perceptions and attitudes about using these platforms. Results of the research study revealed that the great majority of the respondents actually use these web-based applications to enhance their language skills. Most importantly, they noted that social media contribute in improving their listening, reading, speaking and writing skills. Accordingly, we strongly recommend that instructors use these online tools in distant, blended, or face-to-face language learning settings.},
	doi = {10.3991/ijet.v9i5.3911},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907689668\&doi=10.3991\%2fijet.v9i5.3911\&partnerID=40\&md5=22bc806cdbaebe7f28f0b9664aa83c8f},
	author = {Faizi, R. and El Afia, A. and Chiheb, R.}
}
