@article {Lahlou2022568,
	title = {Automatic Detection of Fake News on Twitter by Using a New Feature: User Credibility},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {568-580},
	abstract = {Nowadays, searching news on social media like Twitter or Facebook is something usual. Any internet users can create a lot of content: posts, comments, and they can also redistribute information with retweet option for example. Nevertheless, a large portion of these pieces of news is fake and its main aim is simply to mislead people. In this case, information credibility on social networks is an increasing important issue. This article develops a method to automatically detect fake news on Twitter by calculating a user credibility. Many approaches uses NLP techniques to analyse the content of tweets to predict the credibility of news. Our approach is based on social context feature; we propose a new feature user credibility. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_43},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135053485\&doi=10.1007\%2f978-3-031-07969-6_43\&partnerID=40\&md5=183d4701938c5f034b79048db6ca5566},
	author = {Lahlou, Y. and Fkihi, S.E. and Faizi, R.}
}
@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 {Faizi202252,
	title = {A Sentiment Analysis Based Approach for Exploring Student Feedback},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13449 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {52-59},
	abstract = {Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students{\textquoteright} reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35\%) and, thus, outperforms other implemented models. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Analysis-based approaches, Education computing, Learning algorithms, Learning outcome, Machine-learning, Outcome quality, Quality control, Sentiment analysis, Sources of informations, Student feedback, Students, Support vector machines, Support vectors machine, Teaching quality, Teaching resources},
	doi = {10.1007/978-3-031-15273-3_6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137997385\&doi=10.1007\%2f978-3-031-15273-3_6\&partnerID=40\&md5=feac3688f39fab9ad1f85464953459ec},
	author = {Faizi, R. and El Fkihi, S.}
}
@article {Faizi2022549,
	title = {A Sentiment-Based Approach to Predict Learners{\textquoteright} Perceptions Towards YouTube Educational Videos},
	journal = {Lecture Notes in Networks and Systems},
	volume = {419 LNNS},
	year = {2022},
	note = {cited By 1},
	pages = {549-556},
	abstract = {Given the increasing proliferation of educational resources on YouTube, this social media platform has become an optimal space to which students usually resort to improve their learning in various subjects. It has also turned into an online community in which all users can voice their opinion about videos they have watched. However, getting the perceptions of learners on these learning materials is not always an easy task. Our objective in this paper is, therefore, to propose a machine learning approach that can classify learners{\textquoteright} feedback as being positive or negative. For this purpose, textual data was retrieved from YouTube educational videos, pre-processed and then classified using different machine learning algorithms. Results of the present study demonstrated that the Support Vector Machines algorithm performs better than other models. In fact, with an accuracy score of 92.82\% on combinations of unigrams and bigrams and 92.67\% for associations of unigrams and trigrams, this supervised machine learning approach outperforms other implemented models, namely Na{\"\i}ve Bayes, Random Forest and Logistic Regression. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-96299-9_52},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126255114\&doi=10.1007\%2f978-3-030-96299-9_52\&partnerID=40\&md5=488d0e3f5f3597e45e73566a94ba74a7},
	author = {Faizi, R.}
}
@article {Faizi2021548,
	title = {Higher Education Students{\textquoteright} Perceptions Towards Using Facebook as a Learning Platform},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13117 LNCS},
	year = {2021},
	note = {cited By 1},
	pages = {548-554},
	abstract = {Given the ongoing debate among educators regarding the effect of social networks on education, our aim in this article is to explore the perceptions of students towards using Facebook as a learning environment. In this vein, on the basis of the survey we carried out among university students, it was revealed that 87.4\% of the informants resort to this Web-based community to improve their learning. Facebook has, actually, been reported to provide students with an ideal opportunity to access a plethora of learning resources and all class-related information and activities. It also allows them to discuss different topics with classmates and peers and enables them to seek help on learning problems they encounter. Despite the significant role that Facebook plays in boosting students{\textquoteright} learning experience, 72.3\% of the subjects noted that no interaction occurs with teachers on this platform. Facebook might, thus, be considered to be more useful for informal rather than formal learning. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Computer aided instruction, e-learning, Facebook, High educations, Higher education students, Learning environments, Learning platform, Learning problem, Learning resource, Online systems, Social networking (online), Student perceptions, Students, Surveys, University students, Web-based communities},
	doi = {10.1007/978-3-030-91540-7_56},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121624980\&doi=10.1007\%2f978-3-030-91540-7_56\&partnerID=40\&md5=90703fa8bfd08b30f2af977163ab4fc8},
	author = {Faizi, R. and Rudneva, M.}
}
@article {ElAfia20211,
	title = {Introduction},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {1-2},
	year = {2021},
	note = {cited By 0},
	pages = {1-2},
	doi = {10.1504/ijdats.2021.13.issue-1-2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105420916\&doi=10.1504\%2fijdats.2021.13.issue-1-2\&partnerID=40\&md5=f62611b1b012adb722cd62fb8366b3a7},
	author = {El Afia, A. and Faizi, R. and Garc{\'\i}a, S.}
}
@article {Bouzbita2021253,
	title = {A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System},
	journal = {Studies in Computational Intelligence},
	volume = {906},
	year = {2021},
	note = {cited By 1},
	pages = {253-267},
	abstract = {We propose in this paper a Hidden Markov Model~(HMM) approach to avoid premature convergence of ants in the Ant Colony System~(ACS) algorithm. Indeed, the proposed approach was modelled as a classifier method to control the convergence through the dynamic adaptation of the α parameter that weighs the relative influence of the pheromone. The implementation was tested on several Travelling Salesman Problem~(TSP) instances with different number of cities. The proposed approach was compared with the standard ACS and the existing fuzzy logic~in the literature. The experimental results illustrate that the proposed method shows better performance. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-58930-1_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097964733\&doi=10.1007\%2f978-3-030-58930-1_17\&partnerID=40\&md5=220c02fd7287bc58dc79db86db73282b},
	author = {Bouzbita, S. and Afia, A.E. and Faizi, R.}
}
@article {Moumen20211634,
	title = {Real-time Arabic scene text detection using fully convolutional neural networks},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {11},
	number = {2},
	year = {2021},
	note = {cited By 1},
	pages = {1634-1640},
	abstract = {The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and an FPS of 24.3 vs 31.7 for a VGG-16 fully deployed. {\textcopyright} 2021 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijece.v11i2.pp1634-1640},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097836850\&doi=10.11591\%2fijece.v11i2.pp1634-1640\&partnerID=40\&md5=18f6883629a31b869bc0f2225a4cbf29},
	author = {Moumen, R. and Chiheb, R. and Faizi, 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.}
}
@article {Bouzbita20205436,
	title = {The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {10},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {5436-5444},
	abstract = {In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails ξ and using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters ξ and on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behaviour of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter ξ is more effective compared to the standard ACS. {\textcopyright} 2020 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/IJECE.V10I5.PP5436-5444},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087312967\&doi=10.11591\%2fIJECE.V10I5.PP5436-5444\&partnerID=40\&md5=fb268b2912214f5d902a8ec0f867a2cd},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@article {Mohamed2020335,
	title = {Detecting text in license plates using a novel MSER-based method},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {12},
	number = {4},
	year = {2020},
	note = {cited By 2},
	pages = {335-348},
	abstract = {A new license plate detection method is proposed in this paper. The proposed approach consists of three steps: the first step aims to delete some details in the input image by converting it to a grey-level image and inverse it (negative) and then use MSER for the extraction of text in candidate regions. The second step is based on a dynamic grouped DBSCAN algorithm for a fast classification of the connected region, and the outer tangent of circles intersections for filtering regions with the same orientations. Finally, a geometrical and statistical character filter is used to eliminate false detections in the third step. Experimental results show that our approach performs better and achieves a better detection than that proposed by Yin et al. (2014). {\textcopyright} 2020 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJDATS.2020.111488},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097276428\&doi=10.1504\%2fIJDATS.2020.111488\&partnerID=40\&md5=ec0868e89fae7b8ade5bbd67748009dd},
	author = {Mohamed, A. and Sanaa, E.F. and Faizi, R.}
}
@article {Boudad2020243,
	title = {Exploring the Use of Word Embedding and Deep Learning in Arabic Sentiment Analysis},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 3},
	pages = {243-253},
	abstract = {In the past couple of years, improving Arabic sentiment Analysis systems has been one of the important fields of research. There are several challenges and issues facing existing systems, especially, handling multiple dialects and feature extraction. Most of those systems are generated using linear classification models and traditional bag-of-word features. In this work, we explore the use of word embedding as a modern feature representation, and Convolutional Neural Networks as a Deep Neural Network in a sentiment classification of Arabic texts. The application of our model on five benchmark datasets has yielded results that outperform previous works on 4 out of 5 datasets. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Arabic texts, Bag of words, Benchmark datasets, Benchmarking, Convolution, Convolutional neural networks, Deep learning, Deep neural networks, Embeddings, Existing systems, Feature representation, Intelligent systems, Linear classification, planning, Sentiment analysis, Sentiment classification, Sustainable development, Word embedding},
	doi = {10.1007/978-3-030-36674-2_26},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080913982\&doi=10.1007\%2f978-3-030-36674-2_26\&partnerID=40\&md5=816d3952c972aa5ce92cb7318fe768aa},
	author = {Boudad, N. and Ezzahid, S. and Faizi, R. and Thami, R.O.H.}
}
@article {Bouzbita2019153,
	title = {Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameters},
	journal = {Studies in Computational Intelligence},
	volume = {774},
	year = {2019},
	note = {cited By 0},
	pages = {153-169},
	doi = {10.1007/978-3-319-95104-1_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053070003\&doi=10.1007\%2f978-3-319-95104-1_10\&partnerID=40\&md5=a3b7e585281ec5fd0d0268e2187f8014},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@conference {Moumen2018,
	title = {Arabic diacritization with gated recurrent unit},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230931},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484931\&doi=10.1145\%2f3230905.3230931\&partnerID=40\&md5=3a401539dc4a1f24a5a97d1b3fdc8e36},
	author = {Moumen, R. and Chiheb, R. and Afia, A.E. and Faizi, R.}
}
@article {Hdioud201870,
	title = {Detecting and shadows in the HSV color space using dynamic thresholds},
	journal = {Bulletin of Electrical Engineering and Informatics},
	volume = {7},
	number = {1},
	year = {2018},
	pages = {70-79},
	doi = {10.11591/eei.v7i1.893},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045525834\&doi=10.11591\%2feei.v7i1.893\&partnerID=40\&md5=ca79c66bd666526ee7849c304d10f7ef},
	author = {Hdioud, B. and El Haj Tirari, M. and Oulad Haj Thami, R. and Faizi, R.}
}
@article {Moumen2018360,
	title = {Evaluation of gated recurrent unit in Arabic diacritization},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {9},
	number = {11},
	year = {2018},
	pages = {360-364},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059029808\&partnerID=40\&md5=8ec6347667792a079047e6121cfd5999},
	author = {Moumen, R. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
@conference {Faizi20182109,
	title = {Exploring the potentials of big data analytics in marketing},
	booktitle = {Proceedings of the 31st International Business Information Management Association Conference, IBIMA 2018: Innovation Management and Education Excellence through Vision 2020},
	year = {2018},
	pages = {2109-2114},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060828578\&partnerID=40\&md5=f6ed0328b04bb583011318fc69ad0c58},
	author = {Faizi, R. and Fkihi, S.E. and Afia, A.E.}
}
@article {Sarhani2018447,
	title = {Facing the feature selection problem with a binary PSO-GSA approach},
	journal = {Operations Research/ Computer Science Interfaces Series},
	volume = {62},
	year = {2018},
	pages = {447-462},
	doi = {10.1007/978-3-319-58253-5_26},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032615163\&doi=10.1007\%2f978-3-319-58253-5_26\&partnerID=40\&md5=45163b8637f55b8af04f902f4f33afc2},
	author = {Sarhani, M. and El Afia, A. and Faizi, R.}
}
@article {Sarhani2018447,
	title = {Facing the feature selection problem with a binary PSO-GSA approach},
	journal = {Operations Research/ Computer Science Interfaces Series},
	volume = {62},
	year = {2018},
	note = {cited By 0},
	pages = {447-462},
	abstract = {Feature selection has become the focus of much research in many areas where we can face the problem of big data or complex relationship among features. Metaheuristics have gained much attention in solving many practical problems, including feature selection. Our contribution in this paper is to propose a binary hybrid metaheuristic to minimize a fitness function representing a trade-off between the classification error of selecting the feature subset and the corresponding number of features. This algorithm combines particle swarm optimization (PSO) and gravitational search algorithm (GSA). Also, a mutation operator is integrated to enhance population diversity. Experimental results on ten benchmark dataset show that our proposed hybrid method for feature selection can achieve high performance when comparing with other metaheuristic algorithms and well-known feature selection approaches. {\textcopyright} Springer International Publishing AG 2018.},
	doi = {10.1007/978-3-319-58253-5_26},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032615163\&doi=10.1007\%2f978-3-319-58253-5_26\&partnerID=40\&md5=45163b8637f55b8af04f902f4f33afc2},
	author = {Sarhani, M. and El Afia, A. and Faizi, R.}
}
@conference {Khaldi2018,
	title = {Forecasting of bitcoin daily returns with eemd-elman based model},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230948},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484904\&doi=10.1145\%2f3230905.3230948\&partnerID=40\&md5=0f2e4191d42a3eaf7857e8a6821b5f27},
	author = {Khaldi, R. and Afia, A.E. and Chiheb, R. and Faizi, R.}
}
@conference {Fkihi20182184,
	title = {How big data is reshaping the e-commerce industry},
	booktitle = {Proceedings of the 31st International Business Information Management Association Conference, IBIMA 2018: Innovation Management and Education Excellence through Vision 2020},
	year = {2018},
	pages = {2184-2189},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060821566\&partnerID=40\&md5=0b245a1936c2cf3e78d4a9af77e3d592},
	author = {Fkihi, S.E. and Faizi, R.}
}
@conference {Zarra201888,
	title = {MOOCs{\textquoteright} recommendation based on forum latent dirichlet allocation},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	pages = {88-93},
	doi = {10.1145/3289100.3289115},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058674172\&doi=10.1145\%2f3289100.3289115\&partnerID=40\&md5=1de27cf008154c73414e9740b9f86e22},
	author = {Zarra, T. and Chiheb, R. and Faizi, R. and Afia, A.E.}
}
@article {Faizi201886,
	title = {Moroccan higher education students{\textquoteright} and teachers{\textquoteright} perceptions towards using Web 2.0 technologies in language learning and teaching},
	journal = {Knowledge Management and E-Learning},
	volume = {10},
	number = {1},
	year = {2018},
	pages = {86-96},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044324038\&partnerID=40\&md5=dbc2a7327c61d66e53e10b1fe45c000d},
	author = {Faizi, R.}
}
@conference {Moumen20181,
	title = {An NLP based text-to-speech synthesizer for Moroccan Arabic},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-5},
	doi = {10.1109/CloudTech.2017.8284745},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046676982\&doi=10.1109\%2fCloudTech.2017.8284745\&partnerID=40\&md5=9ca745be5c63a00cda019ef8b0230a9d},
	author = {Moumen, R. and Chiheb, R. and Faizi, R. and Afia, A.E.}
}
@conference {Admi2018,
	title = {A novel mser based method for detecting text in license plates},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230923},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484803\&doi=10.1145\%2f3230905.3230923\&partnerID=40\&md5=e4a83cc458bcdf11fbe9b492ea53bb87},
	author = {Admi, M. and Fkihi, S.E. and Faizi, R.}
}
@conference {Bensouda2018,
	title = {Opinion spam detection: A review of the literature},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230922},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053485124\&doi=10.1145\%2f3230905.3230922\&partnerID=40\&md5=a94b2b0c2405c84e95f213eb29c413f2},
	author = {Bensouda, N. and Fkihi, S.E. and Faizi, R.}
}
@conference {Bouzbita2018,
	title = {Parameter adaptation for ant colony system algorithm using hidden markov model for tsp problems},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230962},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053485206\&doi=10.1145\%2f3230905.3230962\&partnerID=40\&md5=69e09fa2debc5dc07106895d9ce7948b},
	author = {Bouzbita, S. and El Afia, A. and Faizi, 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.}
}
@conference {ElKarfi201859,
	title = {A spectral clustering-based approach for sentiment classification in modern standard Arabic},
	booktitle = {MCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2018, Theory and Practice in Modern Computing 2018 and Connected Sma},
	year = {2018},
	pages = {59-65},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054167622\&partnerID=40\&md5=101982324dd788664f052d2919012106},
	author = {El Karfi, I. and El Fkihi, S. and Faizi, R.}
}
@article {Faizi20181219,
	title = {Teachers{\textquoteright} perceptions towards using Web 2.0 in language learning and teaching},
	journal = {Education and Information Technologies},
	volume = {23},
	number = {3},
	year = {2018},
	pages = {1219-1230},
	doi = {10.1007/s10639-017-9661-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032681369\&doi=10.1007\%2fs10639-017-9661-7\&partnerID=40\&md5=6a82c0e13c947b2aed26c84386814ef2},
	author = {Faizi, R.}
}
@conference {ElHaddaoui2018,
	title = {Toward a sentiment analysis framework for social media},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3230905.3230919},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053484908\&doi=10.1145\%2f3230905.3230919\&partnerID=40\&md5=c602c6dc79537942d4d5ac0b9e4a0197},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and Afia, A.E.}
}
@conference {Khaldi2017,
	title = {Artificial neural network based approach for blood demand forecasting: Fez transfusion blood center case study},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Blood demand and supply management are considered one of the major components of a healthcare supply chain, since blood is a vital element in preserving patient{\textquoteright}s life. However, forecasting it faces several challenges including frequent shortages, and possible expiration caused by demand uncertainty of hospitals. This uncertainty is mainly due to high variability in the number of emergency cases. Thereupon, this investigation presents a real case study of forecasting monthly demand of three blood components, using Artificial Neural Networks (ANNs). The demand of the three blood components (red blood cells (RBC), plasma (CP) and platelets (PFC)) and other observations are obtained from a central transfusion blood center and a University Hospital. Experiments are carried out using three networks to forecast each blood component separately. Last, the presented model is compared with ARIMA to evaluate its performance in prediction. The results of this study depict that ANN models overcomes ARIMA models in demand forecasting. Thus high ANN models can be considered as a promising approach in forecasting monthly blood demand. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090415},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028449243\&doi=10.1145\%2f3090354.3090415\&partnerID=40\&md5=7ab92ea6ba7c2f5837ee88fcb2a91ffc},
	author = {Khaldi, R. and El Afia, A. and Chiheb, R. and Faizi, R.}
}
@conference {Er-Rady2017,
	title = {Automatic sign language recognition: A survey},
	booktitle = {Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017},
	year = {2017},
	note = {cited By 0},
	abstract = {Sign Language, which is a fully visual language with its own grammar, differs largely from that of spoken languages [21]. After nearly 30 years of research, SL recognition still in its infancy when compared to Automatic Speech Recognition. When producing Sign language (SL), different body parts are involved. Most importantly the hands, but also facial expressions and body movements/postures. The recognition of SL is still one of the most challenging problems in gesture recognition. In this survey, we are going to discuss the advancement of sign language recognition through the last decade. In this paper, we provide a review of the state-of-the-art building blocks of Automatic Sign Language Recognition (ASLR) system, from feature extraction up to sign. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/ATSIP.2017.8075561},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035355057\&doi=10.1109\%2fATSIP.2017.8075561\&partnerID=40\&md5=2dda585a2de8f963b1f1f0cc678e01d3},
	author = {Er-Rady, A. and Faizi, R. and Thami, R.O.H. and Housni, H.}
}
@conference {Bouzbita2017344,
	title = {Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model},
	booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016},
	year = {2017},
	note = {cited By 1},
	pages = {344-349},
	abstract = {The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CloudTech.2016.7847719},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013861130\&doi=10.1109\%2fCloudTech.2016.7847719\&partnerID=40\&md5=26cafb2d23ee70bf2f3552d7fec22e8a},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R. and Zbakh, M.}
}
@article {ElAfia20172161,
	title = {The effect of updating the local pheromone on ACS performance using fuzzy logic},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {7},
	number = {4},
	year = {2017},
	note = {cited By 1},
	pages = {2161-2168},
	abstract = {Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the Ξ parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB). Copyright {\textcopyright} 2017 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijece.v7i4.pp2161-2168},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030845141\&doi=10.11591\%2fijece.v7i4.pp2161-2168\&partnerID=40\&md5=b3dc47e58e78c530f0c597ab93eda8bf},
	author = {El Afia, A. and Bouzbita, S. and Faizi, R.}
}
@conference {Faizi2017997,
	title = {Extracting business value from big data},
	booktitle = {Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: From Regional Development Sustainability to Global Economic Growth},
	year = {2017},
	note = {cited By 0},
	pages = {997-1002},
	abstract = {The exponential growth of structured and unstructured data in the last few years has resulted in the emergence of {\textquoteright}big data{\textquoteright} as one of the hottest technology trends. With data of all kinds being generated in record amounts every minute, big data has become a buzzword across various fields. The objective of this paper is, therefore, to explore the various ways through which businesses can derive value from both internal and external data. In this respect, it was found out that companies, both big and small - across all industries - can use big data to increase customers{\textquoteright} retention and to meet the need of every individual customer. This huge amount of data can also help a business identify the strengths and weaknesses of its competitors and, thus, stay ahead of them. Finally, big data can provide businesses with real-time insights that would enable them to spot or predict problems, and solve them before they happen. Given all these benefits, we strongly believe that the collection and processing of the massive amounts of data, generated by customers or smart devices, can help any enterprise make more powerful and accurate decisions and, thus, significantly improve business operations and organizational performance.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029742611\&partnerID=40\&md5=bfa9daca776ba7dea239abddfc70501a},
	author = {Faizi, R. and El Fkihi, S. and El Afia, A. and Chiheb, R.}
}
@conference {Bouzbita2017633,
	title = {A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2017},
	note = {cited By 0},
	pages = {633-638},
	abstract = {The aim of this paper is to propose a new method capable of dynamically controlling the evaporation parameter in an Ant Colony System (ACS) using a Hidden Markov Model. The purpose is to improve the performance of ACS by controlling the exploration and exploitation in the search space. To this end, two HMM approaches are proposed. The first is a training method that best suits the observed data of the Hidden Markov Model. The second is a method that dynamically controls the adapted parameter by applying several processes. To test our algorithm we used a set of Travelling Salesman Problem (TSP) instances. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ICMCS.2016.7905544},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019108752\&doi=10.1109\%2fICMCS.2016.7905544\&partnerID=40\&md5=2acc848761420a801ab6567757203ac4},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
@conference {Moumen2017,
	title = {A novel text-to-speech synthesizer for processing sms in moroccan Arabic},
	booktitle = {Proceedings of International Conference on Computers and Industrial Engineering, CIE},
	year = {2017},
	note = {cited By 0},
	abstract = {This paper proposes a novel text-to-speech (TTS) system that converts text messages (SMS) into speech sequences. This system takes into consideration the characteristics of the Moroccan context, where several languages might be used in the same text message. Therefore, the languages that are analyzed in this work are English, French, Moroccan Arabic and Modern Standard Arabic (MSA). Processing French, English, or Modern Standard Arabic is common and has given satisfying results. However, Moroccan Arabic remains under-studied for many reasons, particularly the lack of data resources given that this Arabic dialect is primarily spoken and non-codified.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040947811\&partnerID=40\&md5=1ddd4fb5d94d0350d8211d7bc3fc7c61},
	author = {Moumen, R. and Chiheb, R. and Zarra, T. and Faizi, R. and El Afia, A.}
}
@conference {Khaldi2017,
	title = {Prediction of supplier performance: A novel DEA-ANFIS based approach},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {The focus of this paper is on investigating the feasibility of using ANFIS combined with DEA for supplier{\textquoteright}s post-evaluation. The proposed framework aims at modeling performance measurement, and forecasting of a selected hospital{\textquoteright}s drug suppliers. Even though it is broadly employed as a benchmarking tool to evaluate DMUs efficiency, DEA can hardly be used to predict the performance of unseen DMUs. For this reason, ANFIS model has been integrated to DEA due to its nonlinear mapping, strong generalization capabilities and pattern prediction functionalities. DEA based BCC model is used to evaluate the efficiency scores of a set of suppliers, then ANFIS intervenes to learn DEA patterns and to forecast the performance of new suppliers. The results of this research highlight the prediction power of the proposed model in a new scope. They present it as an efficient benchmarking tool and a promising decision support system applied at the operational level. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090416},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028459962\&doi=10.1145\%2f3090354.3090416\&partnerID=40\&md5=da01d9720d77f4831205623526bfc9d9},
	author = {Khaldi, R. and Chiheb, R. and El Afia, A. and Akaaboune, A. and Faizi, R.}
}
@conference {Faizi20171760,
	title = {Screening job candidates via social media: Opportunities and pitfalls},
	booktitle = {Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: From Regional Development Sustainability to Global Economic Growth},
	year = {2017},
	note = {cited By 0},
	pages = {1760-1765},
	abstract = {Many businesses have undergone significant changes over the past decade due to the exponential growth of social media. These technologies have not only altered how professionals and businesses operate but also the way they get engaged with target groups. With millions of job seekers being tech-savvy, recruiters are heavily using social media to screen job candidates. Whether seeking a job or hunting for candidates, these online networks seem to be the ideal venue. Therefore, our objective in this paper is to examine the opportunities and pitfalls of using social media in the recruitment process. In this respect, it was found out that these online platforms enable human resources managers to have access to a large and diverse pool of qualified prospective employees at a lower cost, and to get connected with passive candidates. Social media also provide recruiters with an opportunity to get greater insight into an applicant{\textquoteright}s real abilities and competences as well as to view aspects of his personality. Nevertheless, using these platforms in the hiring process usually inundates employers with huge amounts of data that is often difficult to analyze. Moreover, being limited to the information available on social media runs the risk of excluding skilled candidates or hiring under qualified employees. Finally, accessing sensitive information on a job candidate{\textquoteright}s social media profile could be viewed as an invasion of privacy. Despite all these pitfalls, we strongly believe that social media represent a significant component of the hiring process that will give businesses a competitive advantage.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029761283\&partnerID=40\&md5=099a91a3a97bbda87b2b604b24ae305c},
	author = {Faizi, 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 {Faizi2016501,
	title = {A perceptual analysis of phrasal stress in Amazigh},
	journal = {Journal of North African Studies},
	volume = {21},
	number = {3},
	year = {2016},
	note = {cited By 0},
	pages = {501-511},
	abstract = {ABSTRACT: In the last few years, a number of research studies have been conducted on stress in Amazigh, a Hamito-Semitic language spoken in North Africa. However, these works were mainly confined to analysing word-level stress. The objective of this paper is, therefore, to examine stress distribution beyond this level, namely within the phrase and the sentence in Goulmima Amazigh. Specifically, an attempt is made to investigate whether the stress patterns of isolated words are similar or distinct from those of items that appear within phrases or sentences. Results of the study revealed that the assignment of stress at the phrasal and sentence levels shows marked differences from the assignment of stress at the word level. In fact, stress above the word level no longer depends on the internal make-up of the syllable. Apart from nominal forms that occupy the initial position of a phrase or sentence, those that are part of each of these strings get stressed on the rightmost syllable whose nucleus is a plain vowel. Nonetheless, only the last stressed syllable in the sequence bears more prominence. {\textcopyright} 2016 Taylor \& Francis.},
	doi = {10.1080/13629387.2016.1141681},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958046528\&doi=10.1080\%2f13629387.2016.1141681\&partnerID=40\&md5=957f7a2d6d61c252acf888f321071d1a},
	author = {Faizi, 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.}
}
@article {ElHajTirari201270,
	title = {A new optimal quadratic predictor of a residual linear model in a finite population},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {42},
	number = {1},
	year = {2012},
	note = {cited By 0},
	pages = {70-74},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867528812\&partnerID=40\&md5=3adc9b77708ed77880f3937f292856ec},
	author = {El Haj Tirari, M. and El Afia, A. and Faizi, R.}
}
@conference {Souali2011,
	title = {An automatic ethical-based recommender system for e-commerce},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2011},
	note = {cited By 2},
	doi = {10.1109/ICMCS.2011.5945631},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961215460\&doi=10.1109\%2fICMCS.2011.5945631\&partnerID=40\&md5=8ab17e424b6230f9880c1381229a5999},
	author = {Souali, K. and El Afia, A. and Faizi, R.}
}
@conference {Souali2011,
	title = {A new recommender system for e-learning environments},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2011},
	note = {cited By 4},
	doi = {10.1109/ICMCS.2011.5945630},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961232743\&doi=10.1109\%2fICMCS.2011.5945630\&partnerID=40\&md5=4d89bd30150afdd564b7caedffba4902},
	author = {Souali, K. and El Afia, A. and Faizi, R. and Chiheb, R.}
}
@article {Chiheb201169,
	title = {Using objective online testing tools to assess students{\textquoteright} learning: Potentials and limitations},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {24},
	number = {1},
	year = {2011},
	note = {cited By 1},
	pages = {69-72},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951845409\&partnerID=40\&md5=8dfb6d5a464b4a730925a6ff3009b592},
	author = {Chiheb, R. and Faizi, R. and El Afia, A.}
}
