@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 {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 {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.}
}
