@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.}
}
@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 {Sarhani20151,
	title = {Electric load forecasting using hybrid machine learning approach incorporating feature selection},
	booktitle = {CEUR Workshop Proceedings},
	volume = {1580},
	year = {2015},
	note = {cited By 0},
	pages = {1-7},
	abstract = {Forecasting of future electricity demand is very important for the electric power industry. As influenced by various factors, it has been shown in several publications that machine learning methods are useful for electric load forecasting (ELF). On the one hand, we introduce in this paper the approach of support vector regression (SVR) for ELF. In particular, we use particle swarm optimization (PSO) algorithm to optimize SVR parameters. On the other hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. Our contribution consists of investigating the importance of applying the feature selection approach for removing the irrelevant factors of electric load. The experimental results elucidate the feasibility of applying feature selection without decreasing the performance of the SVR-PSO model for ELF.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977574358\&partnerID=40\&md5=405be8fb16c29c43f82f16549d26252b},
	author = {Sarhani, M. and El Afia, A.}
}
@conference {Sarhani201479,
	title = {An extension of X13-ARIMA-SEATS to forecast islamic holidays effect on logistic activities},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 0},
	pages = {79-84},
	abstract = {To better manage and optimize logistic activities, factors that affect it must be determined: The calendar effect is one of these factors which must be analyzed. Analyzing such kind of data by using classical time series forecasting methods, such as exponential smoothing method and ARIMA model, will fail to capture such variation. This paper is released to present a review of the models which are used to forecast the calendar effect, especially moving holidays effect. We adopt the recent approach of X13-ARIMA-SEATS and extend it for being able to forecast the effect of Islamic holidays. Our extension is applied to Moroccan case studies, and aims to give recommendations concerning this effect on logistic activities. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887423},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908655728\&doi=10.1109\%2fGOL.2014.6887423\&partnerID=40\&md5=de88391f3f7ec0517e753c7aeb4aeae4},
	author = {Sarhani, M. and El Afia, A.}
}
