@article {Slimani20224243,
	title = {Automated machine learning: the new data science challenge},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {12},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {4243-4252},
	abstract = {The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a {\textquotedblleft}data science approach{\textquotedblright} is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with {\textquotedblleft}machine learning{\textquotedblright} learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models is compared based on different metrics. {\textcopyright} 2022 Institute of Advanced Engineering and Science. All rights reserved.},
	doi = {10.11591/ijece.v12i4.pp4243-4252},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129666214\&doi=10.11591\%2fijece.v12i4.pp4243-4252\&partnerID=40\&md5=1d5f93103617370f5a92a90901bfe815},
	author = {Slimani, I. and Slimani, N. and Achchab, S. and Saber, M. and Farissi, I.E. and Sbiti, N. and Amghar, M.}
}
@article {Bousqaoui2018626,
	title = {Information sharing as a coordination tool in supply chain using multi-agent system and neural networks},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {745},
	year = {2018},
	pages = {626-632},
	doi = {10.1007/978-3-319-77703-0_62},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045145521\&doi=10.1007\%2f978-3-319-77703-0_62\&partnerID=40\&md5=3a6c9dcdd6ba98d43e615abe474572b2},
	author = {Bousqaoui, H. and Slimani, I. and Achchab, S.}
}
@article {Slimani2017144,
	title = {Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data},
	journal = {International Journal of Logistics Systems and Management},
	volume = {28},
	number = {2},
	year = {2017},
	note = {cited By 0},
	pages = {144-163},
	abstract = {The purpose of any effective supply chain is to find balance between supply and demand by coordinating all internal and external processes in order to ensure delivery of the right product, to the right customer, at the best time and with the optimal cost. Therefore, the estimation of future demand is one of the crucial tasks for any organisation of the supply chain system who has to make the correct decision in the appropriate time to enhance its commercial competitiveness. In an earlier study, where various artificial neural networks{\textquoteright} structures are compared including perceptron, adaline, no-propagation, multi layer perceptron (MLP) and radial basis function for demand forecasting, the results indicate that the MLP structure present the best forecasts with the optimal error. Consequently, this paper focuses on realising a daily demand predicting system in a supermarket using MLP by adding inputs including previous demand, days{\textquoteright} classification and average demand quantities. {\textcopyright} Copyright 2017 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJLSM.2017.086345},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029227042\&doi=10.1504\%2fIJLSM.2017.086345\&partnerID=40\&md5=ccd93c50554b8b201bac46a1363f9953},
	author = {Slimani, I. and El Farissi, I. and Achchab, S.}
}
@conference {ElFarissi2017122,
	title = {Coordination by sharing demand forecasts in a supply chain using game theoretic approach},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	year = {2017},
	note = {cited By 0},
	pages = {122-127},
	abstract = {Through the literature, authors give a considerable attention to game theory because of its wide range of applications in various fields including economics, political science, psychology or biology. The aim of this case study is to employ game theoretic approach to model information sharing as a coordination mechanism in a basic two-echelon supply chain composed of a single retailer and a single supplier. This paper is the sequel of previous works; where demand is forecasted based on historical data of a supermarket in Morocco using the Multi Layer Perceptron structure of the artificial neural networks. Nevertheless, this work focuses on the implementation of the obtained forecasting results in the studied system modeled as a game of two players with asymmetric and imperfect information, in order to find the equilibrium of the game that guaranties maximum payoff for both players. In fact, this is what game theory is all about. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CIST.2016.7805028},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010210515\&doi=10.1109\%2fCIST.2016.7805028\&partnerID=40\&md5=79f1785352cd1586a09b76b45cdd93d1},
	author = {El Farissi, I. and Slimani, I. and Achchab, S.}
}
@conference {Slimani2014168,
	title = {Game theory to control logistic costs in a two-echelon supply chain},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 4},
	pages = {168-170},
	abstract = {As a mathematical tool of the decision maker, game theory is an essential methodology to analyze and solve situations where the decision of each rational agent, called player, affects the other agent{\textquoteright}s payoff. Indeed, in this work game theory is used to analyze an inventory and transportation optimization problem within a supply chain composed of two agents: a retailer who faces a random demand of a final product and his supplier of raw materials that is also responsible of the transportation function. Since the retailer is closer to the market and has a better view of demand forecasts careful attention given to Information sharing and its impacts. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887435},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908632329\&doi=10.1109\%2fGOL.2014.6887435\&partnerID=40\&md5=059b1ee51ffe9bf41468fce7e9cd9ed5},
	author = {Slimani, I. and Achchab, S.}
}
