@article {7218,
	title = {Deep learning in Logistics: systematic review},
	journal = {International Journal of Logistics Systems and Management},
	year = {2022},
	abstract = {Logistics is one of the main tactics that countries and businesses are improving in order to increase profits. Another prominent theme in today{\textquoteright}s logistics is emerging technologies. Today{\textquoteright}s developments in logistics and industry are how to profit from collected and accessible data to use it in various processes such as decision making, production plan, logistics delivery programming, and so on, and more specifically deep learning methods. The aim of this paper is to identify the various applications of deep learning in logistics through a systematic literature review. A set of research questions had been identified to be answered by this article.},
	url = {https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijlsm},
	author = {Sahraoui, Khaoula and Samia Aitouche and AKSA, Karima}
}
@proceedings {7264,
	title = {Application of Data Mining in Industry in the Transition Era to Industry 4.0: Review},
	journal = {The Twelfth International Conference on Information, Process, and Knowledge Management (eKNOW 2020)},
	year = {2020},
	abstract = {The era of Industry 4.0 has already begun, however, several improvements should be achieved concerning this revolution. Data mining is one of the modest and efficient tools. Based on a specific query entered in Scopus, related to Industry 4.0, data mining (DM) and logistics, selected documents were studied and analyzed. A brief background of Industry 4.0 and DM are presented. A generic analysis showed that the attentiveness for the cited subject area by countries, universities, authors and especially companies and manufacturers increased through the years. Content analysis reveals that the improvement in quality of the technologies used in manufacturing was noticed, concluding that DM would give Industry 4.0 a leap forward, yet research is dealing with several challenges.},
	url = {file:///C:/Users/VRPG/Downloads/eknow_2020_1_30_68006.pdf},
	author = {Sahraoui, Khaoula and Samia Aitouche and AKSA, Karima}
}
@proceedings {7265,
	title = {A Scientometric Framework: Application for Knowledge Management (KM) in Industry Between 2014 and 2019},
	journal = {The Twelfth International Conference on Information, Process, and Knowledge Management (eKNOW 2020)},
	year = {2020},
	abstract = {It is always difficult to identify the most recent works that have been published, especially those published in recent years, due to delays in putting publications online, citations indexe, etc. Scientometry offers to researchers various concepts, models and techniques that can be applied to knowledge management (KM) in order to explore its foundations, its state, its intellectual core, and its potential future development. To this end, we have developed a scientometric KM framework to calculate the scientometric indexes related to a query introduced in the Scopus database, to facilitate research and monitoring of productivity and collaboration between the authors of KM in particular and also the dissemination of knowledge. The works between 2014 and 2019 are taken, the industry of services was omitted. It might help the decision makers and researchers to optimize their time and efforts. We used Unified Modeling Language (UML) to translate the development ideas of the scientometric framework structure into diagrams, and Delphi 7 to calculate the indexes and ensure other operations of research (about: articles, their authors, conferences, etc). This framework is only valid for Excel files extracted from Scopus or similar format. Finally, the relation between KM and industry 4.0 was established on found articles in Scopus.},
	url = {http://personales.upv.es/thinkmind/dl/conferences/eknow/eknow_2020/eknow_2020_1_10_68003.pdf},
	author = {Samia Aitouche and Sahraoui, Khaoula and AKSA, Karima and Djouggane, Fadhila and Cherrid, Walid and Belayati, Souleyman}
}
