@article {Rhanoui20222809,
	title = {A hybrid recommender system for patron driven library acquisition and weeding},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	volume = {34},
	number = {6},
	year = {2022},
	note = {cited By 2},
	pages = {2809-2819},
	abstract = {Nowadays the explosion of information sources has shaped how library users access information and provide feedback on their preferences. Therefore, faced with this explosion and the blossoming of digital libraries, modern libraries must take up the challenge of meeting the needs of their users and considering their opinions and preferences in order to offer them adequate resources and henceforth eliminate those that are unsatisfactory. Almost all library recommender systems aim to provide users with items of interest to them. However, despite its definite interest, the staff-oriented adaptation and application of this revolutionary technique to the collection development process is still in an embryonic stage. We propose a patron-driven hybrid library recommender system that uses machine learning techniques to recommend and assist in the acquisition and weeding decision-making operations by extracting and analyzing users{\textquoteright} opinions and ratings. The recommender system is applied and validated in a real national library case using Amazon{\textquoteright}s digital library and the library{\textquoteright}s catalog as a data source. {\textcopyright} 2020 The Authors},
	doi = {10.1016/j.jksuci.2020.10.017},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096428211\&doi=10.1016\%2fj.jksuci.2020.10.017\&partnerID=40\&md5=49af7074b1295a09ce952c01fd4c7f92},
	author = {Rhanoui, M. and Mikram, M. and Yousfi, S. and Kasmi, A. and Zoubeidi, N.}
}
@article {Yousfi2022,
	title = {Smart big data framework for insight discovery},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	year = {2022},
	note = {cited By 2},
	abstract = {Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems. {\textcopyright} 2021},
	doi = {10.1016/j.jksuci.2021.12.009},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122932927\&doi=10.1016\%2fj.jksuci.2021.12.009\&partnerID=40\&md5=16742da4bfac0205224464c7c43d3855},
	author = {Yousfi, S. and Chiadmi, D. and Rhanoui, M.}
}
@article {Yousfi202050,
	title = {Comparative study of cnn and lstm for opinion mining in long text},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {14},
	number = {3},
	year = {2020},
	note = {cited By 0},
	pages = {50-55},
	abstract = {The digital revolution has encouraged many companies to set up new strategic and operational mechanisms to supervise the flow of information published about them on the Web. Press coverage analysis is a part of sentiment analysis that allows companies to discover the opinion of the media concerning their activities, products and services. It is an important research area, since it involves the opinion of informed public such as journalists, who may influence the opinion of their readers. However, from an implementation perspective, the analysis of the opinion from media coverage encounters many challenges. In fact, unlike social networks, the Media coverage is a set of large textual documents written in natural language. The training base being huge, it is necessary to adopt large-scale processing techniques like Deep Learning to analyze their content. To guide researchers to choose between one of the most commonly used models CNN and LSTM, we compare and apply both models for opinion mining from long text documents using real datasets. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.},
	doi = {10.14313/JAMRIS/3-2020/34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100520263\&doi=10.14313\%2fJAMRIS\%2f3-2020\%2f34\&partnerID=40\&md5=fc9c41966d823ca1951c9f01e3fa341c},
	author = {Yousfi, S. and Rhanoui, M. and Mikram, M.}
}
@article {Mikram202071,
	title = {Unit load devices (Uld) demand forecasting in the air cargo fooptimal cost management},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {14},
	number = {3},
	year = {2020},
	note = {cited By 0},
	pages = {71-80},
	abstract = {In recent decades, the airline industry has become very competitive. With the advent of large aircraft in service, unit load devices (ULD) have become an essential element for efficient air transport. They can load a large amount of baggage, cargo or mail using only one unit. Since this results in fewer units to load, saving time and efforts of ground crews and helping to avoid delayed flights. However, a deficient loading of the units causes operating irregularities, costing the company and contributing to the dissatisfaction of the customers. In contrast, an excess load of containers is at the expense of cargo. In this paper we propose an approach to predict the demand for baggage in order to optimize the management of its ULD flow. Specifically, we build prediction models: ARIMA following the BOX-JENKINS approach and exponential smoothing methods, in order to obtain more accurate forecasts. The approach is tested using the operational data of flight processing and the results are compared with four benchmark method (SES, DES, Holt-Winters and Naive prediction) using different performance indicators: MAE, MSE, MAPE, WAPE, RMSE, SMPE. The results obtained with the exponential smoothing methods surpass the benchmarks by providing more accurate forecasts. {\textcopyright} 2020, Industrial Research Institute for Automation and Measurements. All rights reserved.},
	doi = {10.14313/JAMRIS/3-2020/37},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100524740\&doi=10.14313\%2fJAMRIS\%2f3-2020\%2f37\&partnerID=40\&md5=2a7b355fdf9b83161cf3d7a9c85f19b8},
	author = {Mikram, M. and Rhanoui, M. and Yousfi, S. and Briwa, H.}
}
@article {Yousfi201979,
	title = {Mixed-Profiling Recommender Systems for Big Data Environment},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {756},
	year = {2019},
	note = {cited By 0},
	pages = {79-89},
	doi = {10.1007/978-3-319-91337-7_8},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047301457\&doi=10.1007\%2f978-3-319-91337-7_8\&partnerID=40\&md5=95ad0c9dc701ce47a8fefa5d66452387},
	author = {Yousfi, S. and Rhanoui, M. and Chiadmi, D.}
}
@article {Yousfi2019207,
	title = {Towards a generic multimodal architecture for batch and streaming Big Data integration},
	journal = {Journal of Computer Science},
	volume = {15},
	number = {1},
	year = {2019},
	note = {cited By 0},
	pages = {207-220},
	doi = {10.3844/jcssp.2019.207.220},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061562274\&doi=10.3844\%2fjcssp.2019.207.220\&partnerID=40\&md5=5e332b31353f824de4847e4a59a0d84a},
	author = {Yousfi, S. and Rhanoui, M. and Chiadmi, D.}
}
