@article {Mikram2022167,
	title = {Hybrid Deep Learning Models for Diabetic Retinopathy Classification},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {167-178},
	abstract = {Diabetic retinopathy is a complication of diabetes in the eye. This disease is caused by the damage of the blood vessels of the back of eye (i.e., retina). Unfortunately, diabetic retinopathy can cause several symptoms, the most serious of which is complete vision loss. Indeed, the detection of diabetic retinopathy is a time-consuming manual process that requires a qualified clinician to examine and evaluate digital color photographs of the retina{\textquoteright}s fundus. Currently, several researches are looking to employ artificial intelligence techniques, especially the Deep Learning, to deal with this issue. In this paper, we study some hybrid models for diabetic retinopathy severity classification in distributed and non-distributed environments. The studied models perform two main tasks: deep feature extraction and then classification of diabetic retinopathy according to its severity. The models were trained and validated on a publicly available dataset of 80,000 images and they achieved an accuracy of 80.7\%. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135078332\&doi=10.1007\%2f978-3-031-07969-6_13\&partnerID=40\&md5=bf1f98b5263465c335e38e77dda6ea10},
	author = {Mikram, M. and Moujahdi, C. and Rhanoui, M. and Meddad, M. and Khallout, A.}
}
@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.}
}
@conference {Ouardi2021790,
	title = {Hybrid Prototypical Networks Augmented by a Non-linear Classifier},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2021},
	note = {cited By 0},
	pages = {790-794},
	abstract = {Text classification is one of the most prolific domains in machine learning. Present in a raw format all around us in our daily life Starting from human to human communication mainly by the social networks apps, arriving at the human-machine interaction especially with chatbots, text is a rich source of information. However, despite the remarkable performances that deep learning achieves in this field, the cost in therm of the amount of data needed to train this model still considerably high, adding to that the need of retraining this model to learn every new task. Nevertheless, a new sub-field of machine learning has emerged, named meta-learning it targets the overcoming of those limitations, widely used for image-related tasks, it can also bring solutions to tasks associated with text. Starting from this perspective we proposed a hybrid architecture based on well-known prototypical networks consisting of adapting this model to text classification and augmenting it with a non-linear classifier. Copyright {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Chatbots, Classification (of information), Daily lives, Deep learning, Few-shot learning, Human communications, Human machine interaction, Learning systems, Machine-learning, Metalearning, Nonlinear classifiers, Sources of informations, Text classification, Text processing},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137962135\&partnerID=40\&md5=8d0e3e181cd871cde23a9553ea783fb2},
	author = {Ouardi, A.E. and Rhanoui, M. and Benlarabi, A. and Asri, B.E.}
}
@conference {Houari20181,
	title = {Hybrid big data warehouse for on-demand decision needs},
	booktitle = {Proceedings of 2017 International Conference on Electrical and Information Technologies, ICEIT 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/EITech.2017.8255261},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047477758\&doi=10.1109\%2fEITech.2017.8255261\&partnerID=40\&md5=f88360d6e054f43cc33c4593431fa891},
	author = {Houari, M.E. and Rhanoui, M. and Asri, B.E.}
}
