Raddouane Chiheb
Mohammed V University
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Publication
Featured researches published by Raddouane Chiheb.
international conference on multimedia computing and systems | 2011
Kamal Souali; Abdellatif El Afia; Rdouan Faizi; Raddouane Chiheb
Today recommender systems are widely used not only in e-commerce but in e-learning as well. They are actually made use of in the latter environment to suggest resources and learning materials to learners and, thus, contribute in improving the quality of both teaching and learning. In this paper, we put forward a new recommendation system that provides learners with the most appropriate answers and clues through a request /answer module.
international conference on multimedia computing and systems | 2016
Mohamed Lalaoui; Abdellatif El Afia; Raddouane Chiheb
The Simulated Annealing (SA) is a stochastic local search algorithm. It is an adaptation of the Metropolis-Hastings Monte Carlo algorithm. SA mimics the annealing process in metallurgy to approximate the global optimum of an optimization problem and uses a temperature parameter to control the search. The efficiency of the simulated annealing algorithm involves the adaptation of the cooling schedule. In this paper, we integrate Hidden Markov Model (HMM) in SA to iteratively predict the best cooling law according to the search history. Experiments performed on many benchmark functions show that our proposed scheme outperforms other SA variants in term of quality of solutions.
international conference on big data | 2017
Abdellatif El Afia; Mohamed Lalaoui; Raddouane Chiheb
The present paper explores a new approach to control the simulated annealing (SA) with Huang cooling. The central idea is to control the cooling speed using a fuzzy controller to maintain a balance between exploration and exploitation which is critical for a good performances of SA. We perform this through a dynamic variation of the Huang cooling parameter during the run. The proposed method was applied on many benchmark functions and encouraging results are presented.
international conference on big data | 2017
Rohaifa Khaldi; Raddouane Chiheb; Abdellatif El Afia; Adil Akaaboune; Rdouan Faizi
The focus of this paper is on investigating the feasibility of using ANFIS combined with DEA for suppliers post-evaluation. The proposed framework aims at modeling performance measurement, and forecasting of a selected hospitals drug suppliers. Even though it is broadly employed as a benchmarking tool to evaluate DMUs efficiency, DEA can hardly be used to predict the performance of unseen DMUs. For this reason, ANFIS model has been integrated to DEA due to its nonlinear mapping, strong generalization capabilities and pattern prediction functionalities. DEA based BCC model is used to evaluate the efficiency scores of a set of suppliers, then ANFIS intervenes to learn DEA patterns and to forecast the performance of new suppliers. The results of this research highlight the prediction power of the proposed model in a new scope. They present it as an efficient benchmarking tool and a promising decision support system applied at the operational level.
international conference on big data | 2017
Rohaifa Khaldi; Abdellatif El Afia; Raddouane Chiheb; Rdouan Faizi
Blood demand and supply management are considered one of the major components of a healthcare supply chain, since blood is a vital element in preserving patients life. However, forecasting it faces several challenges including frequent shortages, and possible expiration caused by demand uncertainty of hospitals. This uncertainty is mainly due to high variability in the number of emergency cases. Thereupon, this investigation presents a real case study of forecasting monthly demand of three blood components, using Artificial Neural Networks (ANNs). The demand of the three blood components (red blood cells (RBC), plasma (CP) and platelets (PFC)) and other observations are obtained from a central transfusion blood center and a University Hospital. Experiments are carried out using three networks to forecast each blood component separately. Last, the presented model is compared with ARIMA to evaluate its performance in prediction. The results of this study depict that ANN models overcomes ARIMA models in demand forecasting. Thus high ANN models can be considered as a promising approach in forecasting monthly blood demand.
international conference on intelligent systems theories and applications | 2014
Mohammed Romadi; Rachid Oulah Haj Thami; Rahal Romadi; Raddouane Chiheb
In this paper, we present a robust approach of automatic detection and recognition of road signs in national roads, starting from the images resulting from a video stream taken by a camera embarked on a vehicle. Our approach is composed of three main phases: the first phase is to extract video stream images containing a circle or a triangle. This extraction is performed respectively by Hough transformation and Ramer-Douglas-Peucker filter, the second phase consists of extraction areas of the calculated image, in the previous phase. In the third and last phase, we proceed to a matching of the extracted image areas with signs of reference by comparison of interest points extracted by the SURF method and the matching method FLANN.
ieee international colloquium on information science and technology | 2016
Sara Jebbor; Abdellatif El Afia; Raddouane Chiheb; Fatima Ouzayd
Nowadays, scientific researches attribute a crucial importance to hospital sector. The improvement of this sector is considered by the literature as strongly depending on well mastered hospital supply chain, more exactly drug supply chain. Hence, several research studies have worked on the management of drug supply chain by offering diverse approaches and management models. In this paper, we focus on stochastic drug supply chain and our goal is to present a literature review about the drug supply chain and drug supply and inventory management methods, in order to establish a comparative analysis of the methods and approaches recently proposed by various research projects to identify the most appropriate one.
2016 3rd International Conference on Logistics Operations Management (GOL) | 2016
Sara Jebbor; Abdellatif El Afia; Raddouane Chiheb; Fatima Ouzayd
This paper aims to improve the management of internal drug logistic flows and both, reduce and master the different stochastic aspects in order to increase the degree of flexibility and robustness of drug supply chain. To achieve this goal, we propose as the first solution the KANBAN system implementation with a priority management policy, as an effective management system for hospital supply and inventory. The second proposition is a flexible structure implementation to reduce and master the stochastic effect coming from suppliers. Finally, the Colored & Timed Petri Net is used to model drug logistic flows with the two above propositions, and will serve as a control and decision-support tool for drug supply chain.
Proceedings of the 2017 International Conference on Smart Digital Environment | 2017
Taoufiq Zarra; Raddouane Chiheb; Rajae Moumen; Rdouan Faizi; Abdellatif El Afia
Recently, the multiplication of communication and sharing platforms such as social networks, personal blogs, forums, etc., has facilitated the expression of views and opinions about products, personalities, and public policy. However, gathering these points of view is a complex task that requires resolution of many problems in different disciplines, especially issues related to our language. Among the research areas, topic modeling and sentiment analysis stimulates interest and curiosity of the scientific community. Lately, the current economic, geo-political and geostrategic trends have made researchers specifically more interested in Arabic language, except that the majority of these studies focus on the classical Arabic; nevertheless it is a language of the elites which is different from what is mainly used on the Web. Our paper focuses on Maghrebi colloquial Arabic since the little research that exists in this area is limited to East colloquial Arabic. On a corpus extracted from different Facebook pages we implemented a supervised approach to extract the sentiments, and an unsupervised approach to extract topic, then we proposed a new, semi-supervised, approach in the Arabic language that combines the topic and the sentiment in a single model, in order to join each topic to a specific sentiment.
Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications | 2018
Taoufiq Zarra; Raddouane Chiheb; Rdouan Faizi; Abdellatif El Afia
Virtual collaboration is intuitive and highly developed for most students attending todays schools. Our contribution aims to analysis, using the machine learning technique, some pedagogical benefits of discussions forums for the teaching staff, to help identify keys successes or weakness in courses assimilation. For this purpose, we use a variant of the probabilistic model of latent Dirichlet allocation (LDA) to ensure a better visual supervision of the topics discussed by the students. We will present our platform and discuss the obtained results.