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Abstract-We are currently witnessing the launch and development of a large number of distance training devices in Moroccan universities, whose main objective is to meet society's requirements and the knowledge economy, which is fully emerging. All of the devices are based on the use of elearning platforms, which can be problematic for designers for different reasons (costs, utility, usability, etc.). Being conscious of the impact of these technological tools on learning, we propose a methodical approach that identifies the essential criteria for evaluation of e-learning platforms to fit the needs of teachers and learners from analysis of the evaluation dimensions in multimedia documents, particularly through the dimensions of utility and usability.

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The most of collected data samples from E-learning systems consist of correlated information caused by overlapping input instances, which decrease the classifier credibility and reliability. This paper presents an improved classification model based on Deep Learning and Principal Component Analysis (PCA) method as its use in reducing the dimensionality of data. By this task, we introduce the best learning process to extract just the useful parameters that describe students’ per-formances in an E-learning system. One of the primary goals of this technique is to help earlier in detecting the dropouts and discovering of students who need special attention, so that the teachers could provide the appropriate counseling at the right time. This study presents the proposal approach and its algorithms. In addition, it shows how deep neural network was modeled in the training phase, and how PCA helps in the elimination of correlated information in our dataset to increase the classifier performance. Finally, we introduce an example of an appli-cation of the method in a data mining scenario, find out more references for fur-ther information.

This paper deals with the production planning and control problem of a single product involving combined manufacturing and remanufacturing operations. We investigate here a lot-sizing problem in which the demand for items can be satisfied by both the new and the remanufactured products. We assume that produced and recovered items are of the same quality. Two types of inventories are involved in this problem. The produced items are stored in the first inventory. The returned products are collected in the second inventory and then remanufactured. The objective of this study is to propose a manufacturing/remanufacturing policy that would minimize the holding, the set up and preparation costs. The decision variables are the manufacturing and the remanufacturing rates. The paper proposes an extension of the reverse Wagner/Whitin dynamic production planning and inventory control model, a Memetic Algorithm (MA) and a Hybrid Algorithm (HA). The HA was improved with a post-optimization procedure using Path Relinking. Numerical experiments were conducted on a set of 300 instances with up to 48 periods. The results show that both methods give high-quality solutions in moderate computational time.