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Data science is the discipline that allows the exploration and analysis of data in order to extract useful and relevant information for decision making and problem solving. In the educational domain, human experiences need to be synthesized in order to improve the success rate and help the responsible to make the best informed decision. Analytic Hierarchical Process (AHP) is one of the most widely used multi-criteria analysis techniques in decision making. It allows building models for various problems even in the case of insufficient observation data. This paper aims to, benefit from the potentials of AHP technique, to analyze students’ profiles. Our objective is to detect and classify the most important fac-tors that increase Moroccan student dropout and failure. We expect that this study is the first one that explores AHP, studying the Moroccan context and describing student profiles depending on variant criteria. It reveals, on the one hand, that Moroccan student failure is strongly related to their family and behavioral characteristics. Indeed, lack of motivation, family instability and lack of responsibility are the top three factors causing failure at the university. On the other hand, student dropout is strongly related to studying context, namely the lack of orientation and repeated failures in modules. These findings will enable the decision makers to develop adequate solutions to overcome these two scourges.

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Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of the driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of the classification of driver behavior. The study aim is to highlight and analyze the different types of driver behavior, data sources, datasets, features, and artificial intelligence techniques used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.INDEX TERMS Driver behavior, intelligent transport system, systematic literature review, machine learning, deep learning.

<p>In recent years, the use of credit cards around the world has grown enormously. Thus, the number of fraud cases have also increased, resulting in losses of thousands of dollars worldwide. Therefore, it is mandatory to use techniques that are able to assist in the detection of credit card fraud. For this purpose, we have proposed a multi-level architecture, composed of four levels: authentication level, behavioral level, smart level and background processing level. In this paper, we focus on the implementation of the smart level. The aim of this level is to develop a classifier for the detection of credit card fraud, using bidirectional gated recurrent units (BGRU). The experiments, applied on well-known credit card fraud dataset from Kaggle, show that this model has peak performance compared to other proposed models, with 97.16% for accuracy rate and 99.66% for the area under the ROC curve.</p>