Predicting Academic Performance by Data Mining Methods

Academic failure among first-year university students has long fuelled a large number of debates. Many educational psychologists have tried to understand and then explain it. Many statisticians have tried to foresee it. Our research aims to classify, as early in the academic year as possible, students into three groups: the 'low-risk' students, who have a high probability of succeeding; the 'medium-risk' students, who may succeed thanks to the measures taken by the university; and the 'high-risk' students, who have a high probability of failing (or dropping out). This article describes our methodology and provides the most significant variables correlated to academic success among all the questions asked to 533 first-year university students during November of academic year 2003/04. Finally, it presents the results of the application of discriminant analysis, neural networks, random forests and decision trees aimed at predicting those students' academic success.Academic performance, decision trees, random forests, neural networks, discriminant analysis, education, prediction,

Using a KDD process to forecast the duration of surgery

This paper presents a methodological framework for planning surgery in operating theatre suites based on data warehousing and knowledge discovery in database approaches. We suggest a decisional tool which estimates the appropriate duration for a patient to be in the operating theatre. To achieve this, we first describe a data warehouse model used to extract data from various, possibly non-interacting, databases. Then we compare two data mining methods: rough sets and neural networks. The aim is to identify classes of surgery likely to take different lengths of time according to the patient's profile. These tools permit patients' profiles to be identified from administrative data, previous medical history, etc. The surgical environment (surgeon, type of anesthesia, etc.) is also taken into account in estimating the duration of the surgery.

Supporting Multidisciplinary Teams and Early Design Stages Using Storyboards

Abstract. Current tools for multidisciplinary teams in user-centered software engineering (UCSE) provide little support for the different approaches of the various disciplines in the project team. Although multidisciplinary teams are getting more and more involved in UCSE projects, an efficient approach to communicate clearly and to pass results of a user needs analysis to other team members without loss of information is still missing. Based on previous experiences, we propose storyboards as a key component in such tools. Storyboards contain sketched information of users, activities, devices and the context of a future application. The comprehensible and intuitive notation and accompanying tool support presented in this paper will enhance communication and efficiency within the multidisciplinary team during UCSE projects.

MATIÊRES GRASSES VÉGÉTALES ET ANIMALES DANS LES ALIMENTS D'ALLAITEMENT POUR VEAUX A L'ENGRAIS

International audienc

Analyse de la prevalence de l'anemie ferriprive et de la carence martiale de la malnutrition proteino-energetique moderee et des etats inflammatoires infectieux

CNRS AR 12311 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

A Multi-objective Hospital Operating Room Planning and Scheduling Problem Using Compromise Programming

15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún, Mexico, October 23–28, 2016, Proceedings, Part IInternational audienceThis paper proposes a hybrid compromise programming local search approach with two main characteristics: a capacity to generate non-dominated solutions and the ability to interact with the decision maker. Compromise programming is an approach where it is not necessary to determine the entire set of Pareto-optimal solutions but only some of them. These solutions are called compromise solutions and represent a good tradeoff between conflicting objectives. Another advantage of this type of method is that it allows the inclusion of the decision maker’s preferences through the definition of weights included in the different metrics used by the method. This approach is tested on an operating room planning process. This process incorporates the operating rooms and the nurse planning simultaneously. Three different objectives were considered: to minimize operating room costs, to minimize the maximum number of nurses needed to participate in surgeries and to minimize the number of open operating rooms. The results show that it is a powerful decision tool that enables the decision makers to apply compromise alongside optimal solutions during an operating room planning process