0000000001039670
AUTHOR
F. Mendes
Recommender system for combination of learning elements in mobile environment
5 pages; International audience; The paper presents an ongoing research about the development of a new recommender system dedicated to m-learning. This system is an extension of content based recommender system proposals. It's made of three levels architecture: 1/ a domain model describing the knowledge of teaching, 2/ a user model defining learner's profile and learning's context, 3/ an adaptation model containing rules and metaheuristics, which aims at combining learning modules. Our system takes into account the spatio-temporal context of the learners, the evolution of learner's profile and the dynamic adaptation of modules during the learning process in a mobile environment. The result …
SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
Abstract Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in su…
Tourism-km: A variant of MMKP applied to the tourism domain
International audience; We are interested in an original real-world problem coming from tourism field. We describe a modelling of the problem and propose a first approach that mixes knowledge management and operational research methods. Our algorithms have been implemented in order to produce tourism solutions that are not unique for a given request but that take into account the preferences of the tourist user and provide a personalized solution. We report computational results obtained on real-world instances.