SMILE: smart monitoring intelligent learning engine. An ontology-based context-aware system for supporting patients subjected to severe emergencies

Remote healthcare has made a revolution in the healthcare domain. However, an important problem this field is facing is supporting patients who are subjected to severe emergencies (as heart attacks) to be both monitored and protected while being at home. In this paper, we present a conceptual framework with the main objectives of: 1) emergency handling through monitoring patients, detecting emergencies and insuring fast emergency responses; 2) preventing an emergency from happening in the first place through protecting patients by organising their lifestyles and habits. To achieve these objectives, we propose a layered middleware. Our context model combines two modelling methods: probabilistic modelling to capture uncertain information and ontology to ease knowledge sharing and reuse. In addition, our system uses a two-level reasoning approach (ontology-based reasoning and Bayesian-based reasoning) to manage both certain and uncertain contextual parameters in an adaptive manner. Bayesian network is learned from ontology. Moreover, to ensure a more sophisticated decision-making for service presentation, influence diagram and analytic hierarchy process are used along with regular probabilistic rules (confidence level) and basic semantic logic rules

The Impact of Basel III Capital Regulation on Credit Risk: A Hybrid Model

This research examined the impact of Basel III capital regulation (BCR) on credit risk (CR) using a sample of 25 commercial banks in Lebanon over the period 2012–2017. BCR is measured using the capital adequacy ratio (CAR) and the common equity tier one ratio (CET1 ratio), CR is measured using net provision for credit losses /total assets. To analyze the data, we constructed a hybrid model based on 3 statistical approaches. First, we modelled the dual impact of BCR and CR using probabilistic inference in the framework of Bayesian Belief Network formalism (BBN). Second, to highlight more about the correlation between BCR and CR, we used Spearman correlation test as a nonparametric approach. Third to study the simultaneous effect of CAR and CET1 ratio on CR we applied multivariate regression analysis. By analyzing the probabilistic inference for the first approach we concluded that there is an effect of BCR on CR especially for the high level of CET1 ratio, but when we investigated more if this effect is significant using the Spearman correlation test and the multivariate regression analysis, we concluded that there is no effect statistically significant of Basel III capital regulation (BCR) on credit risk (CR)

PHEN : parkinson helper emergency notification system using Bayesian Belief Network

Context-aware systems are used to aid users in their daily lives. In the recent years, researchers are exploring how context aware systems can benefit humanity through assist patients, specifically those who suffer incurable diseases, to cope with their illness. In this paper, we direct our work to help people who suffer from Parkinson disease. We propose PHEN, Parkinson Helper Engine Network System, a context-aware system that aims to support Parkinson disease patients on m any levels. We use ontology is for context representation and modeling. Then the ontology based context model is used to learn with Bayesian Belief network (BBN) which is beneficial in handling the uncertainty aspect of context-aware systems

Contributions à la découverte des stratégies d'écriture d'élèves de scolarité primaire

L'objet de cet article est celui de présenter une étude longitudinale qui assure une aide au suivi d'élèves typique en scolarité primaire. Nous proposons une méthode pour la découverte de groupes d'élèves partageant les mêmes stratégies1 d'écriture tout au long de leur scolarité primaire. Acquis à partir des tests d'écritures et de dessin, nos données sont représentées par un modèle graphique probabiliste, les "réseaux bayésiens". La structure du réseau étant partiellement déterminée par des connaissances a priori des experts. Une stratégie d'écriture est représentée dans le réseau par une variable cachée. Nous considérons d'abord que chacun des tests d'écritures est représenté par une stratégie locale et qu'il existe une stratégie globale qui agit sur chacune des stratégies locales. Nous construisons ainsi un modèle Globale Hierarchie (GH), ensuite nous utilisons l'inférence bayésien comme un outil de classification automatique pour pouvoir lier les stratégies locales aux stratégies globales et modéliser la causalité (ou la dépendance) entre variables et stratégie donnée. Il ressort (découle) de l'inférence que dans chaque test, deux stratégies locales regroupant 86% de la population, puis nous redécouvrons ces deux stratégies locales dans deux stratégies globales(Glob1 et Glob4), ces derniers correspondent à des élèves normo-scripteurs (NS) et normo-scripteurs plus avancés (NSA). De point de vu longitudinal, la distribution des élèves par niveau scolaire des stratégies d'écriture globales (NS et NSA) est constante pour les trois périodes d'acquisitions, de même la probabilité transition entre NS et NSA est encore constante au cours du temps. Enfin les enfants passent graduellement de G1 a G4 par niveau scolaire.The aim of this study is to bring a contribution to the realization of the evolution follow-up in writing among typical pupils in primary education. For this purpose, we have developed a software for the acquisition of handwritten tracings and the automatic extraction of features from these tracings. Distributed on three periods of about six months each, the acquisitions have therefore been achieved three times for the same pupils in the same experimental conditions, these tracings being acquired online by means of a digitizer. An unsupervised classification is first applied on a set of dynamic features chosen by an expert in the field of child's development psychology; strong forms are thus selected as steady clusters from the obtained partitions. With this unsupervised approach, we have thus discovered three strategies: a first one which is performant in control and global planning, a second one labeled local in control and planning, and a third one which is an unstable intermediary strategy. Next we modeled our problem by means of a probabilistic graphical model (bayesian network) in which the writing strategy is represented by a hidden variable. We build a global hierarchical model in order to link local and global strategies and model the probabilistic dependance between variables and strategies. Our hierarchical model, learnt with real data, enables us to discover two global strategies that correspond to normo-writer pupils and more advanced normo-writers. These two strategies are consistent: the distribution of typical pupils by school level is constant over time, and the probability of transition between (or within) these strategies is also constant over time.ROUEN-BU Sciences Madrillet (765752101) / SudocROUEN-BU Sciences (764512102) / SudocSudocFranceF