Search results for "Bayesian Network"
showing 10 items of 70 documents
Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing
2018
International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…
Gender, Coping, and Mental Health: a Bayesian Network Model Analysis
2016
We examined the relationships among gender, coping, and mental health in terms of probabilities. We selected a sample of university students (N = 131) aged between 18 and 32 years, and used the GHQ-28 and COPE instruments for analysis. The Bayesian network model that we constructed showed higher probabilities of symptoms of mental health problems for emotion-focused coping than for problem-focused coping. No differences were found regarding gender. This suggests that the use of problem-focused coping is more recommendable for both male and female university students, and it may also provide some benefits in terms of treatment of symptoms of mental health problems. However, to further verify…
Active and Secretory IgA-Coated Bacterial Fractions Elucidate Dysbiosis in Clostridium difficile Infection
2016
C. difficile is a major enteric pathogen with worldwide distribution. Its expansion is associated with broad-spectrum antibiotics which disturb the normal gut microbiome. In this study, the DNA sequencing of highly active bacteria and bacteria opsonized by intestinal secretory immunoglobulin A (SIgA) separated from the whole bacterial community by FACS elucidated how the gut dysbiosis promotes C. difficile infection (CDI). Bacterial groups with inhibitory effects on C. difficile growth, such as Lactobacillales, were mostly inactive in the CDI patients. C. difficile was typical for the bacterial fraction opsonized by SIgA in patients with CDI, while Fusobacterium was characteristic for the S…
The Monoclonal Antitoxin Antibodies (Actoxumab–Bezlotoxumab) Treatment Facilitates Normalization of the Gut Microbiota of Mice with Clostridium diffi…
2016
Antibiotics have significant and long-lasting impacts on the intestinal microbiota and consequently reduce colonization resistance against Clostridium difficile infection (CDI). Standard therapy using antibiotics is associated with a high rate of disease recurrence, highlighting the need for novel treatment strategies that target toxins, the major virulence factors, rather than the organism itself. Human monoclonal antibodies MK-3415A (actoxumab–bezlotoxumab) to C. difficile toxin A and toxin B, as an emerging non-antibiotic approach, significantly reduced the recurrence of CDI in animal models and human clinical trials. Although the main mechanism of protection is through direct neutraliza…
An Autonomic System for Estimating Human Presence through Bayesian Networks
2010
In the Ambient Intelligence (AmI) context, a relevant research topic is represented by the methods for determining users' presence in order to design context-aware systems capable of monitoring the environment in which they operate, and of timely reacting to changes. This work describes an autonomic software agent comprising a double-level reasoning. At the lower level, a Bayesian network merges the available sensory information related to the users' presence, whereas the upper level performs a meta-reasoning on the system performance and configuration in order to enable the system self-assessment. Experimental results show the validity of the proposed method on a sample scenario.
Multi-sensor Fusion through Adaptive Bayesian Networks
2011
Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.
Fast Fingerprints Classification Only Using the Directional Image
2007
The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.
Integrating the human factor in FMECA-based risk evaluation through Bayesian networks
2020
The contribution of the present paper aims to develop the traditional Failure Modes, Effects and Criticality Analysis (FMECA) for quantitative risk analysis from a Bayesian Network (BN)-based perspective. The main purpose of research consists in providing a framework for analysing causal relationships for risk evaluation and deriving probabilistic inference among significant risk factors. These parameters will be represented by linguistic variables and will include the human factor as a key element of analysis. Traditional approaches for risk evaluation and management performed by FMECA [1] represent helpful tools to globally enhance systems and processes conditions [2]. However, such appro…
Medical news aggregation and ranking of taking into account the user needs
2019
The purpose of this work is to develop an intelligent information system that is designed for aggregation and ranking of news taking into account the needs of the user. The online market for mass media and the needs of readers, the purpose of their searches and moments is not enough to find the news is analyzed. A conceptual model of the information aggression system and ranking of news that would enable presentation of the work of the future intellectual information system, to show its structure is constructed. The methods and means for implementation of the intellectual information system are selected. An online resource for aggregation and ranking of news, news feeds and flexible setting…