Search results for "Bayesian network"

Non-communicable diseases, socio-economic status, lifestyle and well-being in Italy: An additive Bayesian network model

2018

The aim of the paper is to investigate the statistical association, on a sample of Italian subjects, extracted by Survey of Health, Ageing and Retirement in Europe (SHARE) dataset, between chronic diseases (occurrence or number of chronic diseases) and socio-economic and behavioural determinants (lifestyle indicators, QoL indicators, cognitive functioning variables). To this aim, additive Bayesian network (ABN) analysis was used. The resulting ABN model shows that better educated individuals have better health outcomes, age is direct and gender is an indirect determinant of the number of chronic diseases. Furthermore, self-perceived health is associated with lower number of chronic diseases…

Benefits of a dance group intervention on institutionalized elder people: A Bayesian network approach

2018

[EN] The present study aims to explore the effects of an adapted classical dance intervention on the psychological and functional status of institutionalized elder people using a Bayesian network. All participants were assessed at baseline and after the 9 weeks period of the intervention. Measures included balance and gait, psychological well-being, depression, and emotional distress. According to the Bayesian network obtained, the dance intervention increased the likelihood of presenting better psychological well-being, balance, and gait. Besides, it also decreased the probabilities of presenting emotional distress and depression. These findings demonstrate that dancing has functional and …

A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning

2013

Published version of a chapter in the book: Recent Trends in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-38577-3_7 Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this chal…

Using Bayesian networks to describe hydrologic processes

2014

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014 The goal for this Masters thesis is to explore the use of dynamic Bayesian networks for describinghydrologic processes. The main intent is to try and provide better descriptions of the uncertainties thatare tied to dealing with such complex and partially unknown processes, while also trying to reducethese uncertainties. For this purpose I have translated part of a well known and widely useddeterministic model, the snow module of the HBV model, into a dynamic Bayesian network.

A Framework for Assessing the Condition of Crowds Exposed to a Fire Hazard Using a Probabilistic Model

2014

Published version of an article in the journal: International Journal of Machine Learning and Computing. Also available from the publisher at: http://dx.doi.org/10.7763/IJMLC.2014.V4.379 open Access Allocating limited resources in an optimal manner when rescuing victims from a hazard is a complex and error prone task, because the involved hazards are typically evolving over time; stagnating, building up or diminishing. Typical error sources are: miscalculation of resource availability and the victims’ condition. Thus, there is a need for decision support when it comes to rapidly predicting where the human fatalities are likely to occur to ensure timely rescue. This paper proposes a probabil…

Decomposition of Dynamic Single-Product and Multi-product Lotsizing Problems and Scalability of EDAs

2008

In existing theoretical and experimental work, Estimation of Distribution Algorithms (EDAs) are primarily applied to decomposable test problems. State-of-the-art EDAs like the Hierarchical Bayesian Optimization Algorithm (hBOA), the Learning Factorized Distribution Algorithm (LFDA) or Estimation of Bayesian Networks Algorithm (EBNA) solve these problems in polynomial time. Regarding this success, it is tempting to apply EDAs to real-world problems. But up to now, it has rarely been analyzed which real-world problems are decomposable. The main contribution of this chapter is twofold: (1) It shows that uncapacitated single-product and multi-product lotsizing problems are decomposable. (2) A s…

Bayesian network based pathway analysis of microarray data

2011

A Physiological Approach for Minimizing Dead Reckoning Velocity Readings Drifts

2018

The evolution of the geo-positioning methods made Dead Reckoning (DR) one of the most important concern due to its performance in indoor pedestrian localization systems. This paper focuses on implementing an approach that relies on physiological parameters to minimize additive velocity error due to noise in pedestrian DR system.

Classification and retrieval on macroinvertebrate image databases

2011

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing …

A Novel Bayesian Network Based Scheme for Finding the Optimal Solution to Stochastic Online Equi-partitioning Problems

2014

A number of intriguing decision scenarios, such as order picking, revolve around partitioning a collection of objects so as to optimize some application specific objective function. In its general form, this problem is referred to as the Object Partitioning Problem (OOP), known to be NP-hard. We here consider a variant of OPP, namely the Stochastic Online Equi-Partitioning Problem (SO-EPP). In SO-EPP, objects arrive sequentially, in pairs. The relationship between the arriving object pairs is stochastic: They belong to the same partition with probability p. From a history of object arrivals, the goal is to predict which objects will appear together in future arrivals. As an additional compl…