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Electrical and Computer Engineering

Bayesian network

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Full-Text Articles in Physical Sciences and Mathematics

Symptom-Aware Hybrid Fault Diagnosis Algorithm In The Network Virtualization Environment, Yuze Su, Xiangru Meng, Xiaoyang Han, Qiaoyan Kang Jan 2019

Symptom-Aware Hybrid Fault Diagnosis Algorithm In The Network Virtualization Environment, Yuze Su, Xiangru Meng, Xiaoyang Han, Qiaoyan Kang

Turkish Journal of Electrical Engineering and Computer Sciences

As an important technology in next-generation networks, network virtualization has received more and more attention. Fault diagnosis is the crucial element for fault management and it is the process of inferring the exact failure in the network virtualization environment (NVE) from the set of observed symptoms. Although various traditional fault diagnosis algorithms have been proposed, the virtual network has some new characteristics, which include inaccessible fault information of the substrate network, inaccurate network observations, and a dynamic embedding relationship. To solve these challenges, a symptom-aware hybrid fault diagnosis (SAHFD) algorithm in the NVE is proposed in this paper. First, a …

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Probabilistic Data Fusion Model For Heart Beat Detection From Multimodal Physiological Data, Tehseen Zia, Zulqarnian Arif Jan 2017

Probabilistic Data Fusion Model For Heart Beat Detection From Multimodal Physiological Data, Tehseen Zia, Zulqarnian Arif

Turkish Journal of Electrical Engineering and Computer Sciences

Automatic detection of heart beats constitutes the basis for electrocardiogram (ECG) analysis and mainly relies on detecting QRS complexes. Detection is typically performed by analyzing the ECG signal. However, when signal quality is low, it often leads to the triggering of false alarms. A contemporary approach to reduce false alarm rate is to use multimodal data such as arterial blood pressure (ABP) or photoplethysmogram (PPG) signals. To leverage the correlated temporal nature of these signals, a probabilistic data fusion model for heart beat detection is proposed. A hidden Markov model is used to decode waveforms into segments. A Bayesian network …

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Discovery Of The Connection Among Age-Related Macular Degeneration, Mthfr C677t And Pai 1 4g/5g Gene Polymorphisms, And Body Mass Index By Means Of Bayesian Inference Methods, Aydan Çelebi̇ler, Huseyin Seker, Bora Yüksel, Ahmet Orun, Si̇bel Bi̇lgi̇li̇, Muhammet Baysal Karaca Jan 2013

Discovery Of The Connection Among Age-Related Macular Degeneration, Mthfr C677t And Pai 1 4g/5g Gene Polymorphisms, And Body Mass Index By Means Of Bayesian Inference Methods, Aydan Çelebi̇ler, Huseyin Seker, Bora Yüksel, Ahmet Orun, Si̇bel Bi̇lgi̇li̇, Muhammet Baysal Karaca

Turkish Journal of Electrical Engineering and Computer Sciences

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in the elderly. The aim of this study was therefore to explore the relationship between the presence of multiple gene polymorphisms and 2 distinct advanced `dry and wet' AMD phenotypes, and to assess gene interactions with the influence of personal factors in a Turkish population as a pilot study. For the analysis, the data were collected from 73 unrelated participants, grouped as 29 wet and 26 dry AMD patients, and 18 healthy controls. They were all genotyped for the multiple gene polymorphisms in 12 different genes. The data set …

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Scaling Bayesian Network Parameter Learning With Expectation Maximization Using Mapreduce, Erik B. Reed, Ole J. Mengshoel Nov 2012

Scaling Bayesian Network Parameter Learning With Expectation Maximization Using Mapreduce, Erik B. Reed, Ole J. Mengshoel

Ole J Mengshoel

Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for incomplete data. Applying the EM algorithm to learn BN parameters is unfortunately susceptible to local optima and prone to premature convergence. We develop and experiment with two methods for improving EM parameter learning by using MapReduce: Age-Layered Expectation Maximization (ALEM) and Multiple Expectation Maximization (MEM). Leveraging MapReduce for distributed machine learning, these algorithms (i) operate on a (potentially large) population of BNs and (ii) partition the data set as is traditionally done with MapReduce machine learning. For example, we achieved gains using the Hadoop implementation …

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Mapreduce For Bayesian Network Parameter Learning Using The Em Algorithm, Aniruddha Basak, Irina Brinster, Ole J. Mengshoel Nov 2012

Mapreduce For Bayesian Network Parameter Learning Using The Em Algorithm, Aniruddha Basak, Irina Brinster, Ole J. Mengshoel

Ole J Mengshoel

This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups versus the sequential case, and carefully compare various Hadoop cluster configurations in experiments with Bayesian networks of different sizes and structures.

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Adaptive Control Of Bayesian Network Computation, Erik Reed, Abe Ishihara, Ole J. Mengshoel Jul 2012

Adaptive Control Of Bayesian Network Computation, Erik Reed, Abe Ishihara, Ole J. Mengshoel

Ole J Mengshoel

This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable.
To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure …

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