• Title/Summary/Keyword: Bayesian information

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Curve Clustering in Microarray

  • Lee, Kyeong-Eun
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.575-584
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    • 2004
  • We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.

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Automatic Sputum Color Image Segmentation for Lung Cancer Diagnosis

  • Taher, Fatma;Werghi, Naoufel;Al-Ahmad, Hussain
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.1
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    • pp.68-80
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    • 2013
  • Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

Prediction in run-off triangle using Bayesian linear model (삼각분할표 자료에서 베이지안 모형을 이용한 예측)

  • Lee, Ju-Mi;Lim, Jo-Han;Hahn, Kyu-S.;Lee, Kyeong-Eun
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.411-423
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    • 2009
  • In the current paper, by extending Verall (1990)'s work, we propose a new Bayesian model for analyzing run-off triangle data. While Verall's (1990) work only account for the calendar year and evolvement time effects, our model further accounts for the "absolute time" effects. We also suggest a Markov Chain Monte Carlo method that can be used for estimating the proposed model. We apply our proposed method to analyzing three empirical examples. The results demonstrate that our method significantly reduces prediction error when compared with the existing methods.

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Visual Saliency Detection Based on color Frequency Features under Bayesian framework

  • Ayoub, Naeem;Gao, Zhenguo;Chen, Danjie;Tobji, Rachida;Yao, Nianmin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.2
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    • pp.676-692
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    • 2018
  • Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.

Parallel Bayesian Network Learning For Inferring Gene Regulatory Networks

  • Kim, Young-Hoon;Lee, Do-Heon
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2005.09a
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    • pp.202-205
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    • 2005
  • Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

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A Hierarchical Bayesian Network for Real-Time Continuous Hand Gesture Recognition (연속적인 손 제스처의 실시간 인식을 위한 계층적 베이지안 네트워크)

  • Huh, Sung-Ju;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.36 no.12
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    • pp.1028-1033
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    • 2009
  • This paper presents a real-time hand gesture recognition approach for controlling a computer. We define hand gestures as continuous hand postures and their movements for easy expression of various gestures and propose a Two-layered Bayesian Network (TBN) to recognize those gestures. The proposed method can compensate an incorrectly recognized hand posture and its location via the preceding and following information. In order to vertify the usefulness of the proposed method, we implemented a Virtual Mouse interface, the gesture-based interface of a physical mouse device. In experiments, the proposed method showed a recognition rate of 94.8% and 88.1% for a simple and cluttered background, respectively. This outperforms the previous HMM-based method, which had results of 92.4% and 83.3%, respectively, under the same conditions.

An analysis of the component of Human-Robot Interaction for Intelligent room

  • Park, Jong-Chan;Kwon, Dong-Soo
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.2143-2147
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    • 2005
  • Human-Robot interaction (HRI) has recently become one of the most important issues in the field of robotics. Understanding and predicting the intentions of human users is a major difficulty for robotic programs. In this paper we suggest an interaction method allows the robot to execute the human user's desires in an intelligent room-based domain, even when the user does not give a specific command for the action. To achieve this, we constructed a full system architecture of an intelligent room so that the following were present and sequentially interconnected: decision-making based on the Bayesian belief network, responding to human commands, and generating queries to remove ambiguities. The robot obtained all the necessary information from analyzing the user's condition and the environmental state of the room. This information is then used to evaluate the probabilities of the results coming from the output nodes of the Bayesian belief network, which is composed of the nodes that includes several states, and the causal relationships between them. Our study shows that the suggested system and proposed method would improve a robot's ability to understand human commands, intuit human desires, and predict human intentions resulting in a comfortable intelligent room for the human user.

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