• Journal of applied mathematics & informatics
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• v.35 no.3_4
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• pp.371-385
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• 2017

Jain and Saraswat (2012) introduced new generalized f-information divergence measure, by which we obtained many well known and new information divergences. In this work, we introduce new information inequalities in absolute form on this new generalized divergence by considering convex normalized functions. Further, we apply these inequalities for getting new relations among well known divergences, together with numerical verification. Application to the Mutual information is also presented. Asymptotic approximation in terms of Chi- square divergence is done as well.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1333-1336
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• 2015

감성분석은 최근 오피니언 마이닝에서 주목받고 있는 분야로써, 특정 주제, 상품, 유명인사 등에 대한 사람들의 반응을 긍정 또는 부정으로 구분하거나 점수를 이용하여 긍정 또는 부정의 강도를 분석하는데 이용되고 있다. PMI(pointwise mutual information)와 SO-PMI(semantic orientation from pointwise mutual information)는 비교적 빠르고 간편하게 극성을 판단할 수 있다는 장점이 있지만, 어휘와 기준 어휘 사이의 극성 값이 넓은 범위를 갖는다는 단점이 있다. 본 논문에서는 일상적인 언어 사용 환경에서 나타나는 어휘로부터 감성을 분석하고자 하였다. 특히 어휘의 극성 값 편차로 인해 나타날 수 있는 어려움을 보완하기 위해 NPMI(normalized pointwise mutual information)를 이용하여 어휘의 감성을 분석하였다. PMI와 NPMI를 비교 분석한 결과 어휘의 감성 강도를 나타내는 데 있어서 밀집도에서 큰 차이를 보였다.

• Genomics & Informatics
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• v.18 no.4
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• pp.41.1-41.8
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• 2020

We provide an algorithm for the construction and analysis of autocorrelation (information) functions of gene nucleotide sequences. As a measure of correlation between discrete random variables, we use normalized mutual information. The information functions are indicative of the degree of structuredness of gene sequences. We construct the information functions for selected gene sequences. We find a significant difference between information functions of genes of different types. We hypothesize that the features of information functions of gene nucleotide sequences are related to phenotypes of these genes.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.691-696
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• 2009

Introduction: There are two types of asthma according to aspirin hypersensitivity: aspirin intolerant asthma (AIA) and aspirin tolerant asthma (ATA). The genetic risk factors that are related with asthma have been investigated intensively and extensively. However the combinatory effects of single nucleotide polymorphisms (SNPs) have hardly been evaluated. In this paper we searched the best set of SNPs that are useful to diagnose the two types of asthma. Methods: We examined 246 asthmatic patients (94 having aspirin intolerant asthma and 152 having aspirin tolerant asthma) and analyzed 25 SNPs typed in them, which are suspected to be associated with asthma. Normalized mutual information values of combinations of typed SNPs are calculated, and those with high normalized mutual information values are selected. We use support vector machines to evaluate the prediction accuracy of the selected combinations. Results: The best combination model turns out four-locus and consists of ALOX5_p1_1708, B2ADR_q1_46, CCR3_p1_520, CysLTR1_p1_634. Its normalized mutual information value is 0.053 and the accuracy in predicting ATA disease risk among asthmatic patients is 71.14%.

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.36-41
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• 2015

This paper is proposed a wavelet-based MCDT(Mask Coefficient Differential and Threshold) method of image registration of Multi-images contaminated with visible image and infrared image. The method for ensure reliability of the image registration is to the increase statistical corelation as getting the common feature points between two images. The method of threshold the wavelet coefficients using derivatives of the wavelet coefficients of the detail subbands was proposed to effectively registration images with distortion. And it can define that the edge map. Particularly, in order to increase statistical corelation the method of the normalized mutual information. as similarity measure common feature between two images was selected. The proposed method is totally verified by comparing with the several other multi-image and the proposed image registration.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.537-544
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• 2013

This paper proposes a recursive localized Normalized Mutual Information(NMI) algorithm to overcome shortcomings of the conventional NMI algorithm and the localized NMI algorithm which proposed before. The localized NMI algorithm divides images into few fixed size segments and applies NMI algorithm to each segments. By extension, the proposed algorithm uses variable size segments using its characteristic. Dividing each segment recursively, the algorithm selects a suitable segment size and improves a performance of the image registration. Experimental result shows the performance of the proposed algorithm.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.39-47
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• 2002

The objective of this study is to provide a Precise form of spatial normalization with affine transformation. The quantitative comparison of the brain architecture across different subjects requires a common coordinate system. For the common coordinate system, not only global brain but also a local region of interest should be spatially normalized. Registration using mutual information generally matches the whose brain well. However. a region of interest may not be normalized compared to the feature-based methods with the landmarks. The hybrid method of this Paper utilizes feature information of the local region as well as intensity similarity. Central gray nuclei of a brain including copus callosum, which is used for feature in Schizophrenia detection, is appropriately normalized by the hybrid method. In the results section. our method is compared with mutual information only method and Talairach mapping with schizophrenia Patients. and is shown how it accurately normalizes feature .

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.6
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.473-479
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• 2019

This study applied SGM (Semi Global Matching) to generate DSM (Digital Surface Model) using WorldView-1 high-resolution satellite stereo pair in Terrassa, Spain provided by ISPRS (International Society for Photogrammetry and Remote Sensing). The SGM is an image matching algorithm that performs the computation of the matching cost for the stereo pair in multi-paths and aggregates the computed costs sequentially. This method finally calculates the disparity corresponding to the minimum (or maximum) value of the aggregation cost. The cost was applied to MI (Mutual Information), NCC (Normalized Cross-Correlation), and CT (Census Transform) in order to the SGM. The accuracy and performance of the outline representation result in DSM by each cost are presented. Based on the images used and the subject area, the accuracy of the CT cost results was the highest, and the outline representation was also most clearly depicted. In addition, while the SGM method represented more detailed outlines than the existing software, many errors occurred in the water area.