• Journal of KIISE:Computing Practices and Letters
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• v.15 no.2
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• pp.135-139
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• 2009

We present a new ensemble learning method that employs the set of region experts, each of which learns to handle a subset of the training data. We split the training data and generate experts for different regions in the feature space. When classifying a data, we apply a weighted voting among the experts that include the data in their region. We used ten datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as Bagging and Adaboost. We used SMO, Naive Bayes and C4.5 as base learning algorithms. As a result, we found that the performance of our method is comparable to that of Adaboost and Bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1167-1174
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• 1999

In this paper, we present a new technique for the local decomposition of convex structuring elements for morphological image processing. Local decomposition of a structuring element consists of local structuring elements, in which each structuring element consists of a subset of origin pixel and its eight neighbors. Generally, local decomposition of a structuring element reduces the amount of computation required for morphological operations with the structuring element. A unique feature of our approach is the use of linear integer programming technique to determine optimal local decomposition that guarantees the minimal amount of computation. We defined a digital convex polygon, which, in turn, is defined as a convex structuring element, and formulated the necessary and sufficient conditions to decompose a digital convex polygon into a set of basis digital convex polygons. We used a set of linear equations to represent the relationships between the edges and the positions of the original convex polygon, and those of the basis convex polygons. Further. a cost function was used represent the total processing time required for computation of dilation/erosion with the structuring elements in a decomposition. Then integer linear programming was used to seek an optimal local decomposition, that satisfies the linear equations and simultaneously minimize the cost function.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Biomedical Science Letters
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• v.18 no.4
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• pp.413-419
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• 2012

The melanoma differentiation-associated gene-7 (MDA-7) protein, also known as interleukin-24 (IL-24), is a novel candidate of tumor suppressor that can induce apoptosis experimentally in a variety of human malignant cells. However, there have been few studies about its role in colorectal cancer. We performed immunohistochemical detection of MDA-7/IL-24 in 399 tissue samples from primary colorectal adenocarcinoma patients using a tissue microarray. Western blotting was then done to confirm the immunohistochemical observations. MDA-7/IL-24 immunoreactivity was observed in 116 (29.1%) of the 399 colorectal adenocarcinoma cases. Analysis of the MDA-7/IL-24 expression by Western blotting confirmed the immunohistochemical results. The tumors with a negative MDA-7/IL-24 expression more frequently showed poor differentiation (P=0004), lymph node metastasis (P=0.001), deep invasion (P=0.008) and high stage (P=0.001). A subset of colorectal adenocarcinoma revealed a decreased expression of MDA-7/IL-24, and this was associated with progressive pathologic features. These findings suggest that loss of MDA-7/IL-24 expression may play a role in tumor growth and progression of colorectal adenocarcinomas.

• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.137-152
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• 2012

As social media such as facebook and twitter is widespread, social commerce appears as a new way of e-commerce. Social commerce is a subset of electronic commerce that involves using social media that supports social interaction and user contributions. From the understanding of social commerce and literature review, we deduced the research model that relationship, collectivism, convenience, usefulness, low price, reputation, and rapidness have influence on satisfaction and repurchase intention. As a result of data analysis, relationship, convenience, usefulness, low price, and rapidness had influence on satisfaction and repurchase intention. And collectivism had influence on relationship. But reputation did not have influence on satisfaction. The focus of this study is on the effect of relationship and collectivism on satisfaction and repurchase, because relationship and collectivism is the major feature of social commerce. Succinctly speaking, collectivism affects relationship among users, in turn, relationship affects satisfaction and repurchase intention. This study gives a contribution to business. Business related with social commerce has to make a business strategy for managing relationship and consider collectivism tendency for selecting target customer.

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.45-52
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• 2014

This paper proposes an efficient photo retrieval system that automatically indexes for searching of relevant images, using a combination of geo-coded information, direction/location of image capture device and content-based visual features. A photo image is labeled with its GPS (Global Positioning System) coordinates and direction of the camera view at the moment of capture, and the label leads to generate a geo-spatial index with three core elements of latitude, longitude and viewing direction. Then, content-based visual features are extracted and combined with the geo-spatial information, for indexing and retrieving the photo images. For user's querying process, the proposed method adopts two steps as a progressive approach, filtering the relevant subset prior to use a content-based ranking function. To evaluate the performance of the proposed scheme, we assess the simulation performance in terms of average precision and F-score, using a natural photo collection. Comparing the proposed approach to retrieve using only visual features, an improvement of 20.8% was observed. The experimental results show that the proposed method exhibited a significant enhancement of around 7.2% in retrieval effectiveness, compared to previous work. These results reveal that a combination of context and content analysis is markedly more efficient and meaningful that using only visual feature for image search.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.343-349
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• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1125-1132
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• 2010

WiBro(Wireless Broadband Internet) is the standard of high-speed portable internet based on OFDMA/TDD (Orthogonal frequency division multiple access / Time division duplexing) techniques, and the subset of consolidated version of IEEE802.16e Wireless MAN standard. In this paper, we propose performance improvements of WiBro system using the 64QAM SOFM(Self-Organizing Feature Maps)prefiltering. Proposed method used the prefiltering SOFM neural network blind equalization in the Broadband 64 QAM WiBro system receiver. The prefiltering SOFM neural network constellates 64QAM that is transmitter data shape and the blind equalization removes ICI(Inter Carrier Interference). To verificate the proposed method usability, the MSE and the BER are simulated. The simulation results shown that is improved the performances of the proposed WiBro system using the 64QAM SOFM Prefiltering than the existing WiBro system.

• Annual Conference of KIPS
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• 2008.11a
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• pp.1563-1566
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• 2008

본 논문에서는 데이터의 전처리과정으로 SNMP MIB 데이터에 대한 속성 부분집합의 선택 방법(attribute subset selection)을 사용하여 특징선택 및 축소(feature selection & reduction)를 실시하였다. 또한 데이터 마이닝의 대표적인 해석학적 분석 모델인 연관관계규칙기법(association rule mining)을 이용하여 트래픽 폭주 공격 및 공격유형별 SNMP MIB 데이터에 내재되어 있는 특징들을 규칙의 형태로 추출하여 분석하는 의미론적 심층해석을 실시하였다. 공격유형에 대한 패턴 규칙의 추출 및 분석은 공격이 발생한 프로토콜에 대해서만 서비스를 제한하고 관리할 수 있는 정책적 근거를 제공함으로써 보다 안정적인 네트워크 환경과 원활한 자원관리를 지원할 수 있다. 본 논문에서 제시한 트래픽 폭주 공격 및 공격유형별 데이터로부터의 자동적 특징의 규칙 추출 및 의미론적 해석방법은 침입탐지 시스템을 위한 새로운 방법론에 모멘텀을 제시할 수 있다는 긍정적인 가능성과 함께 침입탐지 및 대응시스템의 정책 수립을 지원할 수 있을 것으로 기대된다.

• IMMUNE NETWORK
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• v.23 no.1
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• pp.5.1-5.28
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• 2023

Th cell lineage determination and functional specialization are tightly linked to the activation of lineage-determining transcription factors (TFs) that bind cis-regulatory elements. These lineage-determining TFs act in concert with multiple layers of transcriptional regulators to alter the epigenetic landscape, including DNA methylation, histone modification and threedimensional chromosome architecture, in order to facilitate the specific Th gene expression programs that allow for phenotypic diversification. Accumulating evidence indicates that Th cell differentiation is not as rigid as classically held; rather, extensive phenotypic plasticity is an inherent feature of T cell lineages. Recent studies have begun to uncover the epigenetic programs that mechanistically govern T cell subset specification and immunological memory. Advances in next generation sequencing technologies have allowed global transcriptomic and epigenomic interrogation of CD4+ Th cells that extends previous findings focusing on individual loci. In this review, we provide an overview of recent genome-wide insights into the transcriptional and epigenetic regulation of CD4+ T cell-mediated adaptive immunity and discuss the implications for disease as well as immunotherapies.