• Journal of the Korea Institute of Information Security & Cryptology
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• 제29권3호
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• pp.531-539
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• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2455-2475
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• 2021

Patient Navigation Program (PNP) is considered as an important implementation of health care systems that can assist in patient's treatment. Due to the feasibility of PNP implementation, a systematic reuse is needed for a wide adoption of PNP computerized system. SPL is one of the promising systematic reuse approaches for creating a reusable architecture to enabled reuse in several similar applications of PNP systems which has its own variations with other applications. However, stakeholder decision making which result from the imprecise, uncertain, and subjective nature of architecture selection based on quality attributes (QA) further hinders the development of the product line architecture. Therefore, this study aims to propose a quality-driven approach using Multi-Criteria Decision Analysis (MCDA) techniques for Software Product Line Architecture (SPLA) to have an objective selection based on the QA of stakeholders in the domain of PNP. There are two steps proposed to this approach. First, a clear representation of quality is proposed by extending feature model (FM) with QA feature to determine the QA in the early phase of architecture selection. Second, MCDA techniques were applied for architecture selection based on objective preference for certain QA in the domain of PNP. The result of the proposed approach is the implementation of the PNP system with SPLA that had been selected using MCDA techniques. Evaluation for the approach is done by checking the approach's applicability in a case study and stakeholder validation. Evaluation on ease of use and usefulness of the approach with selected stakeholders have shown positive responses. The evaluation results proved that the proposed approach assisted in the implementation of PNP systems.

• Wind and Structures
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• 제37권3호
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• pp.211-227
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• 2023

To investigate the non-Gaussian feature of fluctuating wind pressures on rectangular high-rise buildings, wind tunnel tests were conducted on scale models with side ratios ranging from 1/9~9 in an open exposure for various wind directions. The high-order statistical moments, time histories, probability density distributions, and peak factors of pressure fluctuations are analyzed. The mixed normal-Weibull distribution, Gumbel-Weibull distribution, and lognormal-Weibull distribution are adopted to fit the probability density distribution of different non-Gaussian wind pressures. Zones of Gaussian and non-Gaussian are classified for rectangular buildings with various side ratios. The results indicate that on the side wall, the non-Gaussian wind pressures are related to the distance from the leading edge. Apart from the non-Gaussianity in the separated flow regions noted by some literature, wind pressures behind the area where reattachment happens present non-Gaussian nature as well. There is a new probability density distribution type of non-Gaussian wind pressure which has both long positive and negative tail found behind the reattachment regions. The correlation coefficient of wind pressures is proved to reflect the non-Gaussianity and a new method to estimate the mean reattachment length of rectangular high-rise building side wall is proposed by evaluating the correlation coefficient. For rectangular high-rise buildings, the mean reattachment length calculated by the correlation coefficient method along the height changes in a parabolic shape. Distributions of Gaussian and non-Gaussian wind pressures vary with side ratios. It is inappropriate to estimate the extreme loads of wind pressures using a fixed peak factor. The trend of the peak factor with side ratios on different walls is given.

• Journal of the Institute of Electronics and Information Engineers
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• 제52권8호
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• pp.67-73
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• 2015

In a distribution of digital image, there is a serious problem that is the image alteration by a forger. For the problem solution, this paper proposes the forensic decision algorithm of a median filtering (MF) image using the feature vector based on a coefficient of variation (c.v.) of Fourier transform. In the proposed algorithm, we compute Fourier transform (FT) coefficients of row and column line respectively of an image first, then c.v. between neighboring lines is computed. Subsquently, 10 Dim. feature vector is defined for the MF detection. On the experiment of MF detection, the proposed scheme is compared to MFR (Median Filter Residual) and Rhee's MF detection schemes that have the same 10 Dim. feature vector both. As a result, the performance is excellent at Unaltered, JPEG (QF=90), Down scaling (0.9) and Up scaling (1.1) images, and it showed good performance at Gaussian filtering ($3{\times}3$) image. However, in the performance evaluation of all measured items of the proposed scheme, AUC (Area Under ROC (Receiver Operating Characteristic) Curve) by the sensitivity and 1-specificity approached to 1 thus, it is confirmed that the grade of the performance evaluation is rated as 'Excellent (A)'.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• The Korean Journal of Pain
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• 제34권4호
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• pp.447-453
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• 2021

Background: Provocative discography (PD) is a test that is useful in diagnosing discogenic pain (DP). In this study, to diagnose DP, we used a posterolateral approach of needle placement and followed pressure criteria laid down by the Spine Intervention Society. The aim was to identify the correlation between magnetic resonance imaging (MRI) findings (desiccation, high intensity zone and change in shape and size of the disc) and the results of PD. Methods: Records of 50 patients who underwent PD for DP were analyzed. A total of 109 PDs were performed, with 54 suspect and 55 control discs. Alternate pain generators were ruled out. Results: A total of 35 suspect discs were positive on PD. The mean disc pressure in the suspect disc was 31.9 ± 7.9 psi (range, 15-44). Of the 50 patients who underwent PD, 35 had positive MRI findings. A significant positive correlation was found only between disc desiccation and discography result (r = 0.6, P < 0.001). Logistic regression analysis revealed that only desiccation successfully predicted the result of discography (OR = 26.5, P < 0.001); a high intensity zone and a disc protrusion/extrusion had an OR 2.3 and 1.24, respectively. Disc desiccation of Pfirmann grade 3 or more had a sensitivity and specificity of 0.93 and 0.64 respectively in identifying painful discs; the positive likelihood ratio was 2.58 while the negative likelihood ratio was 0.11. Conclusions: In patients with DP, disc desiccation is the most useful MRI feature that predicts a painful disc on PD.

• Journal of the Korean Institute of Intelligent Systems
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• 제23권4호
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• pp.317-324
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• 2013

Twitter sentiment analysis is to classify a tweet (message) into positive and negative sentiment class. This study deals with SentiWordNet(SWN)-based twitter sentiment analysis. SWN is a sentiment dictionary in which each sense of an English word has a positive and negative sentimental strength. There has been a variety of SWN-based sentiment feature extraction methods which typically first determine the sentiment orientation (SO) of a term in a document and then decide SO of the document from such terms' SO values. For example, for SO of a term, some calculated the maximum or average of sentiment scores of its senses, and others computed the average of the difference of positive and negative sentiment scores. For SO of a document, many researchers employ the maximum or average of terms' SO values. In addition, the above procedure may be applied to the whole set (adjective, adverb, noun, and verb) of parts-of-speech or its subset. This work provides a comparative study on SWN-based sentiment feature extraction schemes with performance evaluation on a well-known twitter dataset.

• Management & Information Systems Review
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• 제37권1호
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• pp.75-101
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• 2018

Social enterprise in Korea has faced with many problems such as small size, management capability, lack of technology and weak ability to obtain resources despite its quantitative growth, raising concern over sustainability of social enterprises. Despite such tough environment, unique feature of social enterprise, differentiated from commercial enterprise is that it has clear social mission. In addition, social enterprise has the organizational feature in that vulnerable social group of workers coexists with ordinary workers, and plays a role of helping independence of vulnerable social group. Due to this feature, successful organizational socialization of members in social enterprise is a very important feature. Based on assumption that social mission of social enterprise can be utilized as the unique competitiveness of social enterprise through vision-sharing in the organization, and may give positive effects on successful organizational socialization of organization members, this study aims to conduct empirical research on relationship between vision-sharing and organizational socialization and to explore mediation effects of organizational health as organizational environmental element in relationship between vision sharing and organizational socialization. This study was conducted on 156 employees working at social enterprises. As a result of study, first, vision sharing is found to have positive effects on organizational socialization at social enterprises. Second, vision sharing in social enterprise has positive effects on organizational health. Third, vitality and community-oriented in social enterprise are found to have mediation effects among lower elements of organizational health in relationship between vision sharing and organizational socialization. In conclusion, it is confirmed that the more visions of organization are shared, the more members recognize their organization healthy, resulting in successful organizational socialization. This study is meaningful in that it presents the plans for successful organizational socialization of members of social enterprise including vulnerable groups and that it is the empirical study on plans of social enterprise on human resource management.

• The Journal of Sustainable Design and Educational Environment Research
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• 제12권3호
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• pp.58-67
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• 2013

This study is to analyze the satisfaction and the image evaluation of landscape elements in outdoor space by types of the university campus. The results are as follows. 1) Out of outdoor elements at university campus, planting area, resting area, access road, and water feature are recognized as major landscape elements. Among them, planting area and access roads are evaluated low in terms of satisfaction levels, therefore, improvement on these elements are required. 2) In outdoor space image evaluation, university campus has image such as 'simple', 'clear', and 'safe'. By scale of universities, both 'A' university, which is the biggest in terms of size of campus, and 'B' university, which has a medium sized campus, have a positive image. However, 'C' university, which is the smallest in terms of size of campus, has a passive and negative image. 3) 6 factors are extracted through Factor Analysis for image evaluation. All of the universities show positive image in the categories of 'clarity' and 'familiarity', however, 'B' university and 'C' university show negative image in the category of 'scale'. 4) In Correlation Analysis between landscape elements satisfaction level and image evaluation, it is showed that the group of landscape facility becomes a relation factor of overall image evaluation. As a result, the higher satisfaction level goes, the better image evaluation of overall outdoor space at university campus is.