• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.372-377
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• 2008

Multiclass cancer classification has been actively investigated based on gene expression profiles, where it determines the type of cancer by analyzing the large amount of gene expression data collected by the DNA microarray technology. Since gene expression data include many genes not related to a target cancer, it is required to select informative genes in order to obtain highly accurate classification. Conventional rank-based gene selection methods often use ideal marker genes basically devised for binary classification, so it is difficult to directly apply them to multiclass classification. In this paper, we propose a novel method for multiclass gene selection, which does not use ideal marker genes but directly analyzes the distribution of gene expression. It measures the class-discriminability by discretizing gene expression levels into several regions and analyzing the frequency of training samples for each region, and then classifies samples by using the naive Bayes classifier. We have demonstrated the usefulness of the proposed method for various representative benchmark datasets of multiclass cancer classification.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.701-712
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• 2013

The e-learning industry status survey statistic provides information about the actual conditions of supply and demand of the e-learning industries. NIPA (National IT Industry Promotion Agency) has published the annual report of the survey results since 2004. Due to the 9th version of the KSIC (Korean standard industrial classification) revised in 2008, a refinement of the sampling design for the survey becomes necessary, especially that for the business demand sector. This article, based on the 9th revision of the KSIC, constructs a stratification of the target population used for the e-learning industry status survey on the business demand sector. Classification of strata in the business population is based on the industrial type and employment scale of business. Under the stratified population, we design a sampling scheme by using the power allocation method that enables us to satisfy a target coefficient of variation of each industrial stratum. In order to secure an accurate survey results based on the proposed sampling design, we consider the problem of calculating the design weights, derivation of parameter estimators, and formulas of their standard errors.

• Cross-Cultural Studies
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• v.22
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• pp.223-277
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• 2011

Tan Yuanjie(譚元杰) of CHINA COSTUME ART OF PEKING OPERA("中國京劇服裝圖譜") is 'Foreword' attention from the bar 'Formalism'. A note is makeup system from ever performances here, 'what kind of adaptation must be a corresponding type of costume should be worn.' This stance to 'type of person's identity and faced the scene correlated' with the actual performance tradition plays out is going and, while here the rules to capture the opera's on the character of 'identity and the circumstances under clothing' is defined. This position discussed previously 'Formalism' in line with the will he perform, and looks to meet the elements of production. This basic stance is clean up, while 'Old Beijing Opera costumes costumes taxonomy largely' literary costume' and 'militant outfit' into two groups divided over throughout steamroll surgery, because surely need to have a more systematic classification. The classification system was established as 'Part 1. Mang, Part 2. Pi, Part 3. Xi, Part 4. Kao, Part 5. YI'. In addition to these classification systems, as well as the aforementioned 'object theory' Given the symbolic significance of the capacity to keep in mind is necessary. Costumes conduct, character, situation, atmosphere and so the transport of charged symbols here, a target symbol of the system is the projection of water. This costume is detrimental to the mall for the positionsay, but I kept in mind damwongeolyi internationalization of Chinese culture. when you see the view from the perspective of semiotic systems for the sign, that the theater is necessary to complement. In this paper, 'Yi(衣)' costume on the corresponding point of the target compared to the China Culture Department of Theatre and Folklore methodology ran off and sprinting was to lay the groundwork for research.

• The Journal of Information Systems
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• v.28 no.3
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.461-470
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• 2019

In diabetes mellitus, renal disease is a common complication, characterized by increased urinary albumin excretion and reduced eGFR. According to KDIGO CKD stage classification, Korean characteristics were analyzed according to urinary albumin and eGFR using the National Health and Nutrition Examination Survey VI raw data. According to KDIGO classification, diabetic patients were classified as Low risk 72.0%, Moderate risk 19.3%, High risk 5.6% and Very high risk 3.0%. Low risk decreased from 74.7% to 52.2%, and moderate to very high risk increased from 25.4% to 47.8% as the duration of diabetes mellitus was prolonged. The risk factors were CKD stage 1 (HR 2.064) to stage 4 (HR 11.049), the highest risk of hypertension. The incidence of renal disease was elevated according to duration of hypertension and HR 0.42 of kidney disease was decreased in the group maintaining proper blood pressure. In the hypertensive patients, the group administered with target blood pressure had a reduction of the kidney disease by 42% than the group with the hypertension. Therefore, controlling and managing hypertension to target blood pressure is important for the prevention of kidney disease.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.27-34
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• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

• Korean Security Journal
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• no.6
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• pp.327-340
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• 2003

As a countermeasure under accidental occurrence situation, First, as application form of martial arts, A security guard foster ability that can protect a target person of guard and own body under accidental occurrence situation through incessant martial arts practice. To achieve this purpose, incessant training mental power and physical strength reinforcement should be kept on to prevent, therefore make a safety control function for such as weapon, small arms, explosive, vehicles terror etc. happened under accidental occurrence situation. Second, according to the contents of training based on the classification category of martial arts for security guard under accidental situation, a security guard must keep safety distance necessarily lest a target person of gurad should be attacked by attacker, therefore, intercept an attack opportunity if a safety distance between a target person of guard and attacker is not kept. Third, It is to practice confrontation techniques based on the type of attack. A security guard must develp situation disposal ability that can cope properly with the attack using empty hands, murderous weapon, small arms, explosive by case or individual or mass of threat that impose danger and injury in a target person's body of guard.

• Journal of the Korean Society of Safety
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• v.25 no.4
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• pp.61-67
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• 2010

Though "Zero Accident Campaign" is a desirable campaign for industrial accident prevention and reducing victims, the number of industrial enterprises has been decreasing abruptly in recent years. One of the reasons for this phenomenon may be attributed to irrationality of 'target accident-free time periods' established by related organizations. This study was carried out to develop a new rational scheme for the campaign. Therefore, for a numerical basis, Poisson process was introduced, and problems induced by current target periods were analyzed mathematically one by one. As a result, it was verified that current target periods were uneven since the probability that manufacturing plants get them would be different form industry to industry. To develop countermeasures, a brand new method were suggested in this research. The first characteristic was that group classification should be based upon average accident rates resulted from past several years, and the second was that adjustment probability which can make the target acquisition probability even. About the suggested method, a questionnaire survey was conducted. To make a conclusion, most manufacturing plants agreed with the suggested method such high affirmative portion that the suggested method would be expected to help promote the campaign again.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.590-597
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• 2011

The IDRS provides detection, classification and bearing/range estimation by performing wavefront curvature analysis on an intercepted active transmission from target. Especially, a estimate of the target bearing/range that significantly affects the optimal operation of own submarine is required. Target bearing/range can be estimated by wavefront curvature ranging which use the difference of time arrival at sensors. But estimation ambiguity occur in bearing/range estimation due to a number of peaks caused by high center frequency and limited bandwidth of the intercepted active transmission and distortion caused by noise. As a result the bearing/range estimation performance is degraded. To estimate target bearing/range correctly, bearing/range estimation method that eliminate estimation ambiguity is required. In this paper, therefore, for wavefront curvature ranging, NLS cost function with curve fitting method is proposed, which provide robust bearing/range estimation performance by eliminating estimation ambiguity. Through simulation the performance of the proposed bearing/range estimation methods are verified.