• Journal of The Korean Association For Science Education
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• v.32 no.6
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• pp.1063-1071
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• 2012

The purpose of this study is to investigate the relation between the classification ability quotient and cortisol-hormonal change of middle school students. Thirty-three students, second graders in middle school, performed the classification task that can be an indicator of students' classification ability. And then amount of the secreted hormone was analyzed during task performance. The study results were as follows: First, the classification methods of students mostly utilized visual, qualitative. Their classification patterns for each subject were static, partial, and non-comparative. Second, the amount of stress-hormone was secreted from students during the experiment decreased in overall after the free classification. It seemed that student-centered activity relieved stress. Third, the classification ability quotient turned out to be significantly correlated to the stress hormone, which means that there was a close relationship between classification ability and stress level. It was also considered that stress had a positive effect on the improvement of classification ability. This study provided physiologically more accurate information on the stress increased in the learning process than other conventional studies based on reports or interviews. Finally, researchers could recognize the effect of stress in the cognitive activity and the need to find an appropriate level of stress in learning processes.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.613-614
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• 2017

Recently, research on the automation of home IoT has been carried out in which IoT (Internet of Things) is applied inside the home. However, the conventional IoT automation system has a problem that the operation of the device is limited only by the threshold value of the sensor, so that the device may collide and interfere with each other and the efficiency of the Task is low due to the malfunction of the device. In this paper, we propose a Situation-specific task classification algorithm to solve these problems. Using the sensor threshold and the current date as classification values in the decision tree, the task according to the internal situation of the home is classified and the corresponding device is selected and proceeded. Therefore, it is expected that the users will be provided with a service that changes flexibly according to changes in the internal situation of the home, and the accuracy of the operation will be increased by reducing the malfunction of the device and the collision between the devices.

• Korean Institute of Interior Design Journal
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• v.22 no.1
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• pp.211-221
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• 2013

As the result of investigating domestic interior design (interior architecture) field-related laws, it was found that interior design (interior architecture) is not recognized as in independent area due to weak classification standards by Korean standard industrial classification and job classification. Korean standard industrial classification is applied as a standard setting limits to applicable targets and industrial fields for laws related to general administration and industrial policy other than various statistic purposes. Also, the standard industrial classifications regarding the industry field determines the laws or applicable tax rates, government support and such according to the classification, and thus is very important. Moreover, interior architecture field is largely different from general architecture due to specialization and distinct characteristics, but due to the comprehensive concept of architecture industry regulations, it is considered the proper assessment for the professionalism is not conducted. Also, interior architecture field has irrational contradictions that is not independent with a clear definition and industry field classification not only in legal system and trade customs. Therefore, The following is proposed as the plan to strengthen the domestic/international competitiveness and system improvements for interior architecture. (1)interior design (interior architecture) must be amended as an industrial classification that can coexist with architecture. (2)interior design (interior architecture) must be amended as a job classification that can coexist with architecture. (3)Among the design tasks of an architect, approval for the design task field of interior architecture field must be legislated. -In architect design standard contract (the existing architecture design task scope and quality standard table) of a structure, among the tasks by request of the owner, (1)interior design tasks shall be legislated. It should be legislated so that interior design (interior architecture) majors can be included as well. (4)The task field of interior design that coexists with design must be amended. (5)National contract law - among contract method by negotiation, specialty item must be vitalized.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• Journal of Korean Society of Archives and Records Management
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• v.13 no.2
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• pp.179-199
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• 2013

This study is analyzed by examining the records management systems that are built to use comfortably and manage the records of universities efficiently. This paper analyzed the current status of the records in Chonnam National University (CNU), unit task, function, and organization. As a result, CNU has 127 sections that treat 1,625 unit tasks. The unit task involved in education accounts for 85.91%. The problem is that unit tasks are not organized unequally. It can seem to include some problems in classification and analysis of work when unit tasks are even, more or less, in a particular area. Functional classification in universities are also unequally organized. In universities, functional classification in the field of administration is detailed, while functional classification in the field of research is small in quantity. A more efficient records management will be done if work and function about organization are formed appropriately.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.165-173
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• 2020

The emergence of unmanned agricultural machinery has brought new research content to the development of precision agriculture. In order to speed up the research on key technologies of unmanned agricultural machinery, classification of intelligence level of unmanned agricultural machinery has become a primary task. In this study, the researchers take the complex interactive system consisting of unmanned grain harvester, task and driving environment as the research object, and carry out a research on the grading and classification of intelligent level of unmanned grain harvester. The researchers of this study also establish an evaluation model of unmanned grain harvester vehicle, which consists of human intervention degree, environmental complexity, and task complexity. Besides, the grading and classification of intelligence level of the unmanned grain harvester is carried out according to the human intervention degree, environmental complexity and the task complexity of the unmanned grain harvester. It provides a direction for the future development of unmanned agricultural machinery.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.29-30
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• 2022

Curriculum learning은 딥러닝의 성능을 향상시키기 위해 사람의 학습 과정과 유사하게 일종의 'curriculum'을 도입해 모델을 학습시키는 방법이다. 대부분의 연구는 학습 데이터 중 개별 샘플의 난이도를 기반으로 점진적으로 모델을 학습시키는 방안에 중점을 두고 있다. 그러나, coarse-to-fine 메카니즘은 데이터의 난이도보다 학습에 사용되는 class의 유사도가 더욱 중요하다고 주장하며, 여러 난이도의 auxiliary task를 차례로 학습하는 방법을 제안했다. 그러나, 이 방법은 혼동행렬 기반으로 class의 유사성을 판단해 auxiliary task를 생성함으로 다중 레이블 분류에는 적용하기 어렵다는 한계점이 있다. 따라서, 본 논문에서는 multi-label 환경에서 multi-class와 binary task를 생성하는 방법을 제안해 coarse-to-fine 메카니즘 적용을 위한 방안을 제시하고, 그 결과를 분석한다.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.29-38
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• 2009

This study investigated the interaction between consonants and vowels in Korean syllable perception using a speeded classification task (Garner, 1978). Experiment 1 examined whether listeners analytically perceive the component phonemes in CV monosyllables when classification is based on the component phonemes (a consonant or a vowel) and observed a significant redundancy gain and a Garner interference effect. These results imply that the perception of the component phonemes in a CV syllable is not linear. Experiment 2 examined the further relation between consonants and vowels at a subphonemic level comparing classification times based on glottal features (aspiration and lax), on place of articulation features (labial and coronal), and on vowel features (front and back). Across all feature classifications, there were significant but asymmetric interference effects. Glottal feature.based classification showed the least amount of interference effect, while vowel feature.based classification showed moderate interference, and place of articulation feature-based classification showed the most interference. These results show that glottal features are more independent to vowels, but place features are more dependent to vowels in syllable perception. To examine the three-way interaction among glottal, place of articulation, and vowel features, Experiment 3 featured a modified Garner task. The outcome of this experiment indicated that glottal consonant features are independent to both the place of articulation and vowel features, but the place of articulation features are dependent to glottal and vowel features. These results were interpreted to show that speech perception is not abstract and discrete, but nonlinear, and that the perception of features corresponds to the hierarchical organization of articulatory features which is suggested in nonlinear phonology (Clements, 1991; Browman and Goldstein, 1989).

• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.179-185
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• 2017

Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.