• International journal of advanced smart convergence
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• v.8 no.2
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.51-66
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• 2017

This study was conducted to develop detailed evaluation indicator as a way for evaluating sustainable urban ecosystem health focused on ecological urban elements from an eco-cultural point of view after the appearance of Eco-cultural City to meet various needs. And this study was also conducted to search for ways to utilize the detailed evaluation indicator like institutionalization. Eco-cultural City was defined as a city where ecological environment and cultural environment coexist and was aimed to derive applicable planning indicators in Korea. For this, FGI was executed, planning indicators were derived, and suitability was examined. The weights were calculated based on the selected indicators through AHP expert survey. After getting the result of FGI, experts reviewed the adequacy of definition from Eco-cultural City and its necessity, and the applicability of planning indicators was examined with evaluation of suitability. As a result of evaluating suitability, it was judged that 41 indicators based on an overall average of 4 areas were relatively high on suitability and also important among sectors. As for the analysis result, the priority order in multistage classification was as followed : harmony between human and environment(B) 0.349, environmental resources(A) 0.266, city environment and quality of culture(C) 0.208, and role division and citizen participation(D) 0.177. In the second level of relative importance, environment protection and infra in the role and citizen participation section was the highest, 0.449, harmonization policy and system in calculating weights was the highest.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.495-502
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• 2020

The 37 indicators for performance evaluation of public sewage management agencies are divided into four major categories (agency manpower management ability, wastewater treatment plant operation and management, sludge and water reuse, service quality) in the first stage, and the necessity and score acquisition for the detailed indicators by each major category in the second stages. Priority was investigated through the Analytic Hierarchy Process (AHP) analysis technique for ease and relevance of company efforts. Also, based on the results of this analysis, integrated type weighting and relative importance were analyzed. As a result of the analysis, the weight and relative importance of the first stage classification were in the order of wastewater treatment plant operation and maintenance, operation agency manpower management ability, sludge and water reuse, and service quality. As a result of analyzing the weights and priorities of the detailed performance indicators in the second stage, it was found that operator's career years, the percentage of certification holding rate in operators, compliance with the effluent water quality standards, training times for operators, and efforts to manage hazardous chemicals were important. Some of the indicators of operation agency performance evaluation may include indicators in which the performance of the company's efforts is underestimated or overestimated. In order to improve this, it is necessary to give weights in consideration of the necessity of the indicator, the relevance of the company's efforts, and the ease of obtaining scores.

• Journal of Wetlands Research
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• v.7 no.2
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• pp.133-144
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• 2005

The study attempts to try a classification of tidal flats types by selecting indicators and go forward to suggest a management plan by tidal flats types. With several indicators selected and PSR(Pressure-State-Response) framework, the relationship between environmental changes and socioeconomic activities in tidal flats was investigated. Tidal flats types were consequently classified into three groups: Wetland Protection Area, Wetland Rehabilitation Area, and Wetland Use-coordination Area. Accordingly, 69 tidal flats were assigned into each groups by PSR analysis: 34 Wetlands Protection Areas, 26 Wetland Rehabilitation Areas, and 9 Wetland Use-coordination Areas. So the baseic management plan of tidal flats must be different by tidal flats and characteristics of region but basically it must give top priority to the sustainable use in the long term.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.32-40
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• 2014

While there is a great demand for malware classification to reduce the time required in malware analysis and find a new type of malware, various similarity measurement methods of malware to classify a lot of malwares have been proposed. But, the existing methods to measure similarity just represented the classification results by them and have not carried out performance comparison with other methods. This is because an evaluation model to compare the performance of similarity measurement methods is non-existent. In this paper, we propose a new performance evaluation model on similarity measurement methods of malware by using two indicators: success rate and degree of confidence. In addition, we compare and evaluate the performance of existing similarity measurement methods by using these two indicators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.8 no.3
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• pp.53-66
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• 2005

The objective of this study is the classification of priority areas for the implementation of green roof by evaluating environmental deterioration in Seoul. Non-permeable pavement, air pollution, habitual floods, energy use, heat island and green space are considered in this assessment indicators. The expert questionnaire survey was conducted in order to determine the most important indicators. These indicators were then, thoroughly evaluated. As a result, high priority areas for the implementation of green roof were deduced in the following order of the districts : Jung, Sungdong, Jungrang, Youngdungpo, Jongro and Kangnam. The highest priority areas were determined to be crowded business-commercial areas. Low priority areas are analyzed in the following order of the districts : Kwanak, Nowon, Seocho and Dobong. The result of this study can be utilized for environmental planning and decision of related policies. Additionally, it can be promoted that awareness of implementing green roof of citizens, policy makers and building owners and effect of green networking between inside and outside Seoul can be increased.

• The Korean Journal of Health Service Management
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• v.13 no.4
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• pp.121-132
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• 2019

Objectives: This study analyzed the prescription antibiotics characteristics of Acute respiratory infection outpatients. It provides a basis for establishing the correct evaluation project on appropriate prescribing indicators. Methods: The research data were collected from the National Health Insurance Corporation's 2014 sample cohort for Internal Medicine, Pediatrics, Otorhinolaryngology, Family Medicine and General practitioner clinics classification of diseases codes J00-J06, J20-J22, J40 outpatients. Results: The antibiotic prescription rate on the evaluation project for appropriate prescribing indicators of Health Insurance Review & Assessment Service was 43.54%, whereas in this study it was about 10% higher because the analysis targeted the entire acute respiratory infection diagnosis. Conclusions: There is a need to identify the correct antibiotic prescription by expanding the current assessment standard. Such standard must include acute lower respiratory infections and minor diagnosis because current evaluation projects on appropriate prescribing indicators targets only the major diagnosis of acute upper respiratory infection.

• Journal of Sasang Constitutional Medicine
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• v.33 no.3
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• pp.95-126
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• 2021

Objectives The purpose of this study was to develop diagnostic tools for Sasang Constitutional patterns based on the middle classification of the Sasang Constitutional patterns. Methods Diagnosis and assessment indicators of Soeumin, Soyangin, Taeeumin, and Taeyangin patterns were extracted based on the 『Clinical Practice Guideline for Sasang Constitutional Medicine Symptomatology』 and 『Donguisusebowon』. An online survey was conducted on the 'importance of diagnosis and assessment indicators' by the Sasang Constitutional Medicine expert group. Results Based on the expert consultation results, the importance weight for each diagnosis and assessment indicators symptom was calculated, and the importance was ranked to develop diagnostic tools for Soeumin, Soyangin, Taeeumin, and Taeyangin patterns. Diagnostic tool consisted of 58 questions for Soeumin patterns, 68 questions for Soyangin patterns, 81 questions for Taeeumin patterns, and 42 questions for Taeyangin patterns. The final total score was calculated by reflecting each response score and the weight of each question. Conclusions The developed 'Diagnostic Tools for Sasang Constitutional patterns' can be used to make an effective and objective diagnosis in the clinical site. In the future, if the reliability and validity of these diagnostic tools are tested through clinical study, it will be possible to improve clinical applicability and contribute to standardization of diagnosis.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.