• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.1518-1523
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• 2008

Train collision and derailment are types of accident with low probability of occurrence, but they could lead to disastrous consequences including loss of lives and properties. The development of the risk assessment model has been called upon to predict and assess the risk for a long time. Nevertheless, the risk assessment model is recently introduced to the railway system in Korea. The classification of the hazardous events and causes is the commencement of the risk assessment model. In previous researches related to the classification, the hazardous events and causes were classified by centering the results. That classification was simple, but might not show the root cause of the hazardous events. This study has classified the train collision and derailment based on the relevant hazardous event including faults of the train related the accidents, and investigates the causes related to the hazardous events. For the risk assessment model, FTA (fault tree analysis) and ETA (event tree analysis) methods are introduced to assess the risk.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6018-6033
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• 2018

Fires are a common cause of catastrophic personal injuries and devastating property damage. Every year, many fires occur and threaten human lives and property around the world. Providing early important sign for early fire detection, and therefore the detection of smoke is always the first step in fire-alarm systems. In this paper we propose an automatic smoke detection system built on camera surveillance and image processing technologies. The key features used in our algorithm are to detect and track smoke as moving objects and distinguish smoke from non-smoke objects using a convolutional neural network (CNN) model for cascade classification. The results of our experiment, in comparison with those of some earlier studies, show that the proposed algorithm is very effective not only in detecting smoke, but also in reducing false positives.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.101-106
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• 2021

Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Journal of Digestive Cancer Research
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• 제5권1호
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• pp.5-15
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• 2017

Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related death in the world. The aggressive but not always predictable pattern of HCC causes the limited treatment option and poorer outcome. Many researches had already proven the heterogeneity of HCC is one of the major challenges for treatment option and prognosis prediction. Molecular subtyping of HCC and selection of patient based on molecular profile can provide the optimization in the treatment and prognosis prediction. In this review, we have tried to summarize the molecular classification of HCC proposed by different valuable researches presented in the logistic way.

• 반도체디스플레이기술학회지
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• 제23권1호
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• pp.61-64
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• 2024

As research on autonomous driving technology becomes more active, various studies on signal recognition of traffic lights are also being conducted. When recognizing traffic lights with different purposes and shapes, such as pedestrian traffic lights, vehicle-only traffic lights, and right-turn traffic lights, existing classification methods may cause misrecognition problems. Therefore, in this study, we studied a model that allows accurate signal recognition by subdividing the classification of signals according to the purpose and type of traffic lights. A signal recognition model was created by classifying traffic lights according to their shape and purpose into horizontal, vertical, right turn, etc., and by comparing them with the existing signal recognition model based on YOLOv5, it was confirmed that more correct and accurate recognition was possible.

• 한국콘텐츠학회논문지
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• 제7권11호
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• pp.309-319
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• 2007

자연재해에 대한 올바른 인식은 발생한 재해에 대한 원인을 규명하여 방지하거나 저감하는 대책을 수립함으로써 인명 및 재산피해를 줄일 수 있다. 이를 위해서는 우선 명확한 자연재해의 범주를 정해야 하고 분류체계를 정비해야 한다. 또한 재해발생 시 신속한 현장조사와 함께 데이터가 구축되어 전문가 차원에서의 원인규명이 되어야 한다. 우리나라의 자연재해 분류체계는 자연재해대책법상에 정해져 있다. 그러나 이 분류는 재해의 관리적 차원에서 분류한 것이므로 기술정보의 구축을 위해 전문가적인 입장에서의 재조명이 필요하다고 본다. 따라서 선진 각국들의 분류사례를 수집분석하고 관련분야의 전문가 의견이 고려된 한국형 분류체계가 필요하다. 자연재해관련 정보의 체계적인 DB가 구축된다면 인터넷 가상공간에서 다양한 정보서비스가 가능하며 자연재해로 인한 방재대책을 수립하는데 큰 도움이 될 것으로 확신한다. 본 연구에서는 국내외 자연재해의 분류체계를 수집 분석하여 전문기술분야에 맞는 한국형 자연재해분류체계안과 온톨로지를 제시하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제14권5호
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• pp.3219-3221
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• 2013

Background: This study used receiver operating characteristic (ROC) curves to screen Surveillance, Epidemiology and End Results (SEER) skin melanoma data to identify and quantify the effects of socioeconomic factors on cause specific survival. Methods: 'SEER cause-specific death classification' used as the outcome variable. The area under the ROC curve was to select best pretreatment predictors for further multivariate analysis with socioeconomic factors. Race and other socioeconomic factors including rural-urban residence, county level % college graduate and county level family income were used as predictors. Univariate and multivariate analyses were performed to identify and quantify the independent socioeconomic predictors. Results: This study included 49,999 parients. The mean follow up time (SD) was 59.4 (17.1) months. SEER staging (ROC area of 0.08) was the most predictive foctor. Race, lower county family income, rural residence, and lower county education attainment were significant univariates, but rural residence was not significant under multivariate analysis. Living in poor neighborhoods was associated with a 2-4% disadvantage in actuarial cause specific survival. Conclusions: Racial and socioeconomic factors have a significant impact on the survival of melanoma patients. This generates the hypothesis that ensuring access to cancer care may eliminate these outcome disparities.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2007년도 춘계학술대회
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• pp.113-123
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• 2007

Through so that accident of semiconductor industry deduces unsafe factor of the person center on unsafe behaviour that incident history and questionnaire and I made starting point that extract very important factor. It served as a momentum that make up base that analyzes factors that happen based on factor that extract factor cause classification for the first factor, the second factor and the third factor and presents model of human error. Factor for whole defines factor component for human factor and to cause analysis 1 stage in human factor and step that wish to do access of problem and it do analysis cause of data of 1 step. Also, see significant difference that analyzes interrelation between leading persons about human mistake in semiconductor industry and connect interrelation of mistake by this. Continuously, dictionary road map to human error theoretical background to basis traditional accidental cause model and modern accident cause model and leading persons. I wish to present model and new model in semiconductor industry by backbone that leading persons of existing scholars who present model of existent human error deduce relation. Finally, I wish to deduce backbone of model of pre-suppression about accident leading person of the person center.

• 대한한방소아과학회지
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• 제16권2호
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• pp.111-128
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• 2002

Objectives : The aim of this study was to investigate the classification methods of the cause of Allergic Rhinitis for Children. Methods : We surveyed the oriental & western medical book concerning the Allergic Rhinitis for Children. Results : 1. The Oriental medicine, Allergic Rhinitis is belong to the BiGu, BunChe and the symptoms are watery rhinorrhea, sneezing and nasal obstruction. 2. The cause of disease is the weak of lung, spleen and kidney, and invasion in to nasal cavity of Poong Han etc a wrong air. 3. In children, the cause of disease is the weak of lung and spleen. and the aim of the treatment is helping the vital energy and expelling the vice.