• 산업경영시스템학회지
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• 제43권4호
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• 센서학회지
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• 제33권3호
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• 디지털융복합연구
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• 제16권4호
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• pp.1-10
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• 2018

우리나라는 매년 리콜 상품의 수는 꾸준히 증가하고 있으나 소비자의 리콜 참여율 자체는 매우 저조하다. 본 연구는 소비자의 적극적인 리콜 참여를 위해 필요한 요소를 리콜 역량으로 보아 리콜 역량의 구성 요소 파악, 리콜 경험에 영향을 미치는 리콜 역량 요소를 규명하고자 하였다. 이를 위하여 우리나라 성인 소비자 1,626명을 대상으로 리콜 경험 및 리콜 역량을 조사하였으며 그 결과 리콜 역량의 하위 요인으로 리콜 참여 의지, 리콜 관련 기술, 리콜 제도 인식, 주관적 지식, 객관적 지식의 5가지 요인을 도출하였다. 조사 대상자 중 리콜 경험, 비경험자로 구분하여 각각의 리콜 역량을 비교한 결과 5개 하위 영역 모두에서 통계적으로 유의한 차이가 나타났고, 실천적 역량으로서 리콜 관련 기술 요인과 주관적 지식 역량이 리콜 경험에 가장 큰 영향을 나타내는 요인으로 나타났다. 리콜 제도의 실효성 제고를 위해서는 정책 대상자인 소비자의 특성을 정확히 파악하는 것이 중요하며, 소비자의 리콜 역량의 강화를 통한 리콜 참여율 제고를 위해 리콜 정보 개선하고 정보탐색 접근도를 높이는 것이 중요함을 강조하였다는 것에 의의가 있다. 소비자 지향적인 리콜 제도로 개선하고 소비자의 리콜 관심도 증진을 위한 환경 조성에 정부, 학계, 단체 등의 유기적인 협력이 이루어지는 것이 요구된다.

• 방사선산업학회지
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• 제18권3호
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• pp.177-182
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• 2024

The purpose of this study is to develop a CNN based deep learning model that can effectively detect pneumonia by analyzing chest X-ray images of adults over the age of 20 and compare it with VUNO, LUNIT a commercialized AI model. The data of chest X-ray image was evaluate based on accuracy, precision, recall, F1 score, and AUC score. The CNN model recored an accuracy of 82%, precision 76%, recall 99%, F1 score 86%, and AUC score 0.7937. The VUNO model recordded an accuracy of 84%, precision 81%, recall 94%, F1 score 87%, and AUC score 0.8233. The LUNIT model recorded an accuracy of 77%, precision 72%, recall 96%, F1 score 83%, and AUC score 0.7436. As a result of the Confusion Matrix analysis, the CNN model showe FN (3), showing the highest recall rate (99%) in the diagnosis of pneumonia. The VUNO model showed excellent overall perfomance with high accuracy (84%) and AUC score (0.8233), and the LUNIT model showed high recall rate (96%) but the accuracy and precision showed relatively low results. This study will be able to provide basic data useful for the development of a pneumonia diagnosis system by comprehensively considers the perfomance of the medel is necessary to effectively discriminate between penumonia and normal groups.

• 인지과학
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• 제2권1호
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• pp.51-72
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• 1990

본연구에서는 덩이글 처리에 대형구조가 미치는 영향을 검증코자하였다. 선행연구들에서 주장되는 인과관계, 이야기 문법범주, 위계구조는 모두 대형구조 속성 내에서 상호작용하는 것으로 보았다. 위계구조와 계열구조, 비연결구조 이야기 구조들에 대한 읽기시간과 문장적합도 판단, 자유회상 과제 를 통해 대형구조 형성이 이해과정과 기억에 중요한 변인임을 밝히었다.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• Nutritional Sciences
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• 제2권2호
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• pp.113-118
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• 1999

Critical evaluation of energy intake data from dietary studies is difficult but important. To investigate the underreporting of total energy intake, we analyzed the one-day dietary intake data collected by 24-hour recall method from 550 elderly Koreans aged 60 years or older. Underreporting was addressed by computing the ratio of energy intake (EI) to estimated basal metabolic rate (BMRest). EI : BMRest ratio was found to be 1.38 for, men and 1.33 for women, with about 14% of men and women classified as underreporters. Underreporting of energy intake was highest in men and women who were overweight, had lower family income, or no school education. For men, the most significant variables to predict the ratio of energy intake to estimated basal metabolic. rate (EI : BMRest) were weight status, members of household, alcohol consumption and age, while income and education level were most significant for women.

• 대한의용생체공학회:의공학회지
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• 제43권1호
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• pp.45-51
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• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• Asian Pacific Journal of Cancer Prevention
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• 제15권13호
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• pp.5143-5147
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• 2014

Cervical cancer screening in Malaysia is by opportunistic Pap smear which contributes to the low uptake rate. To overcome this, a pilot project called the SIPPS program (translated as information system of Pap smear program) had been introduced whereby women aged 20-65 years old are invited for Pap smear and receive recall to repeat the test. This study aimed at determining which recall method is most cost-effective in getting women to repeat Pap smear. A randomised control trial was conducted where one thousand women were recalled for repeat smear either by registered letter, phone messages, phone call or the usual postal letter. The total cost applied for cost-effectiveness analysis includes the cost of sending letter for first invitation, cost of the recall method and cost of two Pap smears. Cost-effective analysis (CEA) of Pap smear uptake by each recall method was then performed. The uptake of Pap smear by postal letter, registered letters, SMS and phone calls were 18.8%, 20.0%, 21.6% and 34.4%, respectively (p<0.05). The CER for the recall method was lowest by phone call compared to other interventions; RM 69.18 (SD RM 0.14) compared to RM 106.53 (SD RM 0.13), RM 134.02 (SD RM 0.15) and RM 136.38 (SD RM 0.11) for SMS, registered letter and letter, respectively. ICER showed that it is most cost saving if the usual method of recall by postal letter be changed to recall by phone call. The possibility of letter as a recall for repeat Pap smear to reach the women is higher compared to sending SMS or making phone call. However, getting women to do repeat Pap smear is better with phone call which allows direct communication. Despite the high cost of the phone call as a recall method for repeat Pap smear, it is the most cost-effective method compared to others.

• 한국인공지능학회지
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• 제12권2호
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• pp.1-7
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• 2024

In this paper, we compare three models (logistic regression, Random Forest, and XGBoost) for predicting stroke occurrence using data from the Korea National Health and Nutrition Examination Survey (KNHANES). We evaluated these models using various metrics, focusing mainly on recall and F1 score to assess their performance. Initially, the logistic regression model showed a satisfactory recall score among the three models; however, it was excluded from further consideration because it did not meet the F1 score threshold, which was set at a minimum of 0.5. The F1 score is crucial as it considers both precision and recall, providing a balanced measure of a model's accuracy. Among the models that met the criteria, XGBoost showed the highest recall rate and showed excellent performance in stroke prediction. In particular, XGBoost shows strong performance not only in recall, but also in F1 score and AUC, so it should be considered the optimal algorithm for predicting stroke occurrence. This study determines that the performance of XGBoost is optimal in the field of stroke prediction.