• Psychiatry investigation
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• v.15 no.11
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• pp.1037-1045
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• 2018

Objective The present study aimed to identify risk factors for future SI and to predict individual-level risk for future or persistent SI among college students. Methods Mental health check-up data collected over 3 years were retrospectively analyzed. Students were categorized as suicidal ideators and non-ideators at baseline. Logistic regression analyses were performed separately for each group, and the predicted probability for each student was calculated. Results Students likely to exhibit future SI had higher levels of mental health problems, including depression and anxiety, and significant risk factors for future SI included depression, current SI, social phobia, alcohol problems, being female, low self-esteem, and number of close relationships and concerns. Logistic regression models that included current suicide ideators revealed acceptable area under the curve (AUC) values (0.7-0.8) in both the receiver operating characteristic (ROC) and precision recall (PR) curves for predicting future SI. Predictive models with current suicide non-ideators revealed an acceptable level of AUCs only for ROC curves. Conclusion Several factors such as low self-esteem and a focus on short-term rather than long-term outcomes may enhance the prediction of future SI. Because a certain range of SI clearly necessitates clinical attention, further studies differentiating significant from other types of SI are necessary.

• Korean Journal of Community Nutrition
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• v.15 no.5
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• pp.614-624
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• 2010

The purpose of this study was to investigate the effect of 3-month nutrition education (First Time Intervention, FI) + additional 3-month nutrition education (Repeated Intervention, RI) which was performed after the 8-month followup. FI was conducted during 0-3 months and RI for 11-14 months. Ninety-two subjects completed FI program, and 38 out of 92 subjects who received FI finished the RI. Anthropometric data, dietary assessment (24hr recall) and fasting blood analysis were measured at 0 month, 3 months, 11 months and 14 months time points. After FI (3 mo), waist circumference, triglycerides, total cholesterol were significantly decreased. At 11 month follow-up, body weight, BMI, hip circumference, SBP, DBP were significantly rebounced and HDL cholesterol was significantly decreased. Therefore, the effect of short-term nutrition education was not being sustained. After the secondary nutrition intervention (14 mo), waist circumference and hip circumference were again significantly decreased. Total diet quality index-international (DQI-I) score was significantly increased in both FI group and RI group. The changes in DQI-I scores were significantly correlated with the changes in body weight (r = -0.129, p < 0.05) and counts of nutrition education (r = 0.159, p < 0.05), indicating that effective nutrition education helps improve the diet quality leading to a possible role in CVD prevention among male workers. Although a short-term intervention seems to be a success, the effect was not retained in this study. Therefore, we suggest incorporating nutrition education as a routine program for male worker at worksite.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Korean Journal of Cognitive Science
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• v.26 no.1
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• pp.27-51
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• 2015

The present study tested the effect of a top-down influence on recalling the colors of Stroop words. Participants remembered the colors of 1, 2, 3 or 6 Stroop words. After 1 second of a memory delay, they were asked to recall the color of a cued Stroop word by selecting out its corresponding color on a color-wheel stimulus. The correct recall was defined when the participants chose a color that was within ${\pm}45^{\circ}$ from the exact location of Stroop word's color on the color-wheel. Otherwise, the recall was defined as incorrect. The analyses of the frequency distribution of the participants' responses in the error trials showed that the probability of choosing the color-name of the target Stroop word was higher than the probability of other five color-names on the color-wheel. Further analyses showed that increasing the number of Stroop words to manipulate memory load did not affect the probability of the Stroop interference. These results indicate that the top-down interference by Stroop manipulation may induce systematic distortion of the stored representation in visual working memory.

• Korean Journal of Biological Psychiatry
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• v.15 no.3
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• pp.194-203
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• 2008

Objectives : This study was aimed at evaluating the relationship between somatic symptoms, depression, anxiety and cognitive function in the patients with Mild Traumatic Brain Injury(MTBI). Methods : Thirty seven patients with MTBI were selected from those patients who had visited the Department of Neuropsychiatry of Wonkwang University Hospital from 2003 to 2007. To assess and quantify the somatic symptoms, depression and anxiety, Personality Assessment Inventory(PAI) was used. Assessment of cognitive function was carried out by using Korean Wechsler Adult Intelligence Scale(K-WAIS), Rey-Kim Memory Test, and Kims Executive Function Test. The effects of somatic symptoms, depression, and anxiety on the cognitive function were evaluated by Pearson correlation test. Results : Somatic symptoms, depression, and anxiety, all showed inverse correlation to cognitive function. Specifically, 1) an increase in somatic symptoms was associated with a decrease in attention, verbal short term memory, verbal recall and recognition, and visual memory. 2) An increase in anxiety was associated with a decrease in verbal recall and recognition. 3) An increase in depression was associated with a decrease in cognitive function that requires high attention and verbal memory. Conclusion : The patients with MTBI displayed diverse symptoms ranging from cognitive impairment to somatic symptoms, depression, and anxiety. Somatic and emotional symptoms were correlated with cognitive function(especially executive function). Importantly, this study raises the possibility of treating the cognitive impairment associated with MTBI by treating somatic symptoms, depression, and anxiety.

• Journal of Nutrition and Health
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• v.23 no.4
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• pp.279-286
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• 1990

A nutrition education program has been designed for the children of obese or unbalanced dietary habits. A total of 120 children(60-obese and 60-unbalanced dietary habits) who were in 5th-6th grade was chosen for the program and the effectiveness of the nutrition education was evaluated. The nutrition education program was organized into daily lessons(3 wks). A booklet was made for the education and used during the education period, dealing with 5-Basic Food Groups and their functions, excess or deficiency in a human body, food exchange list, importance of health, obesity, unbalance dietary habits and of school lunch program were emphasized in every day session. dietary recall records were collected to assess their daily food intake and the amounts of meals were discussed. During the education period, booklets, wall charts, photographs, food models, VTR films and slide films were utilized. Assessment of effects of the nutrition education program was carried out by a nutrition knowledge test, food habit records, anthropometric measurements and food preference test. The nutrition knowledge scores were significantly improved after the education and the scores were higher on the children in the school with school lunch program. The anthropometric measurements and food preference test revealed no significant influences of the education on the children, showing that the education period was too short to change their eating behavior any may need a long-term education program. Food habit scores were improved after the education in both experimental and control groups. The experimental groups showed higher scores than the control group.

• Journal of Dental Rehabilitation and Applied Science
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• v.29 no.4
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• pp.384-390
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• 2013

Postinsertion problems tend to be minimized when a sequential insertion procedure is followed. However, problems may occur as the result of one or any combination of comfort, function, esthetics, and phonetic difficulties. Following the insertion of a partial denture, an appointment for review in approximately 7 days should be made for the patient. At the review visit, the patient should be questioned concerning any problems that have been experienced when wearing the denture. A thorough examination should then be carried out of the oral tissues and the denture, in the course of which signs of tissue damage may be observed. A diagnosis is then made of the cause of all the problems revealed in the history and examination procedures. Appropriate treatment should then be applied to resolve these problems.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• ETRI Journal
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• v.46 no.4
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• pp.671-682
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• 2024

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.