• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.139-155
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• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.267-273
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• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.685-692
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• 2024

In this paper, we propose a new CNN model to improve the recognition performance of command voice signals. This method obtains a spectrogram image after performing a short-time Fourier transform (STFT) of the input signal and improves command recognition performance through supervised learning using a CNN model. After Fourier transforming the input signal for each short-time section, a spectrogram image is obtained and multi-classification learning is performed using a CNN deep learning model. This effectively classifies commands by converting the time domain voice signal to the frequency domain to express the characteristics well and performing deep learning training using the spectrogram image for the conversion parameters. To verify the performance of the speech recognition system proposed in this study, a simulation program using Tensorflow and Keras libraries was created and a simulation experiment was performed. As a result of the experiment, it was confirmed that an accuracy of 92.5% could be obtained using the proposed deep learning algorithm.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.4
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• pp.325-332
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• 2022

Backgrounds/Aims: To analyze relationships of hepatic histopathological findings and bile microbiological profiles with perioperative outcomes and risk of late biliary stricture in individuals undergoing surgical bile duct injury (BDI) repair. Methods: A historical cohort study was carried out at a tertiary university hospital. Fifty-six individuals who underwent surgical BDI repair from 2014-2018 with a minimal follow-up of 24 months were enrolled. Liver biopsies were performed to analyze histopathology. Bile samples were collected during repair procedures. Hepatic histopathological findings and bile microbiological profiles were then correlated with perioperative and late outcomes through uni- and multi-variate analyses. Results: Forty-three individuals (76.8%) were females and average age was 47.2 ± 13.2 years; mean follow-up was 38.1 ± 18.6 months. The commonest histopathological finding was hepatic fibrosis (87.5%). Bile cultures were positive in 53.5%. The main surgical technique was Roux-en-Y hepaticojejunostomy (96.4%). Overall morbidity was 35.7%. In univariate analysis, liver fibrosis correlated with the duration of the operation (R = 0.3; p = 0.02). In multivariate analysis, fibrosis (R = 0.36; p = 0.02) and cholestasis (R = 0.34; p = 0.02) independently correlated with operative time. Strasberg classification independently correlated with estimated bleeding (R = 0.31; p = 0.049). The time elapsed between primary cholecystectomy and BDI repair correlated with hepatic fibrosis (R = 0.4; p = 0.01). Conclusions: Bacterial contamination of bile was observed in most cases. The degree of fibrosis and cholestasis correlated with operative time. The waiting time for definitive repair correlated with the severity of liver fibrosis.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.

• Physical Therapy Korea
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• v.31 no.2
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• pp.174-181
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• 2024

Background: Effective management of clinical assessment tools is critical in stroke and brain injury rehabilitation research. Managing rehabilitation outcome measures (ROMs) scores and training therapists in multicenter randomized clinical trials (RCTs) is challenging. Objects: The aim of this study was to develop a web-based platform, the Korean Rehabilitation Outcome Measurement (KoROM), to address these limitations and improve both therapist training and patient involvement in the rehabilitation process. Methods: The development of the KoROM spanned from June 2021 to July 2022, and included literature and web-based searches to identify relevant ROMs and design a user-friendly platform. Feedback from six physical therapy and informatics experts during pilot testing refined the platform. Results: Several clinical assessment tools categorized under the International Classification of Functioning, Disability, and Health (ICF) model are categorized in the KoROM. The therapist version includes patient management, assessment tool information, and data downloads, while the patient version provides a simplified interface for viewing scores and printing summaries. The master version provides full access to user information and clinical assessment scores. Therapists enter clinical assessment scores into the KoROM and learn ROMs through instructional videos and self-checklists as part of the therapist standardization process. Conclusion: The KoROM is a specialized online platform that improves the management of ROMs, facilitates therapist education, and promotes patient involvement in the rehabilitation process. The KoROM can be used not only in multi-site RCTs, but also in community rehabilitation exercise centers.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Sasang Constitutional Medicine
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• v.11 no.1
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• pp.137-157
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• 1999

The purpose of this study is to help classifying Sasang Constitution through correlation with sound characteristic. This study was done it under the suppose that Sasang Constitution has correlation with sound spectrogram. The following result were obtained about correlation between sound spectrogram and Sasang Constitution by comparison and analysis 1. Soeumin answered his voice low tone, smooth and quiet in the survey. Soyangin answered his voice high, clear, fast and speaking random. Taeumin answered his voice low, thick and muddy. 2. Taeyangin was significantly slow compared with the others in the time of reading composition. Taeyangin was significantly slow compared with the others in Formant frequency 1. Taeyangin was significantly discriminated from Soeumin in Formant frequency 5. Taeyangin was significantly low compared with the others in Bandwidth 2. Soeumln was significantly low compared with Taeyangin in Pitch Maximum and Pitch Maximum-Pitch Minimum. Taeyangin was significantly high compared with the others in Energy mean. 3. In list of specification, the discrimination rate was higher than that by lists of 13 in the results of Multi-dimensional 4-class minimum-distance. The discrimination rate of three disposition except Soyangin was higher than that of four disposition in the results of One way ANOVA and Analysis of dis crimination in SPSS/PC+. In CART, the estimate rate of Sasang Constitution discrimination was higher than any other method. It is considered that there is a correlation between sound spectrogram and Sasang constitution according to the results. And method of Sasang constitution classification through sound spectrogram analysis can be one method as assistant for the objectification of Sasang constitution classification.

• Korean Journal of Health Education and Promotion
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• v.6 no.2
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• pp.23-39
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• 1989

There have been a lot of considerable. discussion and debate surrounding the management model in the health insurance management system and opinions regarding the management operating cost. It is a well known fact that there have always been dissenting opinions and debates surrounding the issue. The management operating cost varies according to the scale of the management organization and component members characteristics of the insurance carrier. Therefore, it is necessary to examine and compare the management operating cost to the simulated management models developed to cover those eligible for the health insurance scheme in this country. Since the management operating cost can vary according to the different models of management, four alternative management models have been established based on the critical evaluation of existing theories concerned, as well as on the basis of the survey results and simulation attempts. The first alternative model is the Unique Insurance Carrier Model(Ⅰ) ; desigened to cover all of the people with no classification of insurance qualifications and finances from the source of contribution of the insured, nationwide. The second is the Management Model of Large-scale District Insurance Carrier(Ⅱ) ; this means the Korean society would be divided into 21 large districts; each having its own insurance carrier that would cover the people in that particular district with no classification of insurance qualifications arid finances as in Model I. The third is the Management Model of Insurance Carrier Divided by Area and Classified with Occupation if Largescale (Ⅲ) ; to serve the self-employed in the 21 districts divided as in Model Ⅱ. It would serve the employees and their dependents by separate insurance carriers in large-scale similar to the area of the district-scale for the self-employed, so that the insurance qualifications and finances would be classified with each of the insurance carriers: The last is the Management Model of the Multi - insurance Carrier (Ⅳ) based on the Si. Gun. Gu area which will cover their own self- employed people in the area with more than 150 additional insurance carriers covering the employees and their dependents. The manpower necessary to provide services to all of the people according to the four models is calculated through simulation trials. It indicates that the Management Model of Large-scale District Insurance Carrier requires the most manpower among the four alternative models. The unit management operating costs per the insured individuals and covered persons are leveled with several intervals based on the insurance recipients. in their characteristics. The interval levels derived from the regression analysis reveal that the larger the scale of the insurance carriers is in the number of those insured and covered. the more the unit management operating cost decreases. significantly. Moreover. the result of the quadratic functional formula also shows the U-shape significantly. The management operating costs derived from the simulated calculation. on the basis of the average salary and related cost per staff- member of the Health Insurance Societies for Occupational Labours and Korean Medical Insurance Corporation for the Official Servants and Private School Teachers in 1987 fiscal year. show that the Model of Multi-insurance Carrier warrants the highest management operating cost. Meanwhile the least expensive management operating cost is the Management Model of Unique Insurance Carrier. Insurance Carrier Divided by Area and Classified with Occupation in Large-scale. and Large-scale District Insurance Carrier. in order. Therefore. it is feasible to select the Unique Insurance Carrier Model among the four alternatives from the viewpoint of the management operating cost and in the sense of the flexibility in promoting the productivity of manpower in the human services field. However. the choice of the management model for health insurance systems and its application should be examined further utilizing the operation research analysis for such areas as the administrative efficiency and factors related to computer cost etc.