• Annual Conference of KIPS
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• 2024.10a
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• pp.507-510
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• 2024

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive function. Accurate and early diagnosis of AD is crucial for effective management and treatment. Traditional machine learning models, though commonly applied, often fall short in capturing the intricate relationships between diverse tabular data. Furthermore, the missing data issue, typically addressed using conventional imputation techniques, leads to reduced accuracy and generalizability of AD classification models. This paper introduces ADxClass, a novel deep learning framework that enhances AD classification by leveraging multi-domain attention fusion and data type-based imputation techniques for handling missing heterogeneous tabular data. ADxClass integrates data from various domains, including demographic, cognitive, genetic, and biomarkers obtained from neuroimaging measurements, to improve the robustness and accuracy of AD classification models. The model's efficiency is validated via a 5-fold cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, showing significant improvements in classification performance compared to traditional machine learning approaches.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.375-384
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• 2015

This study is aimed at analyzing class participation value of the students who participate in physical education class implemented by university, and inquiring into the convergence relations between students-perceived class participation propensity and participatory behavior. In an effort to verify hypotheses consequent on this research objectives, this study conducted confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation model analysis using PASW 18.0 and AMOS 18.0. The concrete results of this research are as follows: First, the class participation value of the students participating in general physical education was found to have an influence on participatory propensity. Second, the participatory propensity of the students participating in general physical education was found to have an influence on class participation behavior intention.

• Journal of Fashion Business
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• v.25 no.2
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• pp.98-109
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• 2021

The purpose of this study was to present the case of class operation by paralleling a general instructor's lecture class and a IC-PBL class in a fashion design major course and comparing the educational effects. The teaching model of this study was designed to improve the ability to use the knowledge gained in accordance with the needs of the industrial field and to develop an independent learning ability. It will provide meaningful data. This study measured and considered the qualitative items of self-efficacy and changes in class perception through interviews and questionnaires completed by the learners after experiencing each general class and IC-PBL class. The results of this study are, first, that in the History of Fashion and Costume class, the general teaching method and the IC-PBL teaching method were applied in parallel to design a class, and a method case was presented. Second, as a result of comparing the educational effects of the two teaching methods through a student questionnaire, IC-PBL was more effective in improving learning attitude, learning achievement and self-efficacy. In addition, after the IC-PBL class on History of Fashion and Costume, the students' negative perception of team activities improved, and the students' cooperative ability and creativity improved.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.146-151
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• 2016

This study concentrates on the design of floater for 15kW-class wave energy converter that extracts the ocean energy by oscillating vertically along the wave motion. The floater connects to a arm structure that connects to a hydraulic cylinder, which drives a hydraulic generator. The study mainly focuses on the structural analysis of the floater. Previous studies have been conducted using a miniature model; however, this study focuses on the size selection of the floater for a full scale model. Static structural analysis is conducted using fine numerical grids. Due to the complexity of the whole model, it is analyzed as a separate component. There are several load cases for each floater size, and they are analyzed thoroughly for stress (von-mises, shear, and normal) and deformation. The initial design was conducted by scaling up from the miniature model of the previous study, and the final design has been redesigned by changing the thickness and internal support structure shape.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.41 no.3
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• pp.222-226
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• 2005

A series of model tests carried out at the CWC of WJFEL for the purpose of prediction of resistance for the performance and improvement of resistance by attaching appendage for the ship of 50 knots class planing hull. The resistance performance evaluation has been carried out for the bare hull and for the appendage hull with two different depth of vertical type wedges. In the bare model test, trim and sinkage is calculated for the planing hull and the resistance is calculated. For minimizing the resistance, wedge appendage is attached and tested. Analysis and tests shows that for a 12.5mm wedge, resistance is minimum and overall power tallied to 5636ps.

• Journal of Preventive Medicine and Public Health
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• v.16 no.1
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• pp.113-120
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• 1983

The main objective of this case study is to develop demand forecasting model for durg inventory control in a university hospital. This study is based on the pertinent records during the period of January 1975 to August 1981 in the pharmacy and stock departments of the hospital. Through the analysis of the above records the author made some major findings as follows: 1. In A.B.C. classification, the biggest demand (A class) consists of 9 items which include 6 items of antibiotics. 2. Demand forecasting level of an index or discrepancy in A class drug compared with real demand for 6 months is average 30.4% by X-11 Arima method and 84.6% by Winter's method respectively. 3. After the correcting ty the number of bed, demand forecasting of drug compared with real demand for 6 months is average 23.1% by X-11 Arima method and 46.6% by Winter's method respectively.