• Communications of Mathematical Education
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• v.37 no.2
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• pp.257-276
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• 2023

Mathematical modeling can be described as a series of processes in which real-world problem situations are understood, interpreted using mathematical methods, and solved based on mathematical models. The effectiveness of mathematics instruction using mathematical modeling has been demonstrated through prior research. This study aims to explore insights for mathematical modeling instruction by analyzing the metacognitive characteristics shown in the mathematical modeling cycle, according to the mathematical thinking styles of elementary math gifted students. To achieve this, a mathematical thinking style assessment was conducted with 39 elementary math gifted students from University-affiliated Science Gifted Education Center, and based on the assessment results, they were classified into visual, analytical, and mixed groups. The metacognition manifested during the process of mathematical modeling for each group was analyzed. The analysis results revealed that metacognitive elements varied depending on the phases of modeling cycle and their mathematical thinking styles. Based on these findings, didactical implications for mathematical modeling instruction were derived.

• Journal of Technologic Dentistry
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• v.45 no.3
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• pp.67-73
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• 2023

Purpose: This in vitro study aimed to evaluate the clinical acceptability of precision of fit of the support thickness of Co-Cr alloy copings fabricated using selective laser melting (SLM). Methods: Thirty dental stone models of maxillary left molar abutments were manufactured, images were taken using a scanner, and a computer-aided design program was used to design the form of a conventional metal ceramic crown coping. Overall, 30 single copings were made from Co-Cr alloy using SLM and divided into three support radius groups (0.1, 0.25, and 0.35 mm) of 10 for each. Digitized data were superimposed with three-dimensional inspection software to quantitatively obtain the machinability of a ceramic crown coping, and visual differences were confirmed using a color map. The root mean square values of the ceramic crown coping group were statistically analyzed using one-way analysis of variance (α=0.05). Results: The precision of fit was superior with 0.25 mm compared with 0.1 mm and 0.35 mm, and the results exhibited significant differences (p<0.05). All specimens showed that various support thicknesses did not exceed the clinically permitted value of 120 ㎛, which mean that more than 0.1 mm and 0.35 mm of support radius for SLM was adequate. Conclusion: The support thickness of Co-Cr alloy restoration fabricated using SLM is shown to affect the adaptation.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.101-111
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• 2024

In this paper, we propose a concpetual architecture and its implementation approach for contextualizing unstructured CCTV-video frame data into structured XML-video textual data by using the deep-learning neural network models and frameworks. Conclusively, through the conceptual architecture and the implementation approach proposed in this paper, we can eventually realize and implement the so-called sharable working and experiencing knowledge management platforms to be adopted to smart factories in various industries.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.87-99
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• 2024

This study aims to identify the competencies required for webtoon artists to survive in the future era of AI commercialization. It explores the current and future use of AI in webtoons, and predicts the role of artists in the future webtoon industry. The study finds that AI will replace human workers in some areas, but human empathy-related fields can be sustained. Artist roles like story projectors, Visual directors, and AI editors were identified as potential models for the changing role of artists. To address terminology ambiguity, a three-step AI categorization mechanical type AI, humanoid type AI, and transcendent type AI was proposed for a more realistic separation of AI capabilities. The researcher suggested these findings as guidelines for developing skills in emerging artists or re-skilling existing ones, emphasizing collaboration with AI for mutual growth rather than a negative acceptance of new technology.

• Korean Journal of Construction Engineering and Management
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• v.25 no.3
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• pp.58-67
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• 2024

In the era of the fourth industrial revolution, data plays a vital role in enhancing the productivity of industries. To advance digitalization in the construction industry, which suffers from a lack of available data, this study proposes a model that classifies construction site photos by work types. Unlike traditional image classification models that solely rely on visual data, the model in this study includes semantic analysis of construction work types. This is achieved by extracting the significance of relationships between objects and work types from the standard construction specification. These relationships are then used to enhance the classification process by correlating them with objects detected in photos. This model improves the interpretability and reliability of classification results, offering convenience to field operators in photo categorization tasks. Additionally, the model's practical utility has been validated through integration into a classification program. As a result, this study is expected to contribute to the digitalization of the construction industry.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Dementia and Neurocognitive Disorders
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• v.22 no.4
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• pp.148-157
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• 2023

Background and Purpose: Episodic memory is a system that receives and stores information about temporally dated episodes and their interrelations. Our study aimed to investigate the relevance of episodic memory to time perception, with a specific focus on simultaneity/order judgment. Methods: Experiment 1 employed the simultaneity judgment task to discern differences in time perception between patients with mild cognitive impairment or dementia, and age-matched normals. A mathematical analysis capable of estimating subjects' time processing was utilized to identify the sensory and decisional components of temporal order and simultaneity judgment. Experiment 2 examined how differences in temporal perception relate to performance in temporal order memory, in which time delays play a critical role. Results: The temporal decision windows for both temporal order and simultaneity judgments exhibited marginal differences between patients with episodic memory impairment, and their healthy counterparts (p = 0.15, t(22) = 1.34). These temporal decision windows may be linked to the temporal separation of events in episodic memory (Pearson's ρ = -0.53, p = 0.05). Conclusions: Based on our findings, the frequency of visual events accumulated and encoded in the working memory system in the patients' and normal group appears to be approximately (5.7 and 11.2) Hz, respectively. According to the internal clock model, a lower frequency of event pulses tends to result in underestimation of event duration, which phenomenon might be linked to the observed time distortions in patients with dementia.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.591-606
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• 2016

The purpose of this study is to develop the argumentation program to build scientific concepts on natural selection for science-gifted elementary students and to know how to implement this program. For this study, nine key concepts about natural selection such as the overproduction of offspring, limited resources, population stability, competition, variation, heredity of variation, differential survival, change of the population and speciation were selected through the literature study. The programs were developed by learning cycle instructional model. Argument writings and discourses have been collected, analyzed and compared before and after the program. Two questionnaires to compare pre and post concept change consist of multiple choice questionnaire and open-ended response question were developed and applied to 19 science-gifted elementary students. Sufficiency of the explanation and conceptual quality of the explanation were used to assess the quality of their arguments before and after the program. Discourse and visual models collected from the highest and lowest group about score improvement were compared. The scores of the gifted statistically improved significantly in multiple choice questionnaire. Students' alternative conceptions about natural selection at the beginning of the program decreased and changed scientifically after the program. Visual models drawn by the students supported the results as well. This study asserts that elementary science-gifted students are able to explain evolutionary perspectives about organism change and use the key concepts of natural selection. The study means that evolutionary perspective is possible to be reflected in elementary science curriculum for the gifted.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.