• Computers and Concrete
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• v.34 no.1
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.93-103
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• 2024

Mongolia's diverse geographical landscape and harsh climate make it particularly susceptible to various natural disasters, including forest fires, heavy rains, dust storms, and heavy snow. This study aims to explore the relationships between key climatic variables and the frequency of these disasters. We collected monthly data from January 2022 to April 2024, encompassing average temperature, temperature variability (absolute temperature difference), average humidity, and precipitation across the capitals of Mongolia's 21 provinces and the capital city Ulaanbaatar. The data were analyzed using multiple statistical models: Linear Regression, Poisson Regression, and Negative Binomial Regression. Descriptive statistics provided initial insights into the variability and distribution of the climatic variables and disaster occurrences. The models aimed to identify significant predictors and quantify their impact on disaster frequencies. Our approach involved standardizing the predictor variables to ensure comparability and interpretability of the regression coefficients. Our findings indicate that climatic variables significantly affect the frequency of natural disasters. The Negative Binomial Regression model was particularly suitable for our data, which exhibited overdispersion common characteristic in count data such as disaster occurrences. Understanding these relationships is crucial for developing targeted disaster management strategies and policies to mitigate the adverse effects of climate change on Mongolian communities. This research provides valuable insights into how climatic changes impact disaster occurrences, offering a foundation for informed decision-making and policy development to enhance community resilience.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.345-355
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• 2017

The purpose of this study was to examine the validity and reliability of the Communication Skills Attitude Scale, which is used to examine communication learning attitudes for domestic nursing students. Study subjects were 401 nursing students at two nursing college who completed the CSAS scale consisting of 26 items from June 1 to 15. Data were analyzed using exploratory factor analysis, confirmatory factor analysis, internal consistency with IBM Statistics SPSS 21.0, and the IBM Statistics AMOS 21.0 program. To verify the construction factor of the scale, exploratory factor analysis with varimax rotation was performed, resulting in four factors but confirmed positive and negative attitudes two factors with 19 items considering the construct of theory and interpretability. The internal structure of the scale was schematized using confirmatory factor analysis, and goodness of fit of the final research model was very appropriate as shown by ${\chi}^2=446.475$ (df=148, p<0.001), TLI=.90, CFI=.91, RMSEA=.07, SRMR=.05. The final scale consisted of 19 items and two factors based on the confirmatory factor analysis. Cronbach's ${\alpha}$ for final scale was .90, showing internal consistency. The CSAS is expected to be useful to monitor the effectiveness of multiple teaching strategies about communication for domestic nursing students.

• Korean Journal of Environment and Ecology
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• v.27 no.5
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• pp.642-649
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• 2013

This study evaluated the efficacy of information and self-interpretive media that provide information on park environment using self-administered visitor survey to the Dadohaehaesang National Park, Korea. Excluding missing and unreliable responses, 205 valid responses were used for the analysis. Socioeconomic status and visiting behavior of the visitors to the Dadohaehaesang National Park were similar to those to other Korean national parks. Results showed that, of the self-interpretive media, 'Information board of park use and resources' were most frequently used (87.7%), followed by 'Interpretive label of woody plant,' and 'Bulletin boards for information and enlightenment.' 'Guided interpretation' was used less than 40% of the visitors. Visitors also highly rated the importance of the media (higher than 4.0 on average out of 5 point Liker scale question). The average performance rate was 3.82, suggesting that visitors were satisfied on the self-interpretive media. Visitors responded that 'Information board of park use and resources' and 'Bulletin boards for information and enlightenment' were not useful and, therefore, should be amended and managed to improve the self-interpretability of the media.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.803-812
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• 2021

This study explored the concept of artificial intelligence and the problem-solving process that can be explained through literature research. Through this study, the educational meaning and application plan of artificial intelligence that can be explained were presented. XAI education is a human-centered artificial intelligence education that deals with human-related artificial intelligence problems, and students can cultivate problem-solving skills. In addition, through algorithmic education, it is possible to understand the principles of artificial intelligence, explain artificial intelligence models related to real-life problem situations, and expand to the field of application of artificial intelligence. In order for such XAI education to be applied in elementary schools, examples related to real world must be used, and it is recommended to utilize those that the algorithm itself has interpretability. In addition, various teaching and learning methods and tools should be used for understanding to move toward explanation. Ahead of the introduction of artificial intelligence in the revised curriculum in 2022, we hope that this study will be meaningfully used as the basis for actual classes.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.139-159
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• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.