• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1386-1394
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• 2022

As consumption of high-quality fruits increases and sales and packaging units become smaller, the demand for automatic fruit grading systems is increasing. Compared to other crops, the quality of fruit is determined by visual characteristics such as shape, color, and scratches, rather than just physical size and weight. Accordingly, this study presents a CNN model that can effectively extract and classify the visual features of fruits and a perceptron that classifies fruits using physical features, and proposes a stacking ensemble model that can effectively combine the classification results of these two neural networks. The experiments with AI Hub public data show that the stacking ensemble model is effective for grading fruits. However, the ensemble model does not always improve the performance of classifying all the fruit grading. So, it is necessary to adapt the model according to the kind of fruit.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.130-138
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• 1996

In general, the scattler is observed in fatigue test data due to the nonhomogeneity of a material. Consequently. It is necessary to use the statistical method to describe the fatigue crack growth process precisely. Bogdanoff and Kozin suggested and developed the B-model which is the probabilistic models of cumulative damage using the Markov process in order to describe the damage process. But the B-model uses only constant probability ratior(r), so it is not consistent with the actual damage process. In this study, the r-decreasing model using a monotonic decreasing function is introduced to improve the B-model. To verify the model, thest data of fatigue crack growth of A12024-T351 and A17075-T651 are used. Compared with the empirical distribution of test data, the distribution from the r-decreasing model is satisfactory and damage process is well described from the probabilistic and physical viewpoint.

• Smart Media Journal
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• v.12 no.5
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• pp.9-16
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• 2023

In the recommendation system of the credit card company, it is necessary to understand the customer patterns to predict a customer's next merchant based on their histories. The data we want to model is much more complex and there are various patterns that customers choose. In such a situation, it is necessary to use an effective model that not only shows the relevance of the merchants, but also the relevance of the customers relative to these merchants. The proposed model aims to predict the next merchant for the customer. To improve prediction performance, we propose a novel model, called Customer-based Recommendation Model (CRM), to produce a more efficient representation of customers. For the next merchant recommendation system, we use a synthetic credit card usage dataset, BC'17. To demonstrate the applicability of the proposed model, we also apply it to the next item recommendation with another real-world transaction dataset, IJCAI'16.

• Journal of The Korean Association For Science Education
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• v.27 no.8
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• pp.724-742
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• 2007

The purpose of this study was to develop a scientific inquiry model that makes scientific inquiry accessible to science teachers as well as students. To develop a scientific inquiry model, we investigated the research process demonstrated by ten scientists who were working at academic research institutions or industrial research institutions. We collected data through scientists' journal articles, lab meetings and seminars, and observation of their inquiry process. After we analyzed the scientists' inquiry strategies and processes of inquiry, we finally developed the Scientist's Methodology of Investigation Process model named SMIP. The SMIP model consists of four domains, 15 stages, and link questions, such as "if, why", and "how". The SMIP model stressed that inquiry process is a selective process rather than a linear or a circular process. Overall, these findings can have implication science educators in their attempt to design instruction to improve the scientific inquiry process.

• The Journal of Industrial Distribution & Business
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• v.8 no.7
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• pp.39-49
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• 2017

Purpose - There are common factors both in Technology Readiness and Acceptance Model and Technology Paradox Theory which can be put together and made in one unified model. The unified model can provide the following merits. First, the unified model is simple but contains factors of the models. Second, the unified model can clarify the process of technology acceptance of common consumers. Third, the unified model can provide the opportunities to analyze the negative sides of new technology, thus find ways to improve the level of acceptance by general consumers. Research design, data, and methodology - The 450 questionnaires were handed out to people around Seoul and 421 were collected. Except insincere and wrong-marked ones, 402 were used to analyze. SPSS program was used to analyze. Factor analysis, regression analysis was conducted to test the hypotheses. Results - By analyzing sub-factors of both models and binding the common factors in one category, we accomplish one model. And we tested the model by empirical method. The results show that the results from the unified model are almost same as the results from the two models. In other words, the unified model works. Conclusions - Explaining one state of affair by two different method is in some sense distracting attention. By devising a new model including factors of both models, we can explain the affair more straightforward and efficiently. At first the technology acceptance model was devised to explain the technology users in an organization and the following tests and revised models were for the similar purposes. However, as on-lone activities including contracts have been expanded and become important, consumers as the technology uses have emerged as first factor to consider. In accordance models to explain this situation has been suggested. The model suggested in this research is one of the models but it has the following merits. That is, it is simple but has strong explanation power, it can clarify the process of technology acceptance of common consumers by containing negative sides of consumer conception, and thus, it can provide the opportunities to analyze the negative sides of new technology, also find ways to improve the level of acceptance by general consumers.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.4
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• pp.69-74
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• 2013

According to the axial torsion applied at power transmission and the vibration from the roughness of road surface, this paper analyzes the stresses on two kinds of universal joint model. As stress and deformation at model 2 becomes smaller than model 1 on structural analysis, model 2 is more stabilized than model 1. The natural frequencies at model 1 and 2 are 7,040 and 9,540 Hz respectively. As the natural frequency range of model 2 becomes higher than model 1, model 2 becomes safer than model 1. Critical frequencies at these models are calculated through harmonic response analyses. On critical frequencies at model 1 and 2, the stress at model 2 becomes lower than 2 times as much as model 1 and the deformation at model 2 becomes lower than 4 times as much as model 1. Model 2 on durability is thought to become better than model 1. This study result is applied with the design of safe universal joint and it can be useful to improve the durability by predicting prevention against the deformation due to its vibration.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.