• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of Christian Education in Korea
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• v.77
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• pp.225-249
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• 2024

Research Purpose : Based on artificial intelligence, this study considers learners' characteristics, learning content, and individual learning, and analyzes the collected learning data to develop a model that supports customized learning for individual learners. Research content and method : In order to achieve the research purpose, the literature was analyzed to investigate the structure of customized learning support, learning data analysis, and learning activities, and based on the investigated data, the area and detailed components of the customized learning support model were derived. did. A draft model was constructed through literature analysis, and the first expert Delphi survey was conducted on the draft model with five experts. The model was revised by reflecting the results of the first Delphi, and the validity of the revised model was verified through the second expert Delphi. The model was elaborated through expert Delphi, and the final model was constructed through this. Conclusion and Recommendation : Through research, customized learning support area, class management system area, and learning analysis data area were formed, and detailed elements were derived for each area. The results of this study provide basic data that can be used as a reference for constructing a customized learning support system based on artificial intelligence, taking into account the university's class environment.

• Korean Institute of Interior Design Journal
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• v.14 no.2 s.49
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• pp.217-224
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• 2005

The lack of objective decisive criteria and the absence of analyzing tools accrued from the experiments on various types developed from space design process makes it difficult to select and execute alternatives for them. As an attempt of coping with these problems, the aims of this study is to establish space analysis' models and to propose possibility of analyzing models by utilizing the technology of image process. It is now under study in the field of artificial intelligence based on the accomplishment of digital images. This study focused on establishment an analysis model based on accomplished digital images and image processing framework. It helps utilize various processing technologies that are currently in use of image processes, and problems of the study can be supplemented through further follow-up studies. Finally, analysis model can be constructed gradually huge design data in the analogue data to the digital image database and be proposed with index in design or evaluation step.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.151-165
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Korean Institute of Interior Design Journal
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• no.37
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• pp.136-143
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• 2003

Users' demands on the space are changing in variety. These demands include reasonable space and form, harmonious composition with surroundings and esthetic satisfaction that could be brought by personal tastes and preferences. In addition, models that are introduced from designing process and from various forms tend to lack objective decision making standard. Accordingly it is difficult to find a clear alternative plan and process. In an effort to solve these problems, the objects of this study are; to propose an analysis model of image and space by using image process techniques that are on study in the field of artificial intelligence based on acquisition of digital image and to verify the application possibilities of such analysis model, 'Isovist' on quantitative analysis. The model can be applied with variable analysis model, as digital image process and other analysis model such as 'Isovist' It is possible that further study can complement problems from this study.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.331-332
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• 2023

With the aging of buildings, the number and importance of regular inspections of buildings are increasing. The current safety inspection goes through a procedure in which a skilled technician visits an old building, visually checks it, takes a photo, and finally organizes and judges it at the office. For this, field personnel and analysis and review personnel are required. Since the inspection procedure includes taking pictures, a huge amount of data has been accumulated from the time digital photos were used to the present. When a model that can check cracks outside a building is developed using these data, manpower and time required can be greatly reduced. Therefore, this study aims to create a model for classifying cracks that occur outside the building through the artificial intelligence method. The created model can be used as a basic model for determining cracks only by external photography in the future, and furthermore, it can be used as basic data for calculating the size and width of cracks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Advances in nano research
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• v.14 no.6
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• pp.527-539
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• 2023

Measuring energy burn during intensive combat sport has been a challenging concerns for a long time. In the present article, the energy consumption during combat sports is measured by use of wearable GoBe2 equipped with nanotechnology measuring devices. In this regard, 12 professional combat athletes were asked to wear GoBe2 devices during different sessions of intensive combat exercises. The curves provided by GoBe2 nano-sensor devices are further collected and analyzed for different combat durations. On the other hand, energy consumption in these athlete is calculated using other validated methods to evaluate reliability of GoBe2 wearable devices. Based on the results obtained from these experiments a multi-parameter mathematical model is presented for estimation of combat calorie consumptions. The results show that nanotechnology in these type of sensors could help in estimation of calorie consumption during combat. Moreover, the reliability of using wearable GoBe2 sensors are satisfactory except for some specific conditions. The mathematical model provides a satisfactory results based on athlete physical condition and also duration of the combat with about 8% error margin in the results.