• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.99-102
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• 2001

We propose a PC cluster using general-purpose microprocessors and a high-speed network for simulating ANN (Artificial Neural Network) processes on Linux OS. We apply this cluster to intelligent information processing such as ANN simulation. The elapsed time for simulating ANNs can be reduced from 7,295 seconds by a PE (Processing Element) to 1,226 seconds by six PEs. The reliability of a pattern-classification using ANNs can be improved by the proposed ANN, Randomized-ANN. In order to generate a Randomized-ANN, we choose three ANNs and combine the output results from three huts by means of logical AND. Results are as follows: The mean correct answer rate is 94.4%, the mean wrong answer rate is only 0.1 %, and the mean unknown answer rate is 5.5 %. We make sure that Randomized-ANN approach reduces the mean wrong answer rate within a tenth part and improves the reliability of Japanese coin classification.

• The Korean Journal of Zoology
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• v.5 no.1
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• pp.35-38
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• 1962

Two subspecies are known to Family Hylidae(Order Anura) in Korea : namely , Hylaarborea japonica GUENTHER and H.a.stepheni BOULENGER, and they have been hitherto distinguished as different subspecies from four characteristics : the rate of interorbital to incternasal length, the rate of diameter of tympanum to diameter of 3 rd finger disc, the rate of the length of inner metatarsal tubercle to diameter of 3 rd finger disc, and the rate of inner metatarsal tubercle to the length of lst toe. The author has compared the above to subspecies for the characteristics with 123 individuals collected from ten different localities in Korea and has found that their fluctation curves overlap each other. The author considers, therefore, that the four characteristics could not be assumed as keys for the classification of the two subspecies and has reached the conclusion that these two subspecies should be regarded as one subspecies and Hyla arborea japonica GUENTHER should be given to both of them as the subspecies name.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.99-104
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• 2008

The combustion properties of sandwich panels were tested and analyzed according to ISO 13784-1(Room Corner Test for Sandwich panel building systems) test method for the purpose of establishing the classification of reaction to fire performance. Several variables including heat release rate, smoke production rate, FIGRA, SMOGRA, and so on, were analyzed for specific four materials about sandwich panel systems on each 5 times, totally 20 times. Finally, elements for Classification system were suggested and evaluations for those elements were made.

• Korean Journal of Cognitive Science
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• v.3 no.2
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• pp.249-270
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• 1992

The purpose of this paper is to classify the types of input characters,printed Hangul characters,using Maximum Run Length Projection(MRLP)that is used to extract features of input character.Because the number of Hangul characters is large and its structure is complex,there exists close similarities among characters.This paper,therefore,tried to increment the type classification rate using fuzzy resoning.The Maximum Run Length Projection is very immune to noise,and also useful to extracting the demanding information efficiently.In a test case with the most frequently use 917 printed Hangul characters,it achieved 98.58%correct classification rate.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.147-154
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• 2011

In this Paper, we suggest the effective pre-correction algorithm on sensor values and the classification algorithm for gesture recognition that use values for each axis of the accelerometer to send data(a number or specific input data) to device. we know that creation of reliable preprocessed data in experimental results through the error rate of X-Axis and Y-Axis for pre-correction and post-correction. we can show high recognition rate through recognizer using the normalization and classification algorithm for the preprocessed data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.4
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• pp.23-29
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• 2008

This research proposes two sub-models and one integrated model for the classification of safety in curve section of road, where the fatal-rate is relatively higher in accidents. The first sub-model calculates the accident-rate by safety-index that is based on the road geometries. The second decides the safety of curve section by the speed difference between before and in the curve. Finally, the integrated model of two sub-modules can classify the safety of curve section of road.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.