• Journal of Digital Convergence
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• v.18 no.6
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• pp.297-309
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• 2020

This study focuses on the application of the data mining technique logistic regression analysis and decision tree analysis to the domestic public database called Korean Children Youth Panel Survey (KCYPS) to derive a series of important factors affecting the enhancement of life satisfaction of domestic youth. As a result, the general impact factors on life satisfaction for each grade were derived from logistic regression. Using decision tree analysis, we came to conclusions that those factors such as depression, overall grade satisfaction, household economic level, and school adaptation play crucial roles in affecting high school adolesscents' life satisfaction.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.38-42
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• 2016

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.777-786
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• 2018

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.291-303
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• 2006

Data mining is the work to extract information from existing data file. So, the one of best important thing in data mining process is the quality of data to be used. In this thesis, we propose the data fusion technique using decision rule for data enrichment that one phase to improve data quality in KDD process. Simulations were performed to compare the proposed data fusion technique with the existing techniques. As a result, our data fusion technique using decision rule is characterized with low MSE or misclassification rate in fusion variables.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Journal of KIISE
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• v.44 no.1
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• pp.21-26
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• 2017

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.305-312
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• 2020

As a demand for a new video coding standard having higher coding efficiency than the existing standards is growing, recently, MPEG and VCEG has been developing and standardizing the next-generation video coding project, named Versatile Video Coding (VVC). Many inter prediction techniques have been introduced to increase the coding efficiency, and among them, an adaptive motion vector resolution (AMVR) technique has contributed on increasing the efficiency of VVC. However, the best motion vector can only be determined by computing many rate-distortion costs, thereby increasing encoding complexity. It is necessary to reduce the complexity for real-time video broadcasting and streaming services, but it is yet an open research topic to reduce the complexity of AMVR. Therefore, in this paper, an efficient technique is proposed, which reduces the encoding complexity of AMVR. For that, the proposed method exploits a special VVC tree structure (i.e., multi-type tree structure) to accelerate the decision process of AMVR. Experiment results show that the proposed decision method reduces the encoding complexity of VVC test model by 10% with a negligible loss of coding efficiency.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.639-647
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• 2008

This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.