• The Transactions of the Korea Information Processing Society
• /
• v.4 no.9
• /
• pp.2309-2314
• /
• 1997

The study introduces a neural network to predict the bankruptcy of insurance companies. As a method to optimize the network, a genetic algorithm suggests optimal structure and network parameters. The neural network designed by genetic algorithm is compared with discriminant analysis, logistic regression, ID3, and CART. The robust neural network model shows the best performance among those models compared.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.5
• /
• pp.57-62
• /
• 2010

Recently, decision tree techniques have been studied in terms of quick searching and extracting of massive data in medical fields. Although many different techniques have been developed such as CART, C4.5 and CHAID which are belong to a pie in Clermont decision tree classification algorithm, those methods can jeopardize remained data by the binary method during procedures. In brief, C4.5 method composes a decision tree by entropy levels. In contrast, CART method does by entropy matrix in categorical or continuous data. Therefore, we compared C4.5 and CART methods which were belong to a same pie using breast cancer data to evaluate their performance respectively. To convince data accuracy, we performed cross-validation of results in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.1
• /
• pp.466-483
• /
• 2017

Zen Cart is an open-source online store management system. It is used all over the world because of its stability and safety. Today, Zen Cart's session security mechanism is mainly used to verify user agents and check IP addresses. However, the security in verifying the user agent is lower and checking the IP address can affect the user's experience. This paper, which is based on the idea of session protection as proposed by Ben Adida, takes advantage of the HTML5's sessionStorage property to store the shared keys that are used in HMAC-SHA256 encryption. Moreover, the request path, current timestamp, and parameter are encrypted by using HMAC-SHA256 in the client. The client then submits the result to the web server as per request. Finally, the web server recalculates the HMAC-SHA256 value to validate the request by comparing it with the submitted value. In this way, the Zen Cart's open-source system is reinforced. Owing to the security and integrity of the HMAC-SHA256 algorithm, it can effectively protect the session security. Analysis and experimental results show that this mechanism can effectively protect the session security of Zen Cart without affecting the original performance.

• The Journal of Engineering Geology
• /
• v.31 no.3
• /
• pp.395-432
• /
• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Journal of the Korea Safety Management & Science
• /
• v.16 no.4
• /
• pp.275-284
• /
• 2014

Decision Tree is one of analysis techniques which conducts grouping and prediction into several sub-groups from interested groups. Researcher can easily understand this progress and explain than other techniques. Because Decision Tree is easy technique to see results. This paper uses CART algorithm which is one of data mining technique. It used 273 variables and 70094 data(2010-2011) of working environment survey conducted by Korea Occupational Safety and Health Agency(KOSHA). And then refines this data, uses final 12 variables and 35447 data. To find satisfaction factor in working environment, this page has grouped employee to 3 types (under 30 age, 30 ~ 49age, over 50 age) and analyzed factor. Using CART algorithm, finds the best grouping variables in 155 data. It appeared that 'comfortable in organization' and 'proper reward' is the best grouping factor.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.2
• /
• pp.129-136
• /
• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.1
• /
• pp.128-141
• /
• 2010

Drought indices such as SPI(Standard Precipitation Index) and PDSI(Palmer Drought Severity Index) estimated using ground observations are not enough to describe detail spatial distribution of drought condition. In this study, the drought index with improved spatial resolution was estimated by using the CART algorithm and ancillary data such as MODIS NDVI, MODIS LST, land cover, rainfall, average air temperature, SPI, and PDSI data. Estimated drought index using the proposed approach for the year 2008 demonstrates better spatial information than that of traditional approaches. Results show that the availability of satellite imageries and various associated data allows us to get improved spatial drought information using a data mining technique and ancillary data and get better understanding of drought condition and prediction.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.707-715
• /
• 2023

This study utilized CART algorithm, a decision tree analysis method, to comparatively analyze factors affecting housing and residential environment satisfaction by region using data from Ministry of Land, Infrastructure and Transport's housing survey in 2020. First, in terms of residential environment satisfaction, accessibility to medical facilities and school district showed higher importance in metropolitan cities and areas compared to other regions, whereas safety from accident showed the opposite trait, showing difference between region. Second, housing characteristics were important in housing satisfaction, indoor environment level satisfaction and indoor safety and hygiene being important in almost all regions, while residential environment characteristics were more important in residential environment satisfaction and influencing factors were relatively evenly distributed. In order to generalize these regional characteristics, research using time series data needs to be conducted later.

• Journal of the military operations research society of Korea
• /
• v.37 no.1
• /
• pp.147-159
• /
• 2011

Recently, the military service period has been shortened continuously. Meanwhile, more skilled airmen are needed as the complexity of weapon systems increase. This phenomenon could lead to a disastrous result such as deteriorating the level of the readiness and the fighting power. We suggest a method to improve recruit's maintenance capability rapidly by assigning airmen to jobs appropriate to their characteristics using Datamining methods (K-menas and CART). We focus on the assigning method for air force's air-munition maintenance airmen since they are requested more skilled than other airmen. Grouping airmen with k-means method and devising classification rule with CART algorithm, we found that airmen's proficiency arrival period could be shortened by 1.79 months when they are assigned in the suggested way.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.268-274
• /
• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.