• The Korean Journal of Archival Studies
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• no.49
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• pp.247-275
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• 2016

The classification scheme of records indicates a table that intends to express organic relations between records by organizing records and enabling internal order. Although the principles of organic classification have remained in traditional records management environment, they have been changed to "function and business" in the modern times. Therefore, Korea introduced a business reference model (BRM) based on function and business from 2008 and subsequently implemented its operation. However, it has been pointed out that the roles of the classification scheme of records have not been played because the analysis of "Danwigwaje," which belongs to the lowest level of business reference models, is poor. According to this indication, the Gangbuk District of Seoul Special City established a functional classification scheme by executing a business process analysis of "Danwigwaje." First, the record manager carried out analyses on the principles of "Danwigwaje," small function, and "Danwigwaje." Then, the functional classification scheme of "Danwigwaje" was modified by looking into the opinion inquiry process of the treatment department and performing a test operation. Through the case of the Gangbuk District in Seoul Special City, analytical procedures and methods of "Danwigwaje," as well as implications according to the establishment of a functional classification scheme of basic local governments, were arranged in a written format.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.6 no.1
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• pp.81-109
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• 1984

The purpose of this research is (i) to establish the principles particularly appropriate for the arrangement of archival collections is korea, and (ii) to produce a workable model of classification scheme in conformity with the established principles. The archival collections in korea are roughly devided into two groups as follows. (1) The collections of professional institutions of archives such as Korean National Archives. (2) The collections preserved by libraries, museums, and other similar institutions as a secondary collection, and these groups of collections are generally non-systematic collecting. For the arrangement of the former collections, the concept of "respect des fonds" which is universally accepted principies in archives are also applicable. But in case of the arrangement of the latter collections, the above mentioned principles are inappropriate because its collections a re built in separate pieces of documents and records without any relevance to the original function or structure of the corporation. Consequantly it is badly needed to make some devices for the arrangement of these archival collections since the archival collections of korea, in the majority of cases, belong to the latter. The author produced a tentative classification scheme, and adapted the korean traditional form (or type) of documents and records as a cardinal principle of the classification. The scheme is presented at the end of this paper.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.1-15
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• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.2
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• pp.129-163
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• 2017

This study investigated the administrative history, from the introduction of the "Standard of Government Function Classification" (BRM) to its development and application. Through the results of the survey, the causes of the problems observed in the current government's functional classification system were revealed. The current survey examined the functional classification scheme of the central government and local governments on the "cultural heritage" policy area (9 major functions, 59 middle functions, 297 small functions, and 1,287 unit tasks). It confirmed the problem of the separation of functions between central and local governments as well as other problems. Among the problems, this study proposed an improvement model through four representative cases such as the "designation of cultural heritage." In order to reorganize the "Standard of Government Function Classification," it is necessary to design a business function with the reproduction of tasks, establish a system for management and operation in order to maintain the consistency of the business function, educate users, and suggest continuous improvement.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• The Korean Journal of Archival Studies
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• no.61
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• pp.31-69
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• 2019

This study seeks to find an organizational method suitable for literary records through a review of the application of records management and an archival exploration of the literary materials of the authors, which are housed in a decentralized collection of domestic literary museums. First, through literature research and case analysis, I explored the "principles of original order" for organizing by characteristics and values of literary records. Next, the organization model was applied to the literature materials of author Jo Jung-rae(1943~) that existed in the form of a 'split-collection' in the local literature museum after drawing a model suitable for organizing literary records as an example. In order to gain an integrated approach to the 'split-collection' by Cho Jung-rae, the research result suggests a model provided through a single gateway by linking descriptive information related to ICA AtoM-based 'Records-Writers-Literature Museum'. The organizational model for the collection of individual literature museum was designed to provide richer collective and contextual information compared to the existing simple list by developing a hierarchical classification system in accordance with the principle of record organizing.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.2
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• pp.119-145
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• 2016

This study aims to develop the records management standard table of the college of education through mapping transaction and records folder based on identifying the problems of the college of education. For these purpose, the study analyzed the records classification system of the all college of education. And then, drew out the business transaction items for records management standard table of the college of education based on the records management standard table of the local governments. Consequently, based on analysis, this study developed the records management standard table and suggested efficient management plan to apply Business Reference Model(BRM) to the college of education in 2017.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.23-31
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• 2024

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.