• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.259-261
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• 2022

Recently, malicious code is not a single technique, but several techniques are combined and merged, and only important parts are extracted. As new malicious codes are created and transformed, attack patterns are gradually diversified and attack targets are also diversifying. In particular, the number of damage cases caused by malicious actions in corporate security is increasing over time. However, even if attackers combine several malicious codes, the APIs for each type of malicious code are repeatedly used and there is a high possibility that the patterns and names of the APIs are similar. For this reason, this paper proposes a classification technique that finds patterns of APIs frequently used in malicious code, calculates the meaning and similarity of APIs, and determines the level of risk.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.719-734
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• 2020

This paper proposes a new measurement scheme for estimating the processing level according to risk when performing de-identification in the use of personal information by practitioners in the organization in line with the recently revised Data 3 Act. Our proposed methods considered the surrounding circumstances surrounding the data, not just the data, for risk measurement, and divided the data situation into three categories more systematically so that it can be applied in all areas in a general-purpose environment, the data utilization environment, and the data (self) so that it can be calculated quantitatively based on each context risk according to the presented classification. The proposed method is designed to calculate the risk of existing de-identifiable information in a quantitative manner so that personal information controller in general organizations can use it in practice, not just in the qualitative judgment of experts.

• Journal of the Korean Society of Safety
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• v.33 no.1
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• pp.148-161
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• 2018

A traffic fatality by young people marked average annual decrease of 4.5% since 2011. Meanwhile, a traffic fatality by senior over 65 years old marked average annual increase of 7.9% for the last five years which means that the annual increase of traffic fatality by senior will be a serious problem. This study started questioning that senior drivers over 65 years old did not retain the same causal factor of fatal traffic accidents and thus extensively analyzed a risk of it by age group quantitatively, dividing the senior driver group into the early, middle and latter stages. Depending on the aging level, the risk of traffic fatality showed a wide difference in seven different types of traffic accidents generally, and happened to increase with latter and middle parts of the senior driver more than the early part. Therefore, this study proposes four policy suggestions: 1) The senior driver need to be offered customized driving educations and the improvement of road environment is also recommended. 2) Political assistance is needed to support and guide a safety related technology installation for the new or existing car. 3) Renewal of driving license and an aptitude test(physical examination, cognitive test) for drivers over 75 years old should take in a less than 3 years and an additional road test is needed as occasion demands. 4) Like the United States and Europe, development and extension of customized treatment guidebook for medical teams who examine senior drivers is needed and establishment of education and administration system that a supervisor of driving license renewal can impose safety restriction and American anonymity reporting system is considered to institutionalize in the medium to longer term.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.169-181
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• 2016

Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.

• Environmental Analysis Health and Toxicology
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• v.22 no.1 s.56
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• pp.91-101
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• 2007

The new chemicals are developed and circulated without the verified toxicity data. So, the accidents and occupational diseases, such as explosion, fire, suffocation about deadly poisons etc. are frequently to workers. Classifications of chemicals suited with guideline and an offer of correct chemical information data are the molt important thing for the establishment of suitable chemical management system. The GHS (Globally Harmonized System of classification and labeling of chemicals) is based with the chemical classifications and unification plan. The warning symbol and phrases are established for improvements of chemical information data system. According to these unified and improved systematic form of data, and the chemical information data, the workplaces will be presented many chemical safety and risk data correctly. In this paper, we will present constructions and accomplishment contents-based chemical management of workplace through development of chemical information data and the nice using for new chemical investigation and risk assessment of chemicals in workplaces.

• Toxicological Research
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• v.17
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• pp.37-39
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• 2001

As the preliminary data collection for further chemical risk assessment. toxicants rating works is now rather extensively implemented in China. It consists of two parts, ie., rating of the hazard level of the exposed toxicant and that of the toxicant's profession. In the first part, the rating are based on six criteria, ie., acute toxicity, incidence of acute poisoning, prevalence of chronic poisoning, consequence of chronic poisoning, carcinogenecity and MAC level. Four hazardous levels are to be classified as extreme, high, medium, mild. In the second part. three determinants as weighted coefficients are taken into account, ie., toxicant's hazard level. exposure time and folds of MAC surpassing. Eventually, the index of classification C by which the work with toxic hazard can be classified is able to be calculated and assessed. Several comments were discussed and new recommendations were demonstrated.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.236-238
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• 2015

According to the change of maritime environments and the development of science and technologies unidentified risk factors are created day by day in the world. It is crucial reason for the occurrence of maritime accidents that the unidentified risk factors make unexpected and extra-ordinary cases. Thus prior identification of unidentified risk factors is key issues to prevent maritime accidents. In addition to the identification of risk factors, the identification and classification of ongoing risk factors and evaluation model in the future is also one of key issues to consider unidentified maritime accidents. In this study, we searched on the conventional risks and unidentified risks to find risk control options for new, unidentified and expected risks.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.