• International Journal of Contents
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• v.7 no.4
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• pp.108-115
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• 2011

The current study aims to analyze the acceptance of International Standard Terminology (IST) related to herbs and formulas used in Korea. It also intends to examine limitations of each term source by linking texts for herbal medicine research and formula research used in schools of oriental medicine with medicinal substance-formula classification names within the IST framework. This study examined 64 medicinal classification names of IST, including synonyms, 41 formula classification names, 65 classification names of "Herbal Medicine Study," 89 medicinal classification names of "Shin's Clinical Herbal Medicine Study," and lastly 83 formula classification names of "Formula Study." Data on their chief virtue, efficacy and characteristics as medicinal substances were extracted from their definitions, and such data were used to perform Chinese character-English mapping using the IST. The outcomes of the mapping were then analyzed in terms of both lexical matching and semantic matching. In terms of classification names for medicinal substances, "Herbal Medicine Study" had 60.0% lexical matching, whereas "Shin's Clinical Herbal Medicine Study" had 48.3% lexical matching. When semantic matching was also applied, "Herbal Medicine Study" showed a value of 87.7% and "Shin's Clinical Herbal Medicine Study" 74.2%. In terms of formula classification names, lexical matching was 28.9% of 83 subjects, and when semantic matching was also considered, the value was 30.1%. When the conceptual elements of this study were applied, some IST terms that are classified with other codes were found to be conceptually consistent, and some terms were not accepted due to different depths in the classification systems of each source.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of Korean Medical classics
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• v.22 no.1
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• pp.191-205
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• 2009

Objective : Up to the present, theories of medical books is too difficult to understand thoroughly. However, these study methods have some problems in dealing with lots of meaning because the comprehension of theories are dependent upon one's memory. Especially, comparison distinct medical books are more difficult matter. So, we have attempted to solve a problem. Method : We have researched medical terms in the "Piweilun" according to below the procedure. (1) Making a terms list: We have selected constituent of sentence. And we have made term list on the basis of concept of term. (2) Making a synonym list: We have collected identical conception and made a synonym list. So, using an synonym tables of DB, it is possible to search for the non-standard terms of medical theory. (3) Making a classification system: Using UMLS(Unified Medical Language System), MeSH(Medical Subject Headings), IST(International Standard Terminology) ect., we have made a classification system of oriental medicine terms in the "Piwelun". Analysis of relation between terms. Result : In the "Piweilun", there are more than 1,790s concepts. Parts of those are belonged to UMLS-Semantic Type, the other parts of those are not belonged to UMLS-Semantic Type. And those include predicate more than UMLS-Semantic Relations.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.261-287
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• 2005

The purpose of this paper is to explore the ways to improve the performance of SVM (Support Vector Machines) text classifier using inter-document similarities. SVMs are powerful machine learning systems, which are considered as the state-of-the-art technique for automatic document classification. In this paper text categorization via SVMs approach based on feature representation with document vectors is suggested. In this approach, document vectors instead of index terms are used as features, and vector similarities instead of term weights are used as feature values. Experiments show that SVM classifier with document vector features can improve the document classification performance. For the sake of run-time efficiency, two methods are developed: One is to select document vector features, and the other is to use category centroid vector features instead. Experiments on these two methods show that we can get improved performance with small vector feature set than the performance of conventional methods with index term features.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1055-1062
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• 2023

Globally, the need for remanufacturing or reusing ships and various mechanical parts continues to increase due to environmental problems including global warming. Research on remanufacturing is being carried out in many areas. However, research on inspection and classification to identify the performance or degree of wear of mechanical parts is insufficient. In particular, studies on the inspection-ability and classification-ability of mechanical parts equipped with various materials and complex forms are highly required. Remanufacturing must be considered from the stage of design to extend the life cycle of mechanical parts. Particularly, it is very important to perform research for evaluating the degree of ease to inspect and classify various sorts of wear or deterioration of parts caused by long-term use easily. In this study, the degree of ease in inspecting or classifying mechanical parts for remanufacturing is defined as inspection-ability and classification-ability. In fact, to remanufacture old parts, inspection-ability and classification-ability should be reflected from the stage of design. The purpose of this study is to evaluate the inspection-ability and classification-ability of ships and various mechanical parts. This researcher has presented the quantitative evaluation procedure of inspection-ability and classification-ability, derived the factors and ranges that influence each of the details of easiness, assigned scores according to the ranges of the factors, and calculated weights. Lastly, this study presents the procedure of scoring to evaluate the overall weights of inspection-ability and classification-ability and also inspection-ability and classification-ability quantitatively.

• Journal of Korean Academy of Nursing Administration
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• v.18 no.4
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• pp.402-413
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• 2012

Purpose: This study was done to explore factors related to amount of service use for elders with long-term care needs. Methods: A descriptive-correlation design was used. The sample included 259 elders and their primary caregivers who had cared for the elders for at least 6 months. Data on long-term care need assessment, service use and interviews with primary caregivers were analyzed. Results: There was no significant relationship between the sociodemographic characteristics and the amount of services use. Amount of service use differed significantly by Long-term care classification. The mean scores for class 1, 2 and 3 were 22.68, 21.47 and 17.87 days respectively. Primary caregiver relationship with the elders and the number of family-friend helpers were also significant. Multivariate regression analysis showed that gender, marital status, activities of daily living, cognitive impairment, and secondary caregiver support explained 17% of the total variance of service use among these elders (F=3.50, p<.001). Conclusion: The results of this study indicate that critical factors including secondary caregiver support and individual background, and other functional dependencies except for physical function should be considered in accurately predicting the amount of service use for community dwelling elders with long-term care needs.

• Journal of Korean Academy of Nursing
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• v.40 no.3
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• pp.349-358
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• 2010

Purpose: The purpose of this study was to estimate nursing costs and to establish appropriate nursing fees for long-term care services for community elders. Methods: Seven nurses participated in data collection related to visiting time by nurses for 1,100 elders. Data on material costs and management costs were collected from 5 visiting nursing agencies. The nursing costs were classified into 3 groups based on the nurse's visit time under the current reimbursement system of long-term care insurance. Results: The average nursing cost per minute was 246 won. The material costs were 3,214 won, management costs, 10,707 won, transportation costs, 7,605 won, and capital costs, 5,635 won per visit. As a result, the average cost of nursing services per visit by classification of nursing time were 41,036 won (care time <30 min), 46,005 won (care time 30-59 min), and 57,321 won (care time over 60 min). Conclusion: The results of the study indicate that the fees for nurse visits currently being charged for long-term care insurance should be increased. Also these results will contribute to baseline data for establishing appropriate nursing fees for long-term care services to maintain quality nursing and management in visiting nursing agencies.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.2
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• pp.189-195
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• 2006

Purpose: This study was to identify nursing interventions which were perceived highly in the importance and performance of nursing interventions by nurses in long-term care facilities for elderly. Method: Data was collected from nurses working in long-term care facilities for elderly over 2 years who participated in continuous education by Korea Association of Senior Citizens Welfare Institution. Data was analyzed using mean and paired t-test to compare difference between the importance and performance of each nursing intervention. Results: Among 264 nursing intervention, 49 nursing interventions were considered highly important and performed very often by nurses. Especially, 11 nursing interventions had significant difference between the importance and the performance, which meant that nurses perceived them as the most important and they were not implemented often as much as that by nurses. Conclusion: The results of this study revealed that what kinds of the nursing interventions were perceived highly important and performed very often by nurses in long-term care facilities for elderly. These nursing interventions can be utilized in the development of standardized nursing intervention classification to be used for the long-term care facilities for elderly.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.