• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.1
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• pp.17-30
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• 2022

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

• Journal of Integrative Natural Science
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• v.6 no.2
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• pp.100-103
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• 2013

Using computational approaches we can dock small molecules into the structures of Macromolecular targets and then score their potential complementarity to binding sites is widely used in hit identification and lead optimization techniques. This review seeks to provide the application of docking in structure-based drug design (binding mode prediction, Lead Identification and Lead optimization), and also discussed how to manage errors in docking methodology in order to overcome certain limitations of docking and scoring algorithm.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Ecology and Environment
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• v.46 no.4
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• pp.316-323
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• 2022

Background: We developed a habitat suitability index (HSI) model for Pedicularis hallaisanensis, a Grade II Endangered Species in South Korea. To determine the habitat variables, we conducted a literature review on P. hallaisanensis with a specific focus on the associated spatial factors, climate, topography, threats, and soil factors to derive five environmental factors that influence P. hallaisanensis habitats. The specific variables were defined based on the collected data and consultations with experts in the field, with the validity of each variable tested through field studies. Results: Mt. Seorak had a suitable habitat area of 2.48 km² for sites with a score of 1 (0.62% of total area) and 0.01 km² for sites with a score of 0.9. Mt. Bangtae had a suitable habitat area of 0.03 km² for sites with a score of 1 (0.02% of total area) and 0 km² for sites with a score of 0.9. Mt. Gaya showed 0.13 km² of suitable habitat for sites with a score of 1 (0.17% of total area) and 0 km² for sites with a score of 0.9. Lastly, Mt. Halla showed 3.12 km² of suitable habitat related to sites with a score of 1 (2.04% of total area) and 4.08 km² of sites with a score of 0.9 (2.66% of total area). Mt. Halla accounts for 73.1% of the total core habitat area. Considering the climatic, soil, and forest conditions together with standardized collection sites, our results indicate that Mt. Halla should be viewed as a core habitat of P. hallaisanensis. Conclusions: The findings in this study provide useful data for the identification of core habitat areas and potential alternative habitats to prevent the extinction of the endangered species, P. hallaisanensis. Furthermore, the developed HSI model allows for the prediction of suitable habitats based on the ecological niche of a given species to identify its unique distribution and causal factors.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.109-118
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• 2022

The purpose of this study is to identify trends in the application of artificial intelligence by analyzing upper extremity movement assessment and artificial intelligence convergence research using a systematic literature review method. The research was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Among the 380 articles searched in three databases, 8 articles were finally selected according to the selection and exclusion criteria. For the evaluation of upper extremity movement, motion performance evaluation, FMA, and ARAT were used. For quantification, data were extracted using various tools, and upper extremity movement classification, recovery prognosis prediction, and evaluation tool score were predicted using artificial intelligence. This study is meaningful in that it systematically reviewed studies that objectively evaluated upper extremity movement using artificial intelligence and identified the direction in which artificial intelligence is being applied. Based on this, the introduction of artificial intelligence technology in the assessment of upper extremity movements is expected to help objectively identify the intervention effect and the patient's recovery.

• Korean Journal of Agricultural Science
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• v.42 no.4
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• pp.397-406
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• 2015

Quantitative traits are mostly controlled by a large number of genes. Some of these genes tend to have a large effect on quantitative traits in cattle and are known as major genes primarily located at quantitative trait loci (QTL). The genetic merit of animals can be estimated by genomic selection, which uses genome-wide SNP panels and statistical methods that capture the effects of large numbers of SNPs simultaneously. In practice, the accuracy of genomic predictions will depend on the size and structure of reference and training population, the effective population size, the density of marker and the genetic architecture of the traits such as number of loci affecting the traits and distribution of their effects. In this review, we focus on the structure of Hanwoo reference and training population in terms of accuracy of genomic prediction and we then discuss of genetic architecture of intramuscular fat(IMF) and marbling score(MS) to estimate genomic breeding value in real small size of reference population.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1259-1269
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• 2015

Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site 'D' and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.333-341
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• 2011

To compare between calcium scoring CT (CSCT) and Low-dose CT (LDCT) image finding for coronary artery calcification (CAC) in screening of lung disease by MDCT. A total of 61 subjects who retired-workers exposed to inorganic dust were performed LDCT and CSCT by using a MDCT scanner on the same day, after be approved by the institutional review board, and obtaining the written informed consent from all subjects. LDCT images were read for detecting lung diseases as well as CAC by a experienced chest radiologist, then the subjects were divided either the positive group with CAC or the negative group without it. The CSCT was used to quantify and detect the presence of calcification in the coronary artery, and score of CAC calculated by using a Rapidia software (ver 2.8). In all coronary arteries, calcium score of positive group was higher better than that in negative group, especially in the total calcium (13.7 vs. 582.9, p=0.008) and the left anterior descending artery (3.2 vs. 249.0, p=0.006). CAC findings between CSCT and LDCT image were showed excellent agreement in cut-off point 100(K-value=0.80, 95% CI=0.69-0.91) from total calcium score. CAC findings on LDCT images showed the higher relation with CSCT. Therefore, the obstructive coronary artery disease could be predicted by CAC on LDCT images for screening of lung diseases.

• Information Systems Review
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• v.26 no.2
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• pp.169-183
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• 2024

This study summarizes improvement strategies for addressing the imbalance problem in observed default data that must be considered when constructing a default model and compares and analyzes the performance improvement effects using data resampling techniques and default threshold adjustments. Empirical analysis results indicate that as the level of imbalance resolution in the data increases, and as the default threshold of the model decreases, the recall of the model improves. Conversely, it was found that as the level of imbalance resolution in the data decreases, and as the default threshold of the model increases, the precision of the model improves. Additionally, focusing solely on either recall or precision when addressing the imbalance problem results in a phenomenon where the other performance evaluation metrics decrease significantly due to the trade-off relationship. This study differs from most previous research by focusing on the relationship between improvement strategies for the imbalance problem of default data and the enhancement of default model performance. Moreover, it is confirmed that to enhance the practical usability of the default model, different improvement strategies for the imbalance problem should be applied depending on the main purpose of the model, and there is a need to utilize the F_β Score as a performance evaluation metric.