• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.228-234
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• 2021

Real estate market participants need to have a sense of predicting real estate prices in decision-making. Commonly used methodologies, such as regression analysis, ARIMA, and VAR, have limitations in predicting the value of an asset, which fluctuates due to unknown variables. Therefore, to mitigate the limitations, an artificial neural was is used to predict the price trend of apartments in Seoul, the hottest real estate market in South Korea. For artificial neural network learning, the learning model is designed with 12 variables, which are divided into macro and micro factors. The study was conducted in three ways: (Ed note: What is the difference between case 1 and 2? Is case 1 micro factors?)CASE1 with macro factors, CASE2 with macro factors, and CASE3 with the combination of both factors. As a result, CASE1 and CASE2 show 87.5% predictive accuracy during the two-year experiment, and CASE3 shows 95.8%. This study defines various factors affecting apartment prices in macro and microscopic terms. The study also proposes an artificial network technique in predicting the price trend of apartments and analyzes its effectiveness. Therefore, it is expected that the recently developed learning technique can be applied to the real estate industry, enabling more efficient decision-making by market participants.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Mycobiology
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• v.49 no.4
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• pp.406-420
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• 2021

Gloeostereum incarnatum has edible and medicinal value and was first cultivated and domesticated in China. We sequenced the G. incarnatum monokaryotic strain GiC-126 on an Illumina HiSeq X Ten system and obtained a 34.52-Mb genome assembly sequence that encoded 16,895 predicted genes. We combined the GiC-126 genome with the published genome of G. incarnatum strain CCMJ2665 to construct a genetic linkage map (GiC-126 genome) that had 10 linkage groups (LGs), and the 15 assembly sequences of CCMJ2665 were integrated into 8 LGs. We identified 1912 simple sequence repeat (SSR) loci and detected 700 genes containing 768 SSRs in the genome; 65 and 100 of them were annotated with gene ontology (GO) terms and KEGG pathways, respectively. Carbohydrate-active enzymes (CAZymes) were identified in 20 fungal genomes and annotated; among them, 144 CAZymes were annotated in the GiC-126 genome. The A mating-type locus (MAT-A) of G. incarnatum was located on scaffold885 at 38.9 cM of LG1 and was flanked by two homeodomain (HD1) genes, mip and beta-fg. Fourteen segregation distortion markers were detected in the genetic linkage map, all of which were skewed toward the parent GiC-126. They formed three segregation distortion regions (SDR1-SDR3), and 22 predictive genes were found in scaffold1920 where three segregation distortion markers were located in SDR1. In this study, we corrected and updated the genomic information of G. incarnatum. Our results will provide a theoretical basis for fine gene mapping, functional gene cloning, and genetic breeding the follow-up of G. incarnatum.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.568-578
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• 2021

Patents that are research results from industry-university cooperation (IUC) are a source of innovation, and play an important role in economic growth, such as technology transfer and commercialization. For this reason, there are many efforts to revitalize IUC, but in general, company patents are achievements that can be commercialized, rather than research achievements, so not all patents are used for business, even after their creation as the outcome of IUC. Therefore, this research supports the design of measures in which IUC can ultimately be linked to successful utilization of patents by identifying the purposes of IUC, even after it has been successfully promoted, and patents have been filed as a result. To this end, first, the patents registered for industry-academia cooperation in the United States are collected, and second, a predictive model is designed, with unexpired and expired patents predicted using machine learning techniques. The final identified patents are intended to derive available factors in terms of marketability and technicality. This study is expected to help predict the utilization of unexpired and expired patents, and is expected to contribute to setting goals for research results from technical cooperation between corporate and university officials planning early IUC.

• 한국사회정책
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• v.23 no.3
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• pp.49-77
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• 2016

The aim of this study is to reveal errors in the estimations on the nonstandard workers scale which have resulted from little consideration for bogus self-employment. Bogus self-employment means disguised employment relationships that are considered to be self-employed workers even though they have inherent subordination. The methods of previous studies estimate that the bogus self-employed workers are excluded from estimations because they are not workers in principle. We analyze the scale of the bogus self-employments using the empirical data [the Korean Welfare Panel of 2008-2013(six wave panel)] to re-estimate the scale of non-standard workers. In 2013, the year of the last wave, the percentage of the bogus self-employment was high, the proportion of them is 28.5% among self-employment without owning a place of business. Our results expect the number of contract workers from re-analysis would be over 740 thousand when reflecting on the predictive value in the actual number of paid workers. In conclusion, it is necessary to apply reasonable methods of analysis concerning the scope and scale of the nonstandard workers for the sake of coping with measures against the problem of nonstandard workers. The aim of this study is to reveal the estimation error on nonstandard workers' scale which are resulted from having no consideration for bogus self-employment. Bogus self-employment means disguised employment relationship that is considered as self-employment workers even though they have inherent subordination. The methods of previous researches' estimation on nonstandard workers have the problem that the bogus self-employment workers are excluded from estimation because they are not workers in principle.

• Journal of The Korean Society of Clinical Toxicology
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• v.18 no.2
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• pp.66-77
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• 2020

Purpose: The aims of the present study were twofold. First, the research investigated the effect of an individual's risk factors and the prevalence of psychotropic drugs on QTc prolongation, TdP (torsades de pointes), and death. Second, the study compared the risk scoring systems (the Mayo Pro-QT risk score and the Tisadale risk score) on QTc prolongation. Methods: The medical records of intoxicated patients who visited the emergency department between March 2010 and February 2019 were reviewed retrospectively. Among 733 patients, the present study included 426 psychotropic drug-intoxicated patients. The patients were categorized according to the QTc value. The known risk factors of QTc prolongation were examined, and the Mayo Pro-QT risk score and the Tisadale risk score were calculated. The analysis was performed using multiple logistic regression, Spearman correlation, and ROC (receiver operating characteristic). Results: The numbers in the mild to moderate group (male: 470≤QTc<500 ms, female: 480≤QTc<500 ms) and severe group (QTc≥500 ms or increase of QTc at least 60ms from baseline, both sex) were 68 and 95, respectively. TdP did not occur, and the only cause of death was aspiration pneumonia. The statically significant risk factors were multidrug intoxications of TCA (tricyclic antidepressant), atypical antipsychotics, an atypical antidepressant, panic disorder, and hypokalemia. The Tisadale risk score was larger than the Mayo Pro-QT risk score. Conclusion: Multiple psychotropic drugs intoxication (TCA, an atypical antidepressant, and atypical antipsychotics), panic disorder, and hypokalemia have been proven to be the main risk factors of QTc prolongation, which require enhanced attention. The present study showed that the Tisadale score had a stronger correlation and predictive accuracy for QTc prolongation than the Mayo Pro-QT score. As a result, the Tisadale risk score is a crucial assessment tool for psychotropic drug-intoxicated patients in a clinical setting.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.