• Mycobiology
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• v.46 no.4
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• pp.421-428
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• 2018

The white button mushroom (Agaricus bisporus) is one of the most widely cultivated species of edible mushroom. Despite its economic importance, relatively little is known about the genetic diversity of this species. Illumina paired-end sequencing produced 43,871,558 clean reads and 69,174 contigs were generated from five offspring. These contigs were subsequently assembled into 57,594 unigenes. The unigenes were annotated with reference genome in which 6,559 unigenes were associated with clusters, indicating orthologous genes. Gene ontology classification assigned many unigenes. Based on genome data of the five offspring, 44 polymorphic simple sequence repeat (SSR) markers were developed. The major allele frequency ranged from 0.42 to 0.92. The number of genotypes and the number of alleles ranged from 1 to 4, and from 2 to 4, respectively. The observed heterozygosity and the expected heterozygosity ranged from 0.00 to 1.00, and from 0.15 to 0.64, respectively. The polymorphic information content value ranged from 0.14 to 0.57. The genetic distances and UPGMA clustering discriminated offspring strains. The SSR markers developed in this study can be applied in polymorphism analyses of button mushroom and for cultivar discrimination.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.617-623
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• 2019

Numerous water disasters have recently occurred all over the world, including South Korea, due to global climate change in recent years. As water-related disasters occur extensively and their sites are difficult for people to access, it is necessary to monitor them using satellites. The Ministry of Environment and K-water plan to launch the next-generation medium satellite No. 5 (water resource/water disaster satellite) equipped with C-band synthetic aperture radar (SAR) in 2025. C-band SAR has the advantage of being able to observe water resources twice a day at a high resolution both day and night, regardless of weather conditions. Currently, RADARSAT-2 and Sentinel-1 equipped with C-band SAR achieve the purpose of their launch and are used in various environmental fields such as forest structure detection and coastline change monitoring, as well as for unique purposes including the detection of flooding, drought and soil moisture change, utilizing the advantages of SAR. As such, this study aimed to analyze the characteristics of the next-generation medium satellite No. 5 and its application in environmental fields. Our findings showed that it can be used to improve the degree of precision of existing environmental spatial information such as the classification accuracy of land cover map in environmental field works. It also enables us to observe forests and water resources in North Korea that are difficult to access geographically. It is ultimately expected that this will enable the monitoring of the whole Korean Peninsula in various environmental fields, and help in relevant responses and policy supports.

• Convergence Security Journal
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• v.19 no.1
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• pp.11-17
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• 2019

Recently, the abuse of Internet technology has caused economic and mental harm to society as a whole. Especially, malicious code that is newly created or modified is used as a basic means of various application hacking and cyber security threats by bypassing the existing information protection system. However, research on small-capacity executable files that occupy a large portion of actual malicious code is rather limited. In this paper, we propose a model that can analyze the characteristics of known small capacity executable files by using data mining techniques and to use them for detecting unknown malicious codes. Data mining analysis techniques were performed in various ways such as Naive Bayesian, SVM, decision tree, random forest, artificial neural network, and the accuracy was compared according to the detection level of virustotal. As a result, more than 80% classification accuracy was verified for 34,646 analysis files.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.455-464
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• 2018

Objective: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients. Methods: We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs. Results: Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P<0.001). Conclusion: Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.933-940
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• 2020

Land-use change matrix data is important for calculating the LULUCF (land use, land use change and forestry) sector of the national greenhouse gas inventory. In this study, land cover changes in 2004 and 2019 were compared using the Wall-to-Wall technique with a land cover map of Sejong City from the Ministry of Environment. Sejong City was classified into six land use classes according to the Intergovernmental Panel on Climate Change (IPCC) guidelines: Forest land, crop land, grassland, wetland, settlement and other land. The coordinate system of the land cover maps of 2004 and 2019 were harmonized and the land use was reclassified. The results indicate that during the 15 years from 2004 to 2019 forestlands and croplands decreased from 50.4% (234.2 ㎢) and 34.6% (161.0 ㎢) to 43.4% (201.7 ㎢) and 20.7% (96.2 ㎢), respectively, while Settlement and Other land area increased significantly from 8.9% (41.1 ㎢) and 1.4% (6.9 ㎢) to 35.6% (119.0 ㎢) and 6.5% (30.3 ㎢). 79.㎢ of cropland area (96.2 ㎢) in 2019 was maintained as cropland, and 8.8 ㎢, 1.7 ㎢, 0.5 ㎢, 5.4 ㎢, and 0.4 ㎢ were converted from forestland, grassland, wetland, and settlement, respectively. This research, however, is subject to several limitations. The uncertainty of the land use change matrix when using the wall-to-wall technique depends on the accuracy of the utilized land cover map. Also, the land cover maps have different resolutions and different classification criteria for each production period. Despite these limitations, creating a land use change matrix using the Wall-to-Wall technique with a Land cover map has great advantages of saving time and money.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Journal of People, Plants, and Environment
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• v.24 no.1
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• pp.95-105
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• 2021

Background and objective: A biosphere reserve is a complex concept that combines the preservation of biodiversity and the sustainable development with the region, outstanding ecosystems with worth conserving in worldwide. The purpose of this study is to suggest the way of zoning that can perform the functions of conservation, development, and logistics support of biosphere reserves. Methods: To meet the purpose of this study, the designation criteria, restrictions, and permissions of the protected area specified in the law for domestic protected areas were reviewed to classify the functions of a biosphere reserve. Results: Through this classification, 10 domestic protected areas with high ecological protection value, such as the natural beauty of the ecosystem, biodiversity, and habitats for wild animals and plants were derived as the core areas of the biosphere reserves. Also, a total of 21 protected zones that can function as a buffer to protect the core of the natural ecosystem from indiscriminate development such as resource protection, recovery, pollution prevention, and improvement were derived as appropriate sites for a buffer. In the review process, issues such as different behavioral restrictions and ranges of permission due to the application of different laws were identified, if two or more protected areas exist within one of the protected areas, there is a protected area that does not meet the criteria for designating use zone, or where behavior restrictions do not meet the zoning criteria of biosphere reserve, under the laws of domestic protected areas. Conclusion: Although this study was not able to carefully review most of the laws on domestic protected areas that are linked to other laws, it was able to categorize appropriate domestic protected areas that can act as the core and buffer zones of biosphere reserves.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.