• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.23-31
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• 2022

This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

• Journal of Veterinary Science
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• v.23 no.3
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• pp.41.1-41.15
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• 2022

Background: Proliferative enteritis caused by Lawsonia intracellularis undermines the economic stability of the swine industry worldwide. The development of cost-effective animal models to study the pathophysiology of the disease will help develop strategies to counter this bacterium. Objectives: This study focused on establishing a model of gastrointestinal (GI) infection of L. intracellularis in C57BL/6 mice to evaluate the disease progression and lesions of proliferative enteropathy (PE) in murine GI tissue. Methods: We assessed the murine mucosal and cell-mediated immune responses generated in response to inoculation with L. intracellularis. Results: The mice developed characteristic lesions of the disease and shed L. intracellularis in the feces following oral inoculation with 5 × 10⁷ bacteria. An increase in L. intracellularis 16s rRNA and groEL copies in the intestine of infected mice indicated intestinal dissemination of the bacteria. The C57BL/6 mice appeared capable of modulating humoral and cell-mediated immune responses to L. intracellularis infection. Notably, the expression of genes for the vitamin B12 receptor and for secreted and membrane-bound mucins were downregulated in L. intracellularis -infected mice. Furthermore, L. intracellularis colonization of the mouse intestine was confirmed by the immunohistochemistry and western blot analyses. Conclusions: This is the first study demonstrating the contributions of bacterial chaperonin and host nutrient genes to PE using an immunocompetent mouse model. This mouse infection model may serve as a platform from which to study L. intracellularis infection and develop potential vaccination and therapeutic strategies to treat PE.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.73-82
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• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.667-684
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• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.53-61
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• 2022

This study evaluated the impacts of subsurface drainage design, i.e., spacing and depth, on agricultural productivity and environmental sustainability in two tile-drained fields (Sites A and E) under a corn-soybean rotation in the Midwestern United States. A calibrated and validated Root Zone Water Quality Model (RZWQM) was used to simulate Nitrate-N (nitrogen) losses to tile drainage and crop yields of 30 tile spacing and depth scenarios over 24 years (1992-2015). Our results presented that the narrower and deeper the tile drains are placed, the greater corn yield and Nitrate-N losses, indicating that the subsurface drainage design may cause a trade-off between agricultural productivity and environmental sustainability. The simulation results also presented that up to about 255.7% and 628.0% increase in Nitrate-N losses in Sites A and E, respectively, far outweigh the rate of increase in corn yield up to about 1.1% and 1.6% from the adjustment of tile spacing and depth. Meanwhile, the crop yield and Nitrate-N losses according to the tile configuration differed depending on the field, and the soybean yield presented inconsistent simulation results, unlike the corn yield, which together demonstrate the heterogeneous characteristic of agro-environmental systems to a subsurface drainage practice. This study demonstrates the applicability of agricultural systems models in exploring agro-environmental responses to subsurface drainage practices, which can help guide the introduction and installation of tile systems into farmlands, e.g., orchards and paddy fields, in our country.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.97-105
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• 2022

This paper explains trends in security technologies for ICS. Since ICS is usually applied to large-scale national main infrastructures and industry fields, minor errors caused by cyberattack could generate enormous economic cost. ICS has different characteristic with commonly used IT systems, so considering security threats of ICS separately with IT is needed for developing modern security technology. This paper introduce framework for ICS that analyzes recent cyberattack tactics & techniques and find out trends in Intrusion Detection System (IDS) which is representative technology for ICS security, and analyzes AI technologies used for IDS. Specifically, this paper explains data collection and analysis for applying AI techniques, AI models, techniques for evaluating AI Model.

• Journal of Korean Biological Nursing Science
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• v.24 no.1
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• pp.17-25
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• 2022

Purpose: To determine the prognostic role of muscle area and muscle radiation attenuation in the erector spinae (ES) and multifidus (MF) muscles in patients undergoing gastrectomy. Methods: Patients with stage I-III gastric cancer undergoing gastrectomy were retrospectively enrolled in this study. Clinicopathologic characteristics were collected and analyzed. Both paraspinal muscle index of ES/MF muscles (PMI_EM) and paraspinal muscle radiation attenuation in the same muscles (PMRA_EM) were analyzed at the 3rd lumbar level using axial computed tomographic images. Cox regression analysis was applied to estimate overall survival (OS) and disease-free survival (DFS). Results: There was only a weak correlation between PMI_EM and PMRA_EM (r= 0.28). Multivariate Cox regression revealed that PMRA_EM, but not PMI_EM, was an important determinant of survival. PMRA_EM along with age, tumor-node-metastasis (TNM) stage, perineural invasion, and serum albumin level were significant determinants of both OS and DFS that constituted Model 1. Harrell's concordance index and integrated area under receiver operating characteristic curve were greater for Model 1 than for Model 2 (consisting of the same covariates as Model 1 except PMRA_EM) or Model 3 (consisting of only TNM stage). Conclusion: PMRA_EM, but not PMIEM, was an important determinant of survival. Because there was only a weak correlation between PMI_EM and PMRA_EM in this study, it was presumed that they were mutually exclusive. Model 1 consisting of age, TNM stage, perineural invasion, serum albumin level, and PMRA_EM was greater than nested models (i.e., Model 2 or Model 3) in predicting survival outcomes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.123-130
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• 2022

Recently, studies on the detection and classification of Android malware based on API Call sequence have been actively carried out. However, API Call sequence based malware classification has serious limitations such as excessive time and resource consumption in terms of malware analysis and learning model construction due to the vast amount of data and high-dimensional characteristic of features. In this study, we analyzed various classification models such as LightGBM, Random Forest, and k-Nearest Neighbors after significantly reducing the dimension of features using PCA(Principal Component Analysis) for CICAndMal2020 dataset containing vast API Call information. The experimental result shows that PCA significantly reduces the dimension of features while maintaining the characteristics of the original data and achieves efficient malware classification performance. Both binary classification and multi-class classification achieve higher levels of accuracy than previous studies, even if the data characteristics were reduced to less than 1% of the total size.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.115-121
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• 2022

In this paper, we analyze trends in the use of mobile forensic technology, focusing on cases where mobile forensics are used, and we predict the development of future mobile forensics technology using linear regression used in future prediction models. For the current status and outlook analysis, we extracted a total of 8 variables by analyzing 1,397 domestic and foreign mobile forensics-related cases and newspaper articles. We analyzed the prospects for each variable using the year of occurrence as an independent variable, seven variables such as text (text message usage information), communication information (cell phone communication information), Internet usage information, messenger usage information, stored files, GPS, and others as dependent variables. As a result of the analysis, among various aspects of the use of mobile devices, the use of Internet usage information, messenger usage information, and data stored in mobile devices is expected to increase. Therefore, it is expected that continuous research on technologies that can effectively extract and analyze characteristic information of mobile devices such as file systems, the Internet, and messengers will be needed As mobile devices increase performance and utilization in the future and security technology.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.