• Journal of Rhinology
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• v.59 no.2
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• pp.173-180
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• 2021

Background: Neutrophils present as major inflammatory cells in refractory chronic rhinosinusitis with nasal polyps (CRSwNP), regardless of the endotype. However, their role in the pathophysiology of CRSwNP remains poorly understood. We investigated factors predicting the surgical outcomes of CRSwNP patients with focus on neutrophilic localization. Methods: We employed machine-learning methods such as the decision tree and random forest models to predict the surgical outcomes of CRSwNP. Immunofluorescence analysis was conducted to detect human neutrophil elastase (HNE), Bcl-2, and Ki-67 in NP tissues. We counted the immunofluorescence-positive cells and divided them into three groups based on the infiltrated area, namely, epithelial, subepithelial, and perivascular groups. Results: On machine learning, the decision tree algorithm demonstrated that the number of subepithelial HNE-positive cells, Lund-Mackay (LM) scores, and endotype (eosinophilic or non-eosinophilic) were the most important predictors of surgical outcomes in CRSwNP patients. Additionally, the random forest algorithm showed that, after ranking the mean decrease in the Gini index or the accuracy of each factor, the top three ranking factors associated with surgical outcomes were the LM score, age, and number of subepithelial HNE-positive cells. In terms of cellular proliferation, immunofluorescence analysis revealed that Ki-67/HNE-double positive and Bcl-2/HNE-double positive cells were significantly increased in the subepithelial area in refractory CRSwNP. Conclusion: Our machine-learning approach and immunofluorescence analysis demonstrated that subepithelial neutrophils in NP tissues had a high expression of Ki-67 and could serve as a cellular biomarker for predicting surgical outcomes in CRSwNP patients.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.131-136
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• 2023

Local youth outmigration is increasingly growing. Various studies are being conducted to identify the factors contributing to this problem, but there is a lack of research analyzing each region individually. Therefore, this study aims to analyze the factors influencing youth outmigration in Busan and predict the risk levels of youth population outflow using machine learning techniques. By utilizing district-level data collected from the KOSIS, we divided the population into three groups based on age (the early 20s, late 20s, and early 30s) and employed Decision Tree and Random Forest algorithms to classify and predict the risk levels of youth population outmigration. The results indicate that the predictive model for youth outmigration risk levels achieves the highest accuracies of 0.93, 0.75, and 0.63 for each age group, respectively.

• The Journal of Information Systems
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• v.33 no.3
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• pp.187-205
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• 2024

Purpose A prediction model for occupational injuries can support more proactive, efficient, and effective policy-making. This study aims to develop a model that predicts the severity of occupational injuries, classified into 15 disability grades in South Korea, using machine learning techniques applied to COMWEL data. The primary goal is to improve prediction accuracy, offering an advanced tool for early intervention and evidence-based policy implementation. Design/methodology/approach The data analyzed in this study consists of 290,157 administrative records of occupational injury cases collected between 2018 and 2020 by the Korea Workers' Compensation & Welfare Service, based on the 'Workers' Compensation Insurance Application Form' submitted for occupational injury treatment. Four machine learning models - Decision Tree, DNN, XGBoost, and LightGBM - were developed and their performances compared to identify the optimal model. Additionally, the Permutation Feature Importance (PFI) method was used to assess the relative contribution of each variable to the model's performance, helping to identify key variables. Findings The DNN algorithm achieved the lowest Mean Absolute Error (MAE) of 0.7276. Key variables for predicting disability grades included the severity index, primary disease code, primary disease site, age at the time of the injury, and industry type. These findings highlight the importance of early policy intervention and emphasize the role of both medical and socioeconomic factors in model predictions. The academic and policy implications of these results were also discussed.

• Fashion & Textile Research Journal
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• v.9 no.5
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• pp.538-544
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• 2007

We usually try to accomodate ourselves to our nature surroundings interacting with nature. There fore I've decided to apply nature materials, esp. the pine to Hair Art as a way of expressing our feelings from Nature. 'Hair Art' is a follow-up study based on the various artistic point of view. There has been various works of 'Hair Art' with nature materials. However, it is rare to see a work of 'Hair Art' with the pine. That's why I've decided to study more about this. This study is a new experiment in 'Hair Art' against a conventional idea "Hair Art values practicability." Therefore, the primary goal of this study is to do much for the cause of learning in 'Hair Art' and encourages some development of 'Hair Art' industry. I've researched an image of a pine tree in art of Joseon Dynasty and contemporary art. Then I've tried to apply the image to a work of 'Hair Art'. First of all, in a view of expression technique for the symbolism of the pine, the artists in Joseon Dynasty drew pictures of a pine tree with a paintbrush but the modern artists make a new attempt from the thought of Modernism. We can find it in some photos. Next, to express traditional oriental idea such as 'unconventional and elegance', comtemporary artists chose the symbolism of the pine tree as an object of their works like pen and ink sketches from the thought of Modernism. Third, in a fusion style picture which contains features of both oriental paintings and western paintings and in a sexualism style picture that depicts a harmony of a male and a female as a shape of a pine, we can find colorful images of a pine tree and Their figurative beauty in art. Those are another symbols of the pine. In conclusion, the implication of the pine tree still hasn't changed even there are differences of drawings of pine tree in the past and the present. I've tried to combine these symbolic ideas of the pine with 'Hair Art' and made 5 hair styles. Throughout the process of researching this topic which is 'The Study of Hair Art Using The Symbolism of The Pine', I've realized that pine trees make it possible to express intrinsic tough spirit of human being and abundance in color and figurative beauty in art. I hope this can contribute to the field of 'Hair Art' and would become an important educational resource for further study.

• International Commerce and Information Review
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• v.13 no.4
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• pp.81-100
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• 2011

With the smart device deployment, the interest in NFC technology is increasing. In this study, to be successful in NFC based business commercialization, we present main factors affecting success of NFC based e-commerce business model. To this end, we conduct NFC and business models, case study methodology through literature review. And then, we suggest representative NFC e-commerce business model cases, and practices that affect the success or failure of the six factors are derived Derived factors are based on inductive learning to apply the technology to create a case study table, and decision trees to bring it, NFC-based commerce business models need to be successful at the strategic implications are present.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.531-539
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• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.386-390
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields. Classifying a group into subgroups is one of the most important subjects in data mining. Tree-based methods, known as decision trees, provide an efficient way to finding the classification model. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variable should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present some methods for measuring the node impurity, which are applicable to data sets with multivariate target variables. For illustration, a numerical cxample is given with discussion.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.988-992
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• 2009

In this paper, we attempted to extract binary relations between terminologies using composite kernels consisting of convolution parse tree kernels and WordNet verb synset vector kernels which explain the semantic relationships between two entities in a sentence. In order to evaluate the performance of our system, we used three domain specific test collections. The experimental results demonstrate the superiority of our system in all the targeted collection. Especially, the increase in the effectiveness on KREC 2008, 8% in F1, shows that the core contexts around the entities play an important role in boosting the entire performance of relation extraction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.