• 문화기술의 융합
• /
• 제6권3호
• /
• pp.457-462
• /
• 2020

골다공증은 주로 노인에서 나타나는 질병으로써 뼈 질량 및 조직의 구조적 악화에 따라 골절의 위험을 증가시킨다. 본 연구의 목적은 영양소 성분과 골다공증과의 연관성을 파악하고, 영양소 성분을 기반으로 골다공증을 예측하는 모델을 생성 및 평가하는 것이다. 실험방법으로 binary logistic regression을 이용하여 연관성분석을 수행하였고, naive Bayes 알고리즘과 variable subset selection 메소드를 이용하여 예측 모델을 생성하였다. 단일 변수들에 대한 분석결과는 남성에서 식품섭취량과 비타민 B2가 골다공증을 예측하는데 가장 높은 the area under the receiver operating characteristic curve (AUC)값을 나타내었다. 여성에서는 단일불포화지방산이 가장 높은 AUC값을 나타내었다. 여성 골다공증 예측모델에서는 Correlation based feature subset 및 wrapper 기반 feature subset 메소드를 이용하여 생성된 모델이 0.662의 AUC 값을 얻었다. 남성에서 전체변수를 이용한 모델은 0.626의 AUC를 얻었고, 그외 남성 모델들에서는 민감도와 1-특이도에서 예측 성능이 매우 낮았다. 이러한 연구결과는 향후 골다공증 치료 및 예방을 위한 기반정보로 활용할수 있을 것으로 기대된다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제10권3호
• /
• pp.91-98
• /
• 2021

오늘날 정보통신 기술이 급격하게 발달하면서 IT 인프라에서 보안의 중요성이 높아졌고 동시에 지능형 지속 공격(Advanced Persistent Threat)처럼 고도화되고 다양한 형태의 사이버 공격이 증가하고 있다. 점점 더 고도화되는 사이버 공격을 조기에 방어하거나 예측하는 것은 매우 중요한 사안으로, NIDS(Network-based Intrusion Detection System) 관련 데이터 분석만으로는 빠르게 변형하는 사이버 공격을 방어하지 못하는 경우가 많이 보고되고 있다. 따라서 현재는 HIDS(Host-based Intrusion Detection System) 데이터 분석을 통해서 위와 같은 사이버 공격을 방어하는데 침입 탐지 시스템에서 생성된 데이터를 이용하고 있다. 본 논문에서는 기존에 사용되었던 데이터 세트에서 결여된 스레드 정보, 메타 데이터 및 버퍼 데이터를 포함한 LID-DS(Leipzig Intrusion Detection-Data Set) 호스트 기반 침입 탐지 데이터를 이용하여 기계학습 알고리즘에 관한 비교 연구를 진행했다. 사용한 알고리즘은 Decision Tree, Naive Bayes, MLP(Multi-Layer Perceptron), Logistic Regression, LSTM(Long Short-Term Memory model), RNN(Recurrent Neural Network)을 사용했다. 평가를 위해 Accuracy, Precision, Recall, F1-Score 지표와 오류율을 측정했다. 그 결과 LSTM 알고리즘의 정확성이 가장 높았다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권4호
• /
• pp.1887-1898
• /
• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권8호
• /
• pp.3725-3748
• /
• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.202-209
• /
• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.71-78
• /
• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• 한국운동역학회지
• /
• 제32권1호
• /
• pp.1-8
• /
• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Asian Pacific Journal of Cancer Prevention
• /
• 제17권2호
• /
• pp.835-838
• /
• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• 대한통합의학회지
• /
• 제12권2호
• /
• pp.155-166
• /
• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• 디지털콘텐츠학회 논문지
• /
• 제19권4호
• /
• pp.685-692
• /
• 2018

본 연구는 통계분석을 이용한 중년 성인의 고혈압 예측모델 개발이 목적이다. 국민건강영양조사자료(2013년-2016년)를 사용하여 통계분석과 예측모델을 개발하였다. 이진 로지스틱 회귀분석으로 통계적 유의한 고혈압 위험인자를 제시하였으며, Wrapper 변수선택기법을 적용한 로지스틱회귀와 나이브베이즈 알고리즘을 이용하여 예측모델을 개발하였다. 통계분석에서 고혈압에 가장 높은 연관성을 갖는 인자는 남성에서 WHtR (p<0.0001, OR = 2.0242), 여성에서 AGE(p<0.0001, OR = 3.9185)로 나타났다. 예측모델의 성능평가에서, 로지스틱 회귀 모델이 남성(AUC = 0.782)과 여성(AUC = 0.858)에서 가장 좋은 예측력을 보였다. 우리의 연구 결과는 고혈압에 대한 대규모 스크리링 도구를 개발하는데 중요한 정보를 제공하며, 고혈압 연구에 대한 기반정보로 활용할 수 있다.