• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.202-209
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• 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.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.569-572
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• 2008

The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bavesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bavesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Journal of Radiation Industry
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• v.18 no.3
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• pp.173-176
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• 2024

In this paper, general characteristics, blood tests, and ultrasound examination results were used to classify the presence of polycystic ovary syndrome (PCOS). The classification algorithms used were SVM (Support Vector Machine) and k-NN (k-Nearest Neighbors). Out of a total of 300 data samples, 210 were used as training data and 90 as test data. The results showed that SVM achieved higher accuracy compared to k-NN, confirming its greater utility in diagnosing the presence of PCOS. Future research is expected to improve classification performance by incorporating various additional indicators and securing more data. Additionally, it is expected to serve as a foundational resource for predicting and classifying other diseases.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.571-578
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• 2018

Recently, deep learning techniques have shown superior performance in various kinds of pattern recognition. However, there have been some arguments whether the DNN performs better than the conventional machine learning techniques when classification experiments are done using a small amount of training data. In this study, we compared the performance of the conventional GMM and SVM with DNN, a kind of deep learning techniques, in audio event detection. When tested on the same data, DNN has shown superior overall performance but SVM was better than DNN in segment-based F-score.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.