• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.347-353
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• 2019

Recent recommendation systems are developing toward the utilization of high-dimensional data. However, high-dimensional data can increase algorithm complexity by expanding dimensions and be lower the accuracy of recommended items. In addition, it can cause the problem of data sparsity and make it difficult to provide users with proper recommended items. This study proposed an algorithm that classify users' subjective data with objective criteria with fuzzy-AHP and make use of rules with repetitive patterns through fuzzy association rules. Trying to check how problems with high-dimensional data would be mitigated by the algorithm, we performed 5-fold cross validation according to the changing number of users. The results show that the algorithm-applied system recorded accuracy that was 12.5% higher than that of the fuzzy-AHP-applied system and mitigated the problem of data sparsity.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.375-381
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• 2019

Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.827-833
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• 2020

In this paper, to reduce the unpaid rate of local governments, the internal data elements affecting the arrears in Water-INFOS are searched through interviews with meter readers in certain local governments. Candidate data affecting arrears from national statistical data were derived. The influence of the independent variable on the dependent variable was sampled by examining the disorder of the dependent variable in the data set called information gain. We also evaluated the higher prediction rates of decision tree and logistic regression using n-fold cross-validation. The results confirmed that the decision tree can find more accurate customer payment patterns than logistic regression. In the process of developing an analysis algorithm model using machine learning, the optimal values of two environmental variables, the minimum number of data and the maximum purity, which directly affect the complexity and accuracy of the decision tree, are derived to improve the accuracy of the algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.133-138
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• 2014

This study attempted to investigate the validation of Phonation Threshold Power of Patients who have Functional voice disorder. 50 subjects participated in the study (32 subjects were patients who had functional voice disorders and 20 subjects were normal adults). The PAS (Phonatory aerodynamic system, model 6600, KAY electronics, Inc.) was used to measure the data and to do the analysis. Data from the Phonation Threshold Power was measured multiplying Phonation Threshold Pressure and Phonation Threshold Airflow. Phonation Threshold Pressure and Phonation Threshold Airflow were measured by the PAS protocol. Those were used because of the ease of phonation. The results of this study showed that the differences in Phonation Threshold Power between patients who had functional voice disorder and normal adults could become a significant index. Patients who had functional voice disorder showed more higher figures than normal adults. The results of study showed that Phonation threshold Power is more sensitive than Phonation Threshold Pressure and Phonation Threshold Airflow. The measured data also provided useful information for diagnosing patients with vocal fold.