• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.7-12
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• 2012

Recently the medical field to efficiently process the vast amounts of information to decision trees, neural networks, Bayesian Networks, including the application method of various data mining techniques are investigated. In addition, the basic personal information or patient history, family history, in addition to information such as MRI, HRCT images and additional information to collect and leverage in the diagnosis of disease, improved diagnostic accuracy is to promote a common status. But in real world situations that affect the results much because of the variable exists for a particular data mining techniques to obtain information through the enemy can be seen fairly limited. Medical images were taken as well as a minor can not give a positive impact on the diagnosis, but the proportion increased subjective judgments by the automated system is to deal with difficult issues. As a result of a complex reality, the situation is more advantageous to deal with the relative probability of the multivariate model based on Bayesian network, or TAN in the K2 search algorithm improves due to expansion model has been proposed. At this point, depending on the type of search algorithm applied significantly influenced the performance characteristics of the extended Bayesian network, the performance and suitability of each technique for evaluation of the facts is required. In this paper, we extend the Bayesian network for diagnosis of diseases using the same data were carried out, K2, TAN and changes in search algorithms such as classification accuracy was measured. In the 10-fold cross-validation experiment was performed to compare the performance evaluation based on the analysis and the onset of high-risk classification for patients with HRCT images could be possible to identify high-risk data.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.131-146
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• 2023

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8A
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• pp.918-927
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• 2004

WCDMA system is 3rd generation wireless mobile system specified by 3GPP. In WCDMA downlink, two power control schemes are operated. One is inner loop power control operated in every slot. Another is outer loop power control based on one frame time. Base station (BS) can estimate proper transmission power by these two power control schemes. However, because each MS's transmission power makes a severe effect on BS's performance, BS cannot give excessive transmission power to the specific user. 3GPP defined Power Control Dynamic Range (PCDR) to guarantee proper BS's performance. In this paper, we propose Adaptive PCDR algorithm. By APCDR algorithm, Radio Network Controller (RNC) can estimate each MS's current state using received signal to interference ratio (SIR). APCDR algorithm changes MS's maximum code channel power based on frame. By proposed scheme, each MS can reduce wireless channel effect and endure outages in cell edge. Therefore, each MS can obtain better QoS. Simulation result indicate that APCDR algorithm show more attractive output than fixed PCDR algorithm.