• Journal of Digital Convergence
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• v.10 no.2
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• pp.293-297
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• 2012

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1799-1818
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• 2018

Malware detections continue to be a challenging task as attackers may be aware of the rules used in malware detection mechanisms and constantly generate new breeds of malware to evade the current malware detection mechanisms. Consequently, novel and innovated malware detection techniques need to be investigated to deal with this circumstance. In this paper, we propose a new secure malware detection system in which API call fragments are used to recognize potential malware instances, and these API call fragments together with the homomorphic encryption technique are used to construct a privacy-preserving Naive Bayes classifier (PP-NBC). Experimental results demonstrate that the proposed PP-NBC can successfully classify instances of malware with a hit-rate as high as 94.93%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Korean Journal of Child Studies
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• v.36 no.3
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• pp.139-154
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• 2015

The purpose of this study was to explore the process of scientific thinking as it is revealed through the cases of constructive play for young children. For this purpose, the researcher observed and interviewed six four-year-olds in the Jaemi Class while recording them with a camcorder during a free choice activity class in the morning from April 23 to June 25, 2012. The observations were analyzed in chronological order according to the changes of theories and structure as presented by the children themselves. The process of scientific thinking in constructive play for young children can be divided into presentation of naive theories, the abandonment of naive theories according to repetitive experiences and the discovery of inconsistency, the representation of alternative theories, and the abandonment of alternative theories according to repetitive experiences and the discovery of contradictions. On the basis of the results, constructive play has proved to serve a valuable educational function by inducing scientific thinking processes in children. On the basis of this finding, the researcher suggests the need to provide appropriate educational support to teachers.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.457-462
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• 2006

In many applications, an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification learning. In this paper, we analyzed the role of unlabeled data in the context of naive Bayesian learning. Experimental results show that including unlabeled data as part of training data can significantly improve the performance of classification accuracy. The effect of using unlabeled data is especially important in case labeled data are sparse.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.501-512
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• 2003

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

• The Journal of Korean Medicine
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• v.30 no.3
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• pp.51-60
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• 2009

Objectives : It has been known that immune reaction plays an important role in the pathogenesis of otitis media (OM). We investigated the change of middle ear mucosal inflammation induced by lipopolysaccharide (LPS) following administration of Taglisodog-eum (TSE) in experimental animals. Materials and Methods : Otitis media was induced by injecting 1 mg/kg of Pseudomonas aeruginosa LPS transtympanically in 60 rats. These rats were divided into two groups, the LPS group (n=30, remained naive after OM elicitation) and the TSE group (n=30, treated with TSE after OM elicitation) and 6 additional rats were kept as a normal control group (n=6, remained naive until tissue collection). The rats were killed at the 1st, 3rd, and 7th days after challenge. The temporal bones in each group were harvested and examined histopathologically by hematoxyline-eosin stain. Middle ear mucosa were taken at the 1st, 3rd, and 7th days after challenge. The levels of spicing variants of TNF-${\alpha}$ transcription were evaluated by Realtime-PCR. Results : TSE suppressed LPS-induced TNF-${\alpha}$ mRNA expression and thickness of the submucosal layer and infiltration of inflammatory cells in rat middle ear epithelium. Conclusion : The results suggest that TSE may be effective in decreasing inflammation with particular application to mucosal metaplasia in OM.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.2
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• pp.135-139
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• 2009

We present a new ensemble learning method that employs the set of region experts, each of which learns to handle a subset of the training data. We split the training data and generate experts for different regions in the feature space. When classifying a data, we apply a weighted voting among the experts that include the data in their region. We used ten datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as Bagging and Adaboost. We used SMO, Naive Bayes and C4.5 as base learning algorithms. As a result, we found that the performance of our method is comparable to that of Adaboost and Bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.927-934
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• 2017

As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.

• Journal of the Korean Home Economics Association
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• v.26 no.1
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• pp.97-108
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• 1988

The purpose of this study is to classify the type of the Korean document chest, mungab through the literature and market survey and to investigate the preference on characteristics of the mungab. It also attempts to find out the aesthetic perception of the type of mungab by using semantic differential scales. A pilot study was conducted to select the types and pictures of the mungab. Three types and six pictures of mungab were selected. Six hundred homemakers of middle and upper class residing in seoul area were selected for the survey and 449 among them were used for data analysis. Data were analyzed using the SPSS program. The major findings are as follows: 1) The esult of pilot test, the presentative characteristics of mungab were classified into naive type, unique type, and elaborate type. 2) it was found that the modernized style of traditional mungab was wood grain and the next one was Najonchiligi. In the characteristics of decoration which was symbolic pattern, Shi-Jang-Saeng was predominant. In wanting style when they buy, the modernized style of traditional Korean chest was predominant, too. 3) Young and low income group preferred modern style using transparent paint and Sa-Goon-Ja, letters in the symbolic patterns of decoration. Old and high income group preferred traditional style using splendid and expensive materials and Ship Jang Saeng in the symbolic decoration. 4) compared with types of mungab in aesthetic perception, modern style of the traditional mungab was evaluated more positively than traditional style. The young and low income group perceived the naive type and unique type positively, but the old and high income group perceived the elaborate type the most positively.