• 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.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.19-21
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• 2004

In many cases, human's visual recognition depends on contextual information. We need to use effective feature information for performing vigorous place recognition to illumination, noise, etc. In the existing cases that use edge and color, etc., visual recognition doesn't cope effectively with real environment. To solve this problem, using natural marker, we improve the efficiency of place recognition.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1052-1057
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• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.235-240
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• 1996

The automated ECG diagnostic systems that are odd in hospitals have low performance of P-wave detection when faced with some diseases such as conduction block. So, the purpose of this study was the improvement of detection performance in conduction block which is low in P-wave detection. The first procedure was removal of baseline drift by subtracting the median filtered signal of 0.4 second length from the original signal. Then the algorithm detected R peak and T end point and cancelled the QRS-T complex to get'p prototypes'. Next step was magnification of P prototypes with dispersion and detection of'p candidates'in the magnified signal, and then extraction of contextual information concerned with P-waves. For the last procedure, the CIR was applied to P candidates to confirm P-waves. The rule base consisted of three rules that discriminate and confirm P-waves. This algorithm was evaluated using 500 patient's raw data P-wave detection perFormance was in- creased 6.8% compared with the QRS-T complex cancellation method without application of the rule base.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.14-24
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• 2016

Recently, the sound design of the electric vehicle has emerged as one of the new research objects. This study was conducted to explore and examine major values of interior driving sound of the electric vehicle from the perspective of user experience. An exploratory study based on the ground theory extracted contextual information and quietness as independent variables, and then we analyzed the main and interaction effects of those two variables on the usefulness, emotion, and satisfaction through a $2{\times}2$ factorial experimental design. The experimental study demonstrated that the effect on the user experience of electric vehicle can be conditioned by the combination of contextual information sound and quietness. Based on the results of this study, we suggested future research agendas for the optimization of user needs which reflect individual preference of interior driving sound values.

• Proceedings of the Korean Society for Language and Information Conference
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• 1991.02a
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• pp.64-90
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• 1991

• Proceedings of the Korean Society for Language and Information Conference
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• 1985.02a
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• pp.23-29
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• 1985

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.217-236
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• 2013

Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to decide the order in which documents should be displayed. Existing ranking mechanisms decide on the order of a web page based on the amount and popularity of the links pointed to and emerging from it. Sometime search engines result in placing less relevant documents in the top positions in response to a user query. There is a strong need to improve the ranking strategy. In this paper, a novel ranking mechanism is being proposed to rank the web documents that consider both the HTML structure of a page and the contextual senses of keywords that are present within it and its back-links. The approach has been tested on data sets of URLs and on their back-links in relation to different topics. The experimental result shows that the overall search results, in response to user queries, are improved. The ordering of the links that have been obtained is compared with the ordering that has been done by using the page rank score. The results obtained thereafter shows that the proposed mechanism contextually puts more related web pages in the top order, as compared to the page rank score.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.639-647
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• 2020

Entrepreneurial intention is receiving immense recognition in entrepreneurship researches, as it motives an individual to become an entrepreneur. Still, the interplay between gender perspective and contextual factors (i.e., access to capital, business information, social network, educational support, structural support) are not fully investigated in understanding the entrepreneurial intention in developing countries like Bangladesh. Therefore, the paper aims to examine the gender difference and educational discipline difference in the university's students' entrepreneurial intention in relation to contextual factors in Bangladesh. In this study, sample has been particularly taken from the different disciplinary students of private universities. Five-point Likert scale-based survey questionnaire was developed based on past researches. 280 online survey forms were distributed among the university students and finally 225 students' response were found correct as the study sample size (final survey response rate = 80%), after eliminating the incorrect survey responses. For statistical analysis SPSS 23.0 version is used. One-way ANOVA is used to measure the gender and discipline difference on entrepreneurial intention among male and female students. The results show that business information and social network will have more influence on male students' entrepreneurial intention, and comparatively, business students have more willingness to become entrepreneurs than other departmental students.