• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.245-253
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• 2022

Commenced in 1954 by IBM, machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. Morphological analysis plays a major role when translating Indian languages to develop accurate parts of speech taggers and word sense. The paper presents various machine translation methods used by different researchers for Indian languages along with their performance and drawbacks. Further, the paper concentrates on parts of speech (POS) tagging in Marathi dialect using various methods such as rule-based tagging, unigram, bigram, and more. After careful study, it is concluded that for machine translation, parts of speech tagging is a major step. Also, for the Marathi language, the Hidden Markov Model gives the best results for parts of speech tagging with an accuracy of 93% which can be further improved according to the dataset.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.413-422
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• 2022

Smart farms are receiving a lot of attention as a way to solve the chronic labor shortage and aging problems in agriculture. The smart farm industry, called the 6th industrial revolution, needs to strengthen its competitiveness. In order to apply innovative IT technology to agriculture, it is important to collect and analyze information about prior research or patents. This paper examines smart farm patent trends through 5,789 patent data related to smart farm using the domestic patent information search service(KIPRIS). This paper examines the domestic patent trends of smart farm information through keyword network, ego network, simultaneous appearance network, and bigram network analysis. As a result of network analysis related to smart farm patents, patents related to smart farm systems and control technologies were the most common. This paper can provide help in setting the direction of future smart farm-related patent research.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.129-152
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• 2021

This study proposed a plan to develop wearable devices suitable for female college students by analyzing female college students' perceptions and preferences for wearable devices and their needs for health care using topic modeling and network analysis techniques. To this end, 2,457 posts related to health care and wearable devices were collected from the community used by S Women's University students. After preprocessing the collected posts and comment data, LDA-based topic modeling was performed. Through topic modeling techniques, major issues of female college students related to health care and wearable devices are derived, and bi-gram analysis and network analysis are performed on posts containing related keywords to understand female college students' views on wearable devices.

• Journal of Korean Home Economics Education Association
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• v.31 no.3
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• pp.73-90
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• 2019

The purpose of this study was to explore the differences in perceptions of male and female students about population problems and to draw up implications for population education. Using text mining, the report about population problem, which had written by students in population education class, were analysed. After extracting key words, semantic networks were visualized. The results were as follows. First, the high frequency words were the same for each gender. Second, key words based on frequency did not differ depending on gender. And the key words extracted by the correlation analysis and bigram were different. That is, in the semantic network of girls' words, the network of "life"-"marriage"-"birth"-"pregnancy" appeared independently, distinguishing it from male students who showed separate objective links to population problems. Therefore, it drew suggestions that male and female students should be viewed as heterogeneous groups with different cognitive structures on population problems and that the content and methods of population education should be approached differently depending on gender.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.28-41
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• 2020

This study selects two linear parks representing each culture and reveals the differences between them using a visitor survey as small data and social media analytics as big data based on the three components of the model of landscape perception. The 606 in Chicago, U.S., and the Gyeongchun Line in Seoul, Korea, are representative parks built on railroads. A total of 505 surveys were collected from these parks. The responses were analyzed using descriptive statistics, principal component analysis, and linear regression. Also, more than 20,000 tweets which mentioned two linear parks respectively were collected. By using those tweets, the authors conducted the clustering analysis and draw the bigram network diagram for identifying and comparing the placeness of each park. The result suggests that more diverse design concept links to less diversity in behavior; that half of the park users use the park as a shortcut; and that same physical exercise provides different benefits depending on the park. Social media analysis showed the 606 is more closely related to the neighborhoods rather than the Gyeongchun Line Forest. The Gyeongchun Line Forest was a more event-related place than the 606.