• Title/Summary/Keyword: 로지스틱 회귀모형

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Success Factor in the K-Pop Music Industry: focusing on the mediated effect of Internet Memes (대중음악 흥행 요인에 대한 연구: 인터넷 밈(Internet Meme)의 매개효과를 중심으로)

  • YuJeong Sim;Minsoo Shin
    • Journal of Service Research and Studies
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    • v.13 no.1
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    • pp.48-62
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    • 2023
  • As seen in the recent K-pop craze, the size and influence of the Korean music industry is growing even bigger. At least 6,000 songs are released a year in the Korean music market, but not many can be said to have been successful. Many studies and attempts are being made to identify the factors that make the hit music. Commercial factors such as media exposure and promotion as well as the quality of music play an important role in the commercial success of music. Recently, there have been many marketing campaigns using Internet memes in the pop music industry, and Internet memes are activities or trends that spread in various forms, such as images and videos, as cultural units that spread among people. Depending on the Internet environment and the characteristics of digital communication, contents are expanded and reproduced in the form of various memes, which causes a greater response to consumers. Previously, the phenomenon of Internet memes has occurred naturally, but artists who are aware of the marketing effects have recently used it as an element of marketing. In this paper, the mediated effect of Internet memes in relation to the success factors of popular music was analyzed, and a prediction model reflecting them was proposed. As a result of the analysis, the factors with the mediated effect of 'cover effect' and 'challenge effect' were the same. Among the internal success factors, there were mediated effects in "Singer Recognition," the genres of "POP, Dance, Ballad, Trot and Electronica," and among the external success factors, mediated effects in "Planning Company Capacity," "The Number of Music Broadcasting Programs," and "The Number of News Articles." Predictive models reflecting cover effects and challenge effects showed F1-score at 0.6889 and 0.7692, respectively. This study is meaningful in that it has collected and analyzed actual chart data and presented commercial directions that can be used in practice, and found that there are many success factors of popular music and the mediating effects of Internet memes.

Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning (뇌 MRI와 인지기능평가를 이용한 아밀로이드 베타 양성 예측 연구)

  • Hye Jin Park;Ji Young Lee;Jin-Ju Yang;Hee-Jin Kim;Young Seo Kim;Ji Young Kim;Yun Young Choi
    • Journal of the Korean Society of Radiology
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    • v.84 no.3
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    • pp.638-652
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    • 2023
  • Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.