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Volume 14
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10.3390/app14010378
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Article

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

by
Shinya Watanuki
1,*,
Yumiko Nomura
2,
Yuki Kiyota
3,
Minami Kubo
4,
Kenji Fujimoto
5,
Junko Okada
6 and
Katsue Edo
7
1
Department of Marketing, Faculty of Commerce, University of Marketing and Distribution Sciences, 3-1 Gakuen-Nishimachi, Nishi-ku, Kobe 651-2188, Hyogo, Japan
2
Department of Nursing, Japanese Red Cross Hiroshima College of Nursing, 1-2 Ajinadai-Higashi, Hatsukaichi 738-0052, Hiroshima, Japan
3
Graduate School of Comprehensive Scientific Research Program in Health and Welfare, Prefectural University of Hiroshima, 1-1 Gakuencho, Mihara 723-0053, Hiroshima, Japan
4
Hiroshima Red Cross Hospital Atomic-Bomb Survivors Hospital, 1-9-6 Sendamachi, Naka-ku, Hiroshima 730-8619, Hiroshima, Japan
5
Hiroshima Office, Survey Research Center Co., Ltd., 2-29 Tatemachi, Naka-ku, Hiroshima 730-0032, Hiroshima, Japan
6
Faculty of Health and Welfare, Prefectural University of Hiroshima, 1-1 Gakuencho, Mihara 723-0053, Hiroshima, Japan
7
Hiroshima Business and Management School, Prefectural University of Hiroshima, 1-1-71 Ujina-Higashi, Minami-ku, Hiroshima 734-8558, Hiroshima, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(1), 378; https://doi.org/10.3390/app14010378
Submission received: 28 October 2023 / Revised: 27 December 2023 / Accepted: 29 December 2023 / Published: 31 December 2023
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)
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Abstract

Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues.
Keywords: questionnaire survey; marketing research; EEG; deep learning; generative adversarial networks (GANs) questionnaire survey; marketing research; EEG; deep learning; generative adversarial networks (GANs)

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MDPI and ACS Style

Watanuki, S.; Nomura, Y.; Kiyota, Y.; Kubo, M.; Fujimoto, K.; Okada, J.; Edo, K. Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science. Appl. Sci. 2024, 14, 378. https://doi.org/10.3390/app14010378

AMA Style

Watanuki S, Nomura Y, Kiyota Y, Kubo M, Fujimoto K, Okada J, Edo K. Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science. Applied Sciences. 2024; 14(1):378. https://doi.org/10.3390/app14010378

Chicago/Turabian Style

Watanuki, Shinya, Yumiko Nomura, Yuki Kiyota, Minami Kubo, Kenji Fujimoto, Junko Okada, and Katsue Edo. 2024. "Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science" Applied Sciences 14, no. 1: 378. https://doi.org/10.3390/app14010378

APA Style

Watanuki, S., Nomura, Y., Kiyota, Y., Kubo, M., Fujimoto, K., Okada, J., & Edo, K. (2024). Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science. Applied Sciences, 14(1), 378. https://doi.org/10.3390/app14010378

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