An Advanced Prediction Model for Risk Assessment of Imported Food

Da Hyun Lee; Eun Seo Lee; Sang Ji Kwon; Kyunghwa Hwang; Ohbyung Kwon

doi:10.1007/978-3-031-73122-8_25

An Advanced Prediction Model for Risk Assessment of Imported Food

Da Hyun Lee, Eun Seo Lee, Sang Ji Kwon, Kyunghwa Hwang, Ohbyung Kwon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Previous studies have primarily concentrated on the development of systems designed to predict and manage the risk associated with imported foods. However, to achieve more accurate inspection results, it is essential to enhance the performance of these prediction models. Hence, this study aims to propose methods for improving the performance of risk prediction models for imported foods. Through a series of model enhancement experiments, we have confirmed that techniques such as item name risk derivation, feature generation, dimensionality reduction, and stacking ensemble significantly contribute to model performance improvement. The findings of this study are expected to provide a strategic direction for more effective management of imported food safety and to serve as a valuable resource for future research.

Original language	English
Title of host publication	Proceedings of the Future Technologies Conference (FTC) 2024
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	380-385
Number of pages	6
ISBN (Print)	9783031731211
DOIs	https://doi.org/10.1007/978-3-031-73122-8_25
Publication status	Published - 2024
Event	9th Future Technologies Conference, FTC 2024 - London, United Kingdom Duration: 14 Nov 2024 → 15 Nov 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1155 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th Future Technologies Conference, FTC 2024
Country/Territory	United Kingdom
City	London
Period	14/11/24 → 15/11/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Dimensionality reduction
Food safety
Preemptive measures
Risk prediction model
Stacking ensemble

Access to Document

10.1007/978-3-031-73122-8_25

Cite this

Lee, D. H., Lee, E. S., Kwon, S. J., Hwang, K., & Kwon, O. (2024). An Advanced Prediction Model for Risk Assessment of Imported Food. In K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC) 2024 (pp. 380-385). (Lecture Notes in Networks and Systems; Vol. 1155 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-73122-8_25

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title = "An Advanced Prediction Model for Risk Assessment of Imported Food",

abstract = "Previous studies have primarily concentrated on the development of systems designed to predict and manage the risk associated with imported foods. However, to achieve more accurate inspection results, it is essential to enhance the performance of these prediction models. Hence, this study aims to propose methods for improving the performance of risk prediction models for imported foods. Through a series of model enhancement experiments, we have confirmed that techniques such as item name risk derivation, feature generation, dimensionality reduction, and stacking ensemble significantly contribute to model performance improvement. The findings of this study are expected to provide a strategic direction for more effective management of imported food safety and to serve as a valuable resource for future research.",

keywords = "Dimensionality reduction, Food safety, Preemptive measures, Risk prediction model, Stacking ensemble",

author = "Lee, {Da Hyun} and Lee, {Eun Seo} and Kwon, {Sang Ji} and Kyunghwa Hwang and Ohbyung Kwon",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 9th Future Technologies Conference, FTC 2024 ; Conference date: 14-11-2024 Through 15-11-2024",

year = "2024",

doi = "10.1007/978-3-031-73122-8_25",

language = "English",

isbn = "9783031731211",

series = "Lecture Notes in Networks and Systems",

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Lee, DH, Lee, ES, Kwon, SJ, Hwang, K & Kwon, O 2024, An Advanced Prediction Model for Risk Assessment of Imported Food. in K Arai (ed.), Proceedings of the Future Technologies Conference (FTC) 2024. Lecture Notes in Networks and Systems, vol. 1155 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 380-385, 9th Future Technologies Conference, FTC 2024, London, United Kingdom, 14/11/24. https://doi.org/10.1007/978-3-031-73122-8_25

An Advanced Prediction Model for Risk Assessment of Imported Food. / Lee, Da Hyun; Lee, Eun Seo; Kwon, Sang Ji et al.
Proceedings of the Future Technologies Conference (FTC) 2024. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2024. p. 380-385 (Lecture Notes in Networks and Systems; Vol. 1155 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - An Advanced Prediction Model for Risk Assessment of Imported Food

AU - Lee, Da Hyun

AU - Lee, Eun Seo

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AU - Hwang, Kyunghwa

AU - Kwon, Ohbyung

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

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AB - Previous studies have primarily concentrated on the development of systems designed to predict and manage the risk associated with imported foods. However, to achieve more accurate inspection results, it is essential to enhance the performance of these prediction models. Hence, this study aims to propose methods for improving the performance of risk prediction models for imported foods. Through a series of model enhancement experiments, we have confirmed that techniques such as item name risk derivation, feature generation, dimensionality reduction, and stacking ensemble significantly contribute to model performance improvement. The findings of this study are expected to provide a strategic direction for more effective management of imported food safety and to serve as a valuable resource for future research.

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An Advanced Prediction Model for Risk Assessment of Imported Food

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Keywords

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