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Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review | Journal of Data Science and Intelligent Systems

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review | Journal of Data Science and Intelligent Systems </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/7/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Data Science and Intelligent Systems"/> <meta name="citation_journal_abbrev" content="JDSIS"/> <meta name="citation_issn" content="2972-3841"/> <meta name="citation_author" content="Na Luo"/> <meta name="citation_author_institution" content="School of Software Engineering, Chengdu University of Information Technology, China"/> <meta name="citation_author" content="Hua Yu"/> <meta name="citation_author_institution" content="Chengdu Center of China Electronic Port Data Center, China"/> <meta name="citation_author" content="Zeqing You"/> <meta name="citation_author_institution" content="Chengdu Center of China Electronic Port Data Center, China"/> <meta name="citation_author" content="Yao Li"/> <meta name="citation_author_institution" content="School of Software Engineering, Chengdu University of Information Technology, China"/> <meta name="citation_author" content="Tunan Zhou"/> <meta name="citation_author_institution" content="Chengdu Center of China Electronic Port Data Center, China"/> <meta name="citation_author" content="Yuwei Jiao"/> <meta name="citation_author_institution" content="Sichuan Digital Transportation Technology Co., Ltd., China"/> <meta name="citation_author" content="Nan Han"/> <meta name="citation_author_institution" content="School of Management, Chengdu University of Information Technology, China"/> <meta name="citation_author" content="Chenxu Liu"/> <meta name="citation_author_institution" content="School of Software Engineering, Chengdu University of Information Technology, China"/> <meta name="citation_author" content="Zihan Jiang"/> <meta name="citation_author_institution" content="School of Software Engineering, Chengdu University of Information Technology, China"/> <meta name="citation_author" content="Shaojie Qiao"/> <meta name="citation_author_institution" content="School of Software Engineering, Chengdu University of Information Technology, China"/> <meta name="citation_title" content="Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023/06/16"/> <meta name="citation_volume" content="1"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="2"/> <meta name="citation_lastpage" content="11"/> <meta name="citation_doi" content="10.47852/bonviewJDSIS32021078"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1078"/> <meta name="citation_abstract" xml:lang="en" content="With the rapid growth in foreign trade business and the continuous expansion of customs functions, the amount of data obtained by customs monitoring systems has drastically increased, and risk management techniques have been widely used in the customs field. Risk assessment, as an important part of risk management, can help departments of customs to understand the possibility and impact of risks in advance. Assessing the risks of import and export enterprises is an important task for customs. However, the assessment is challenging because of the large-scale of data, rapidly changing information, and inherent fuzziness. To address this issue, this survey summarizes the existing risk assessment methods, combines the preliminaries of fuzzy logic and neural networks, and applies the representative risk assessment models in the risk assessment of import and export enterprises. The risk assessment models based on fuzzy logic and neural networks can handle vague and uncertain data and improve the efficiency of risk assessment through adaptive and learning capabilities. The combination of theoretical and simple case suggests that the risk assessment method based on fuzzy logic and neural networks has good feasibility for import and export enterprises and provides valuable references for developing effective risk assessment models of import and export enterprises in the future. 聽 Received: 30聽April 2023 | Revised: 14聽June 2023 | Accepted: 16 June 2023 聽 Conflicts of Interest: Shaojie Qiao is the Editor-in-Chief for Journal of Data Science and Intelligent Systems聽and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work."/> <meta name="citation_keywords" xml:lang="en" content="customs risk assessment"/> <meta name="citation_keywords" xml:lang="en" content="fuzzy logic"/> <meta name="citation_keywords" xml:lang="en" content="neural network"/> <meta name="citation_keywords" xml:lang="en" content="risk management"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/jdsis/article/download/1078/510"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Na Luo"/> <meta name="DC.Creator.PersonalName" content="Hua Yu"/> <meta name="DC.Creator.PersonalName" content="Zeqing You"/> <meta name="DC.Creator.PersonalName" content="Yao Li"/> <meta name="DC.Creator.PersonalName" content="Tunan Zhou"/> <meta name="DC.Creator.PersonalName" content="Yuwei Jiao"/> <meta name="DC.Creator.PersonalName" content="Nan Han"/> <meta name="DC.Creator.PersonalName" content="Chenxu Liu"/> <meta name="DC.Creator.PersonalName" content="Zihan Jiang"/> <meta name="DC.Creator.PersonalName" content="Shaojie Qiao"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-06-16"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-05-17"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-07-20"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-07"/> <meta name="DC.Description" xml:lang="en" content="With the rapid growth in foreign trade business and the continuous expansion of customs functions, the amount of data obtained by customs monitoring systems has drastically increased, and risk management techniques have been widely used in the customs field. Risk assessment, as an important part of risk management, can help departments of customs to understand the possibility and impact of risks in advance. Assessing the risks of import and export enterprises is an important task for customs. However, the assessment is challenging because of the large-scale of data, rapidly changing information, and inherent fuzziness. To address this issue, this survey summarizes the existing risk assessment methods, combines the preliminaries of fuzzy logic and neural networks, and applies the representative risk assessment models in the risk assessment of import and export enterprises. The risk assessment models based on fuzzy logic and neural networks can handle vague and uncertain data and improve the efficiency of risk assessment through adaptive and learning capabilities. The combination of theoretical and simple case suggests that the risk assessment method based on fuzzy logic and neural networks has good feasibility for import and export enterprises and provides valuable references for developing effective risk assessment models of import and export enterprises in the future. 聽 Received: 30聽April 2023 | Revised: 14聽June 2023 | Accepted: 16 June 2023 聽 Conflicts of Interest: Shaojie Qiao is the Editor-in-Chief for Journal of Data Science and Intelligent Systems聽and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="1078"/> <meta name="DC.Identifier.pageNumber" content="2-11"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJDSIS32021078"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1078"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Data Science and Intelligent Systems"/> <meta name="DC.Source.ISSN" content="2972-3841"/> <meta name="DC.Source.Issue" content="1"/> <meta name="DC.Source.Volume" content="1"/> <meta name="DC.Source.URI" content="https://ojs.bonviewpress.com/index.php/jdsis"/> <meta name="DC.Subject" xml:lang="en" content="customs risk assessment"/> 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href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/55"> Vol. 1 No. 1 (2023) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Review </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Na Luo </span> <span class="affiliation"> School of Software Engineering, Chengdu University of Information Technology, China </span> </li> <li> <span class="name"> Hua Yu </span> <span class="affiliation"> Chengdu Center of China Electronic Port Data Center, China </span> </li> <li> <span class="name"> Zeqing You </span> <span class="affiliation"> Chengdu Center of China Electronic Port Data Center, China </span> </li> <li> <span class="name"> Yao Li </span> <span class="affiliation"> School of Software Engineering, Chengdu University of Information Technology, China </span> </li> <li> <span class="name"> Tunan Zhou </span> <span class="affiliation"> Chengdu Center of China Electronic Port Data Center, China </span> </li> <li> <span class="name"> Yuwei Jiao </span> <span class="affiliation"> Sichuan Digital Transportation Technology Co., Ltd., China </span> </li> <li> <span class="name"> Nan Han </span> <span class="affiliation"> School of Management, Chengdu University of Information Technology, China </span> </li> <li> <span class="name"> Chenxu Liu </span> <span class="affiliation"> School of Software Engineering, Chengdu University of Information Technology, China </span> </li> <li> <span class="name"> Zihan Jiang </span> <span class="affiliation"> School of Software Engineering, Chengdu University of Information Technology, China </span> </li> <li> <span class="name"> Shaojie Qiao </span> <span class="affiliation"> School of Software Engineering, Chengdu University of Information Technology, China </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-4703-780X" target="_blank"> https://orcid.org/0000-0002-4703-780X </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJDSIS32021078"> https://doi.org/10.47852/bonviewJDSIS32021078 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> customs risk assessment, fuzzy logic, neural network, risk management </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>With the rapid growth in foreign trade business and the continuous expansion of customs functions, the amount of data obtained by customs monitoring systems has drastically increased, and risk management techniques have been widely used in the customs field. Risk assessment, as an important part of risk management, can help departments of customs to understand the possibility and impact of risks in advance. Assessing the risks of import and export enterprises is an important task for customs. However, the assessment is challenging because of the large-scale of data, rapidly changing information, and inherent fuzziness. To address this issue, this survey summarizes the existing risk assessment methods, combines the preliminaries of fuzzy logic and neural networks, and applies the representative risk assessment models in the risk assessment of import and export enterprises. The risk assessment models based on fuzzy logic and neural networks can handle vague and uncertain data and improve the efficiency of risk assessment through adaptive and learning capabilities. The combination of theoretical and simple case suggests that the risk assessment method based on fuzzy logic and neural networks has good feasibility for import and export enterprises and provides valuable references for developing effective risk assessment models of import and export enterprises in the future.</p> <p>聽</p> <p><strong>Received: </strong>30聽April 2023 <strong>| Revised: </strong>14聽June 2023 <strong>| Accepted:</strong> 16 June 2023</p> <p>聽</p> <p><strong>Conflicts of Interest:</strong></p> <p>Shaojie Qiao is the Editor-in-Chief for <em>Journal of Data Science and Intelligent Systems</em>聽and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89269", "element":"#trendmd-suggestions"}'></script> </div><!-- .main_entry --> <div class="entry_details"> <div class="item cover_image"> <div class="sub_item"> <a href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/55"> <img src="https://ojs.bonviewpress.com/public/journals/7/cover_issue_55_en_US.png" alt=""> </a> </div> </div> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://ojs.bonviewpress.com/index.php/jdsis/article/view/1078/510"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>2023-06-16</span> </div> </section> </div> <div class="item issue"> <section class="sub_item"> <h2 class="label"> Issue </h2> <div class="value"> <a class="title" href="https://ojs.bonviewpress.com/index.php/jdsis/issue/view/55"> Vol. 1 No. 1 (2023) </a> </div> </section> <section class="sub_item"> <h2 class="label"> Section </h2> <div class="value"> Review </div> </section> </div> <div class="item copyright"> <h2 class="label"> License </h2> <p>Copyright (c) 2023 Authors</p> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry">Luo, N., Yu, H., You, Z., Li, Y., Zhou, T., Jiao, Y., Han, N., Liu, C., Jiang, Z., &#38; Qiao, S. (2023). Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review. <i>Journal of Data Science and Intelligent Systems</i>, <i>1</i>(1), 2-11. <a href="https://doi.org/10.47852/bonviewJDSIS32021078">https://doi.org/10.47852/bonviewJDSIS32021078</a></div> </div> </div> <div class="citation_formats"> <button class="citation_formats_button label" aria-controls="cslCitationFormats" aria-expanded="false" data-csl-dropdown="true"> More Citation Formats </button> <div id="cslCitationFormats" class="citation_formats_list" aria-hidden="true"> <ul class="citation_formats_styles"> <li> <a aria-controls="citationOutput" href="https://ojs.bonviewpress.com/index.php/jdsis/citationstylelanguage/get/acm-sig-proceedings?submissionId=1078&amp;publicationId=2252&amp;issueId=55" data-load-citation 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