Supply Chain Risk Prediction Using Elite Attentive Foraging Optimized Incremental Distributed Learning Based Deep Gradient Boosting Model

Supply Chain Risk Prediction Using Elite Attentive Foraging Optimized Incremental Distributed Learning Based Deep Gradient Boosting Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Supply Chain Risk Prediction Using Elite Attentive Foraging Optimized Incremental Distributed Learning Based Deep Gradient Boosting Model Asmaa Kafou, Ahmad Alzubi, Tolga Oz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5432572/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Discover Computing → Version 1 posted 9 You are reading this latest preprint version Abstract Supply chain risk management has emerged as a critical area of focus, as disruptions in this area can significantly impact the overall performance and profitability of organizations. In today's dynamic and complex global environment, Supply Chains confront escalating challenges, such as demand volatility, globalization, disruptions, and complexity. Traditional approaches to Supply Chain Management cannot predict risks accurately due to their computational complexity and overfitting issues. The proposed framework overcomes the aforementioned challenges in the existing techniques by introducing the Elite attentive foraging optimized incremental distributed learning-based deep Gradient Boosting (EAF-optimized IDL-based Deep GB) model to advance the supply chain risk prediction. Utilizing the K-Nearest Neighbor imputation method, the research addresses missing data, enhancing dataset completeness and reliability. The Synthetic Minority Over-Sampling Technique is utilized to minimize the risk of overfitting and an incremental distributed learning technique mitigates model bias and bolsters predictive capabilities, respectively. Additionally, a Convolutional Neural Network is employed to extract relevant features from supply chain data, optimizing pattern detection in complex datasets. The model leverages a Light Gradient Boosting Machine and an ensemble approach to improve predictive accuracy by offering a nuanced understanding of supply chain data features. Specifically, the EAF optimization fine-tunes the hyperparameters of the IDL-based deep GB model to improve the prediction accuracy. Owing to the reliable risk management system, the performance evaluation of the EAF-optimized IDL-based deep GB model demonstrates notable effectiveness with 90% of training achieving 98% accuracy, 98% F-measure, 97.69% precision, 98.31% recall, 98.31% sensitivity, and 98.24% specificity outperforming other state-of-the-art methods. Supply Chain Risk Prediction Elite Attentive Foraging Optimization Incremental Distributed Learning Deep Gradient Boosting Model Synthetic Minority Over-Sampling Technique Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Discover Computing → Version 1 posted Editorial decision: Accepted 23 May, 2025 Editor assigned by journal 12 May, 2025 Reviews received at journal 05 May, 2025 Reviews received at journal 04 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5432572","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449751393,"identity":"bc8934ba-609a-48fc-bf18-7b84040c544a","order_by":0,"name":"Asmaa Kafou","email":"","orcid":"","institution":"University of Mediterranean Karpasia, TRNC","correspondingAuthor":false,"prefix":"","firstName":"Asmaa","middleName":"","lastName":"Kafou","suffix":""},{"id":449751395,"identity":"0a5f064c-c4b3-49a4-aa6e-0585d3c0b916","order_by":1,"name":"Ahmad Alzubi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACCSDmYWCQAVJsDAkVDAZgUR4itPBAtJxBaJEgTgtjGxFa+Gd3J354w2DHI99+xuzBw3l2xga3GxgfvG1jqDNvwGHJnbObJecwJPMw9uSYGyRuSzYzuHOA2XBuG4OEzAEc1tzI3SDNw8DMw8yQYyaRuI3ZxuBGAps0L1ALLpfJ38jd/JuHoZ6Hjf8NUMucepAW9t/4tBjcyN0GtOUwD48EyJaGw2YgW5jxaTEEarGcY3CcR0LiWblBwrHjxpI3Epsl55yTkJyBQ4sc0GE33lRUy8n3J297+KOm2rDvRvLBD2/KbPhxRwzYeSg8xgYGfDE5CkbBKBgFo4AwAABcc1BafSI09wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Mediterranean Karpasia, TRNC","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Alzubi","suffix":""},{"id":449751396,"identity":"39744feb-a6f0-44f1-8548-aadad5c0ee93","order_by":2,"name":"Tolga Oz","email":"","orcid":"","institution":"University of Mediterranean Karpasia, TRNC","correspondingAuthor":false,"prefix":"","firstName":"Tolga","middleName":"","lastName":"Oz","suffix":""}],"badges":[],"createdAt":"2024-11-11 13:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5432572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5432572/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10791-025-09625-y","type":"published","date":"2025-06-05T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84242558,"identity":"6e582b02-85ba-49c7-8d04-b3e496390de2","added_by":"auto","created_at":"2025-06-09 16:09:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2012910,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5432572/v1_covered_2fe540e9-1ec3-4511-ba32-bfed1792dc3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Supply Chain Risk Prediction Using Elite Attentive Foraging Optimized Incremental Distributed Learning Based Deep Gradient Boosting Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Supply Chain Risk Prediction, Elite Attentive Foraging Optimization, Incremental Distributed Learning, Deep Gradient Boosting Model, Synthetic Minority Over-Sampling Technique","lastPublishedDoi":"10.21203/rs.3.rs-5432572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5432572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Supply chain risk management has emerged as a critical area of focus, as disruptions in this area can significantly impact the overall performance and profitability of organizations. In today's dynamic and complex global environment, Supply Chains confront escalating challenges, such as demand volatility, globalization, disruptions, and complexity. Traditional approaches to Supply Chain Management cannot predict risks accurately due to their computational complexity and overfitting issues. The proposed framework overcomes the aforementioned challenges in the existing techniques by introducing the Elite attentive foraging optimized incremental distributed learning-based deep Gradient Boosting (EAF-optimized IDL-based Deep GB) model to advance the supply chain risk prediction. Utilizing the K-Nearest Neighbor imputation method, the research addresses missing data, enhancing dataset completeness and reliability. The Synthetic Minority Over-Sampling Technique is utilized to minimize the risk of overfitting and an incremental distributed learning technique mitigates model bias and bolsters predictive capabilities, respectively. 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