Return Rate Prediction Model Using Traitor Feline Crow-Based Hybrid Long Short-Term Memory and Light Gradient-Boosting Machine Model

Return Rate Prediction Model Using Traitor Feline Crow-Based Hybrid Long Short-Term Memory and Light Gradient-Boosting Machine 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 Return Rate Prediction Model Using Traitor Feline Crow-Based Hybrid Long Short-Term Memory and Light Gradient-Boosting Machine Model Salem Younes, Muri Wole Adedokun, Ahmad Alzubi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6163361/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Return rate prediction involves forecasting the rate at which products or investments are returned, driven by factors such as customer dissatisfaction or financial performance. This predictive capability is crucial for businesses and financial institutions, as it facilitates improved decision-making, optimized inventory management, and enhanced risk assessments. However, existing predictive models are often constrained by their inability to fully capture complex, sequential patterns in data, their limited capacity to handle both temporal and non-temporal features effectively, and the challenges of balancing predictive accuracy with computational efficiency. To address these limitations, this research introduces the Traitor-Feline Crow Optimization-based Hybrid Long Short-Term Memory and Boosted Gradient Boosting Machine (TFC-LSTM boosted GBM) model for return rate prediction. The proposed TFC-LSTM boosted GBM framework excels in capturing sequential patterns and temporal dependencies, leveraging historical data trends to enhance predictive accuracy. The model strategically optimizes data utilization, effectively reducing prediction errors and improving overall performance. By employing adaptive strategies, the TFC-LSTM boosted GBM framework navigates diverse data landscapes with precision and intelligence, seamlessly integrating temporal data handling with efficient tabular data processing to create a robust predictive framework. Experimental results validate the efficacy of the proposed approach, demonstrating its superior performance using the Bitcoin price prediction dataset. The model achieves exceptionally low error rates, with a Mean Absolute Error (MAE) of 1.31 and a Mean Absolute Percentage Error (MAPE) of 3.48, underscoring its potential as a reliable and efficient solution for accurate return rate prediction. Return rate prediction Traitor Feline Crow optimization Long Short-Term Memory Gradient Boosting Machine and Statistical features Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6163361","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425022494,"identity":"76109a65-09f2-4ff6-9b41-aa66a1e0c5b9","order_by":0,"name":"Salem Younes","email":"","orcid":"","institution":"University of Mediterranean Karpasia","correspondingAuthor":false,"prefix":"","firstName":"Salem","middleName":"","lastName":"Younes","suffix":""},{"id":425022495,"identity":"9fb4bf85-4255-4e51-bbef-a5c2a81c9122","order_by":1,"name":"Muri Wole Adedokun","email":"","orcid":"","institution":"University of Mediterranean Karpasia","correspondingAuthor":false,"prefix":"","firstName":"Muri","middleName":"Wole","lastName":"Adedokun","suffix":""},{"id":425022496,"identity":"d69ac430-86cb-43e8-b901-65d17ae7da55","order_by":2,"name":"Ahmad Alzubi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACCSDmYWCQAVJsDAkVDAZgUR4itPBAtJxBaJEgTgtjGxFa+Gd3J354w2DHI99+xuzBw3l2xga3GxgfvG1jqDNvwGHJnbObJecwJPMw9uSYGyRuSzYzuHOA2XBuG4OEzAEc1tzI3SDNw8DMw8yQYyaRuI3ZxuBGAps0L1ALLpfJ38jd/JuHoZ6Hjf8NUMucepAW9t/4tBjcyN0GtOUwD48EyJaGw2YgW5jxaTEEarGcY3CcR0LiWblBwrHjxpI3Epsl55yTkJyBQ4sc0GE33lRUy8n3J297+KOm2rDvRvLBD2/KbPhxRwzYeSg8xgYGfDE5CkbBKBgFo4AwAABcc1BafSI09wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Mediterranean Karpasia","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Alzubi","suffix":""}],"badges":[],"createdAt":"2025-03-05 14:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6163361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6163361/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78364683,"identity":"874bbd8d-7dd7-4965-b00f-a65fba92664f","added_by":"auto","created_at":"2025-03-12 13:02:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1760310,"visible":true,"origin":"","legend":"","description":"","filename":"OrginalManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6163361/v1_covered_7b47e770-5c92-4864-b96d-296c1e259b3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Return Rate Prediction Model Using Traitor Feline Crow-Based Hybrid Long Short-Term Memory and Light Gradient-Boosting Machine Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Return rate prediction, Traitor Feline Crow optimization, Long Short-Term Memory, Gradient Boosting Machine, and Statistical features","lastPublishedDoi":"10.21203/rs.3.rs-6163361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6163361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReturn rate prediction involves forecasting the rate at which products or investments are returned, driven by factors such as customer dissatisfaction or financial performance. This predictive capability is crucial for businesses and financial institutions, as it facilitates improved decision-making, optimized inventory management, and enhanced risk assessments. However, existing predictive models are often constrained by their inability to fully capture complex, sequential patterns in data, their limited capacity to handle both temporal and non-temporal features effectively, and the challenges of balancing predictive accuracy with computational efficiency. To address these limitations, this research introduces the Traitor-Feline Crow Optimization-based Hybrid Long Short-Term Memory and Boosted Gradient Boosting Machine (TFC-LSTM boosted GBM) model for return rate prediction. The proposed TFC-LSTM boosted GBM framework excels in capturing sequential patterns and temporal dependencies, leveraging historical data trends to enhance predictive accuracy. The model strategically optimizes data utilization, effectively reducing prediction errors and improving overall performance. By employing adaptive strategies, the TFC-LSTM boosted GBM framework navigates diverse data landscapes with precision and intelligence, seamlessly integrating temporal data handling with efficient tabular data processing to create a robust predictive framework. Experimental results validate the efficacy of the proposed approach, demonstrating its superior performance using the Bitcoin price prediction dataset. 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