Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries

Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries | 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 Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries SREEDEVI V, Tina Shivnani, jeyakrishnan venugopal, Jampala Maheshchandra Babu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8857613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The rapid adoption of artificial intelligence (AI) in the automobile banking industry has transformed business processes, yet how AI techniques influence financial performance is not yet fully understood. The difficulty here lies in scattered AI disclosures and disparate metrics that hinder effective forecasts of financial results in car-related banks. In order to bridge this gap, the research introduces a model called "Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries." The analysis relies on a dataset called "AI Techniques on Financial Performance of Automobile Industries," which contains 131 annual report records for sixteen Indian-listed banks from 2015 to 2023. This dataset captures keyword-based AI disclosures, as well as institutional and financial indicators. Data pre-processing is done through the use of the Correlation Coefficient with Min–Max Weighted (CCMMW) method to normalize and fine-tune the input parameters, while the Masked Attention Mask Transformer (Mask2Former) is utilized for feature extraction for isolating the disclosure patterns that have the greatest influence. Following these steps, a Self-Guided Quantum Generative Adversarial Network (SG-QGAN) is constructed and then advanced into the new Residual Self-Guided Quantum Single-Head Generative Elk Herd Vision Adversarial Attention Network (RSG-QSAGE-HV2AN). This network combines SG-QGAN with a Residual Attention Single-Head Vision Transformer Network (RA-SHViT-Net) and fine-tunes its parameters with the Elk Herd Optimizer (EHO). Experimental outcomes show that RSG-QSAGE-HV2AN is 99.9% accurate in calculating how AI methods affect financial performance and also improves the financial analytics' interpretability and greatly lessens computational overhead over conventional deep learning approaches. Artificial Intelligence Correlation Coefficient with Min–Max Weighted (CCMMW) Masked Attention Mask Transformer Self-Guided Quantum Generative Adversarial Network Residual Attention Single-Head Vision Transformer Network Elk Herd Optimizer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 12 Feb, 2026 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. 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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-8857613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598351396,"identity":"a80bc330-5d10-4598-bf7b-94b4503bd42e","order_by":0,"name":"SREEDEVI V","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"SREEDEVI","middleName":"","lastName":"V","suffix":""},{"id":598351402,"identity":"2aa6de3c-f79c-46eb-b525-f2b497c6d7ac","order_by":1,"name":"Tina Shivnani","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"","lastName":"Shivnani","suffix":""},{"id":598351403,"identity":"fd6740de-44f2-4040-9b17-134bef69e239","order_by":2,"name":"jeyakrishnan venugopal","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"jeyakrishnan","middleName":"","lastName":"venugopal","suffix":""},{"id":598351413,"identity":"33204ebc-fba8-475d-8d2d-e9a2c9493119","order_by":3,"name":"Jampala Maheshchandra Babu","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Jampala","middleName":"Maheshchandra","lastName":"Babu","suffix":""}],"badges":[],"createdAt":"2026-02-12 05:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8857613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8857613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104401590,"identity":"1559f6fd-c2eb-43bd-9743-7f991a6a8cbd","added_by":"auto","created_at":"2026-03-11 12:13:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1076963,"visible":true,"origin":"","legend":"","description":"","filename":"ResidualSelfGuidedQuantumVisionAdversarialAttentionNetworkforPredictingAIDrivenFinancialPerformanceinAutomobileIndustries.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8857613/v1_covered_df7b5f3e-3879-49f6-b1df-9e666fce374c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Artificial Intelligence, Correlation Coefficient with Min–Max Weighted (CCMMW), Masked Attention Mask Transformer, Self-Guided Quantum Generative Adversarial Network, Residual Attention Single-Head Vision Transformer Network, Elk Herd Optimizer","lastPublishedDoi":"10.21203/rs.3.rs-8857613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8857613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid adoption of artificial intelligence (AI) in the automobile banking industry has transformed business processes, yet how AI techniques influence financial performance is not yet fully understood. The difficulty here lies in scattered AI disclosures and disparate metrics that hinder effective forecasts of financial results in car-related banks. In order to bridge this gap, the research introduces a model called \"Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries.\" The analysis relies on a dataset called \"AI Techniques on Financial Performance of Automobile Industries,\" which contains 131 annual report records for sixteen Indian-listed banks from 2015 to 2023. This dataset captures keyword-based AI disclosures, as well as institutional and financial indicators. 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