OGAN-POW and Ensemble-Based Learning for Preserving Privacy in Financial Institutional

OGAN-POW and Ensemble-Based Learning for Preserving Privacy in Financial Institutional | 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 OGAN-POW and Ensemble-Based Learning for Preserving Privacy in Financial Institutional Jyotsna Ghildiyal Bijalwan, Anchit Bijalwan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5934690/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You are reading this latest preprint version Abstract AI and Blockchain applications are essential for modernizing outdated multimedia business models, technological processes, and industrial processes. It combines smart contracts and frames resilient technology to simplify service prices. The association of significant characteristics like instantly and securely self-verification with blockchain applications eliminated accessing the use of trustworthy third parties. The industrial 4th revolution employs AI Technology (AI) to analyze and retrieve vital information from real-world systems and hence produces the result of evolving scientific and industrial advancement. In order to increase system efficiency, it also uses digital analytics to connect the data with cloud and blockchain repositories. Investigating A.I. technologies and methodologies is still difficult due to security and privacy concerns. To protect privacy in financial institutions, the work suggests an Optimal Generative Adversarial Network-Based Proof of Work (OGAN-POW) consensus and 90%CI-Ensemble learning for malicious activity detection. The proposed framework is capable of performing a smooth node selection, system operations, service warnings, potential threats, and bogus allegations. Finally, Ensemble-based learning is used to examine data exchange and sharing. Ensemble learning evaluates the transactional services to reduce system strain and determines whether the transmission rate varies due to network connectivity issues. The results of the experiments show that the proposed methodology tends to achieve high throughput with low average latency and remains to be highly secure as compared to the methods currently considered to be state-of-the-art. Ensemble Learning Blockchain AI Technology Optimal Generative Adversarial Network-Based Proof of Work (OGAN-PoW) 90%Confidence Interval -Ensemble learning Privacy preservation and proof of work (POW) 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-5934690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410867292,"identity":"b403b732-0d05-4ec1-9189-35b5621320f2","order_by":0,"name":"Jyotsna Ghildiyal Bijalwan","email":"","orcid":"","institution":"British University Vietnam","correspondingAuthor":false,"prefix":"","firstName":"Jyotsna","middleName":"Ghildiyal","lastName":"Bijalwan","suffix":""},{"id":410867293,"identity":"b120b733-feda-4d0a-8fce-45d8776910ba","order_by":1,"name":"Anchit Bijalwan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3PsWqDQBjA8U8KN9m6XhbvFS4U2qGBvsqFgC4aAoVMHYSAU2m3IvQpfAPLB2aRzIIZzOKcqbik9M5Q7ODVtdD74+Dp/bj7AEymvxkBASAIZFYmV+73xxEiOgKKXPdEz+QfoQ6CjsyjMeK8bZr60MLyysIa7ce9//JacDiuERwWDRK6z2+5vNgDgZyjnTdhUgXcSnYIk1hzrTIgVJJ5DBnHgGAYSXJxGSPwfFiwnmyPGHyizxQ5SXKvIbwnBccwRsEVsdQpmvGnpXdDhUflLMUKT884TStv9f60822ai0Hilotm0s5mS5Zs00PygcytFmndru9cZ5Np5u+i8vmxQb3av+0/50Tje0wmk+l/9gU+S2QPqeM1BQAAAABJRU5ErkJggg==","orcid":"","institution":"Arba Minch University","correspondingAuthor":true,"prefix":"","firstName":"Anchit","middleName":"","lastName":"Bijalwan","suffix":""}],"badges":[],"createdAt":"2025-01-31 08:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5934690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5934690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75607783,"identity":"32383fdd-6e89-4dde-8a7a-e8921c21a4a5","added_by":"auto","created_at":"2025-02-06 09:47:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861992,"visible":true,"origin":"","legend":"","description":"","filename":"AIOCGANPOW.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5934690/v1_covered_02a65a8a-1a13-4e6d-b8ac-0ca8bd468496.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"OGAN-POW and Ensemble-Based Learning for Preserving Privacy in Financial Institutional","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":"Ensemble Learning, Blockchain, AI Technology, Optimal Generative Adversarial Network-Based Proof of Work (OGAN-PoW), 90%Confidence Interval -Ensemble learning, Privacy preservation, and proof of work (POW)","lastPublishedDoi":"10.21203/rs.3.rs-5934690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5934690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"AI and Blockchain applications are essential for modernizing outdated multimedia business models, technological processes, and industrial processes. 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