{"paper_id":"d34c4e89-c686-4f45-ba6d-250023a81660","body_text":"AI-Driven AR/VR Framework for Intelligent IT Operations: A CNN–GRU-Based Approach for Predictive Maintenance and Secure Decision Support | 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 AI-Driven AR/VR Framework for Intelligent IT Operations: A CNN–GRU-Based Approach for Predictive Maintenance and Secure Decision Support SWAPNIL SAURAV, K S SUDEEP This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9284060/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 The increasing complexity of Information Technology (IT) systems, coupled with the rapid adoption of Augmented Reality (AR) and Virtual Reality (VR), has created a need for intelligent frameworks capable of analyzing and predicting system performance. This study presents a data-driven approach for understanding AR/VR system behavior using a structured pipeline that integrates preprocessing, clustering, dimensionality reduction, deep learning, and optimization techniques. The proposed framework begins by transforming raw AR/VR system data into meaningful features representing performance metrics such as latency, response time, and resource utilization. To uncover hidden patterns within the data, K-Means clustering is applied, followed by Principal Component Analysis (PCA) to reduce redundancy and improve computational efficiency. A hybrid deep learning model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) is then employed to capture both feature-level interactions and deeper dependencies within the dataset. To further enhance model performance, the Brown Bear Optimization Algorithm (BBOA) is utilized for hyperparameter tuning. Experimental results demonstrate that the proposed model achieves high predictive accuracy and stable learning behavior, with performance improving significantly after optimization. The findings highlight that combining multiple techniques in a structured manner leads to better understanding and prediction of AR/VR system performance compared to using individual methods. The framework offers a practical and scalable solution for analyzing complex system data and can be extended to other domains involving high-dimensional and dynamic datasets. Computer Architecture and Engineering Augmented Reality (AR) Virtual Reality (VR) CNN–GRU K-Means Clustering Principal Component Analysis (PCA) Brown Bear Optimization Algorithm (BBOA) Predictive Modeling Deep Learning System Performance Analysis. Full Text Additional Declarations The authors declare no competing interests. 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-9284060\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":615501893,\"identity\":\"66438c80-147a-4e14-80f7-2a55f3a147ff\",\"order_by\":0,\"name\":\"SWAPNIL SAURAV\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYNACAwsYywaIGRsPEKFFAsZKA2lpIEILA1zLYTCJV4v8jORnjwsKJBjk28+YffjZdt5ubfthoC01NtE4nXQjzdx4BtBhBmdyjGf2tt1O3nYmEajlWFpuAy4t0glm0jwgLQw5xgw8Z24nmx0AamFsOIxTi/zs9G9gLfL9b4wZ/5w5l2x2/iF+LQy3cyC2MNzIMWbmqThgZ3aDgC0G99+UGwO18BjceFbMLFORnGB2A2hLAh6/yPcc3/aY54+NnHx/8mbGNwZ29mbn0x8++FBjg9thDAxsIIIHxksEq0zArRyuBQ7s8SseBaNgFIyCkQgA1TNaKQCWktgAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0002-8350-409X\",\"institution\":\"GITAM University, Hyderabad\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"SWAPNIL\",\"middleName\":\"\",\"lastName\":\"SAURAV\",\"suffix\":\"\"},{\"id\":615501894,\"identity\":\"5595feae-969e-458b-be16-2fc32a52a09e\",\"order_by\":1,\"name\":\"K S SUDEEP\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"GITAM University, Hyderabad\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"K\",\"middleName\":\"S\",\"lastName\":\"SUDEEP\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-31 20:11:39\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-9284060/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9284060/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":105969330,\"identity\":\"e67c83b1-1d95-4e00-b27a-faec8ff3a490\",\"added_by\":\"auto\",\"created_at\":\"2026-04-02 03:05:57\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":716467,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscript1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9284060/v1_covered_8d21893f-2f87-4a82-9c24-398514b813ee.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eAI-Driven AR/VR Framework for Intelligent IT Operations: A CNN–GRU-Based Approach for Predictive Maintenance and Secure Decision Support\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Augmented Reality (AR), Virtual Reality (VR), CNN–GRU, K-Means Clustering, Principal Component Analysis (PCA), Brown Bear Optimization Algorithm (BBOA), Predictive Modeling, Deep Learning, System Performance Analysis.\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9284060/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9284060/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe increasing complexity of Information Technology (IT) systems, coupled with the rapid adoption of Augmented Reality (AR) and Virtual Reality (VR), has created a need for intelligent frameworks capable of analyzing and predicting system performance. 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