ViSense: An Integrated Multi-Modal AI Framework for Intelligent Video Understanding with Sequential Cognitive Visualization.

ViSense: An Integrated Multi-Modal AI Framework for Intelligent Video Understanding with Sequential Cognitive Visualization. | 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 Article ViSense: An Integrated Multi-Modal AI Framework for Intelligent Video Understanding with Sequential Cognitive Visualization. A. E. Amin, Shaymaa E. Sorour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8521136/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 This paper presents a sophisticated AI-based and deep learning framework for comprehensive video file analysis, comprising nine separate intelligent agencies integrated with an innovative sequential rendering concept. The proposed system utilizes deep learning model architectures, including convolutional neural networks (CNNs) for visual processing, recurrent neural networks for temporal modeling, and transformer-based models for integrating different media. The key innovation lies in the application of a dual screen rendering strategy that separates detailed textual analysis from graphical visualizations, thereby enhancing user comprehension and depth of analysis. Experimental results demonstrate superior performance in video comprehension tasks, with an average accuracy improvement of 23.7% compared to the latest available methods. Experimental results on benchmark datasets demonstrate superior performance in video comprehension tasks, with an average accuracy improvement of up to 23.7% compared to state-of-the-art methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Video Analysis Deep Learning Multi-Agent Visualization Paradigm Computer Vision Sequential Presentation Human-Computer Interaction Full Text Additional Declarations No competing interests reported. Supplementary Files CREMADmaster.zip 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-8521136","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578410252,"identity":"243d9b9e-31f9-4ec5-8e2a-90bc5e9b6b7e","order_by":0,"name":"A. E. 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