Multi Level Artificial Neural Tree Approach with Signature Database Integration for Textual and Image Data

Multi Level Artificial Neural Tree Approach with Signature Database Integration for Textual and Image Data | 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 Multi Level Artificial Neural Tree Approach with Signature Database Integration for Textual and Image Data Usman Ahmad, Han Mu, Wenyi Zheng, Shahid Mahmood This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5517331/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Machine learning (ML) and deep learning (DL) applications are improving operational efficiency in several domains. Combining models from ML with DL provides not only a viable option but is also advantageous for real-world problems. In this paper, we propose an explainable artificial neural tree model that is a novel three-level tree architecture that comprises primary, intermediate, and target trees to process the textual datasets for classification and regression. Our methodology incorporates a neuron signature database that holds ample information on the intermediary tree structure of each neuron. We introduce a tree generation algorithm and a prediction algorithm that elaborate on the testing phase for textual data. To illustrate the efficacy of our model, we leverage two distinct datasets: a well-known publicly available iris dataset and a synthetic diabetes dataset. We continue to collaborate to build the tree structure for image processing. We present the image dataset's preprocessing and feature extraction procedures. We propose an algorithm for building trees for an image, which may be applied to image recognition by means of comparison with an example image. Moreover, we provided a prediction algorithm for the image recognition dataset. We practically demonstrated our model by generating explainable artificial neural trees and conducting the training and testing processes on two sample grayscale images of handwritten digits, 0 and 1. The trees for the training images contain 118 and 43 neurons, corresponding to the digits 0 and 1, respectively. For testing, a tree with 33 neurons was generated for an image representing the digit 1. Our proposed tree model leverages the accuracy of textual and image data, and the results of our experiments validate the efficacy of our model. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Software Decision Tree Artificial Neural Networks Graph Neural Networks Machine Learning Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 29 Mar, 2025 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-5517331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443217440,"identity":"a3e79b23-e800-40d1-bd2b-63cde22ee543","order_by":0,"name":"Usman Ahmad","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Usman","middleName":"","lastName":"Ahmad","suffix":""},{"id":443217441,"identity":"b9094819-39c5-4c17-bfdb-8ecc1972ee05","order_by":1,"name":"Han Mu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACAzBZwcAMoiRI0HKGZC2MbRAOcVrM2c8e3cw77zC7fAPzwds8DHZ5BLVY9uSl3ebddpjZ4ABbsjUPQ3IxYYcdyDGDaGHgMZPmYTiQ2EBQy/k3QC1zDjPLN/B/I1LLDZAtDYeZGQ7wsBGr5Y3ZzTnH0pkNDrMZW84xSCbGYTlmN97UWCfLtzc/vPGmwo6wFhhIhkSmAbHqgcCOBLWjYBSMglEw0gAAD1c3gHPJnOEAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Han","middleName":"","lastName":"Mu","suffix":""},{"id":443217442,"identity":"e4fd0965-dcae-45c1-b5c4-1c1646776621","order_by":2,"name":"Wenyi Zheng","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Zheng","suffix":""},{"id":443217443,"identity":"c153d33a-e57c-40f4-9cea-637066aba727","order_by":3,"name":"Shahid Mahmood","email":"","orcid":"","institution":"Intelligent Mobility and Software, FEV Iberia","correspondingAuthor":false,"prefix":"","firstName":"Shahid","middleName":"","lastName":"Mahmood","suffix":""}],"badges":[],"createdAt":"2024-11-25 06:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5517331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5517331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80704374,"identity":"edfe4800-dc2c-476c-82b3-b722962c1a6f","added_by":"auto","created_at":"2025-04-16 08:13:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":637932,"visible":true,"origin":"","legend":"","description":"","filename":"3SpringerFormatArtificialNeuralTrees.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5517331/v1_covered_1acc1d5f-661e-4ca9-b4d6-49f43e9d93c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi Level Artificial Neural Tree Approach with Signature Database Integration for Textual and Image Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Decision Tree, Artificial Neural Networks, Graph Neural Networks, Machine Learning, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-5517331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5517331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning (ML) and deep learning (DL) applications are improving operational efficiency in several domains. 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