Advanced Smart Assistance with Enhancing Social Interaction and Daily Activities for Visually Impaired Individuals using Deep Learning with Modified Seagull Optimization

Advanced Smart Assistance with Enhancing Social Interaction and Daily Activities for Visually Impaired Individuals using Deep Learning with Modified Seagull Optimization | 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 Advanced Smart Assistance with Enhancing Social Interaction and Daily Activities for Visually Impaired Individuals using Deep Learning with Modified Seagull Optimization Sana Alazwari, Hussah Nasser AlEisa, Mohammed Rizwanullah, Radwa Marzouk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5559574/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2025 Read the published version in Scientific Reports → Version 1 posted 7 You are reading this latest preprint version Abstract Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. These bridges, designed to withstand diverse loads such as seismic forces, traffic weight, and environmental factors, are significant for maintaining structural integrity. Damage detection comprises applying advanced structural health monitoring methods to identify and assess potential deterioration or damage in concrete bridge components. Machine learning (ML) models, pattern detection, and statistical analysis are extensively adopted to identify subtle changes and process sensor information in structural response that might indicate corrosion, cracks, or other structural problems. Earlier detection and continuous monitoring of damage enable prompt intervention, ensuring longevity and safety while reducing the need for extensive repairs or the risk of unexpected failures. This study proposes an Automated Damage Detection using a Modified Seagull Optimizer with Ensemble Learning (ADD-MSGOEL) method for visually impaired people. The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. Initially, the ADD-MSGOEL method utilizes contrast enhancement (CLAHE) to enhance the image quality. Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. At last, an ensemble of three models, namely long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and sparse autoencoder (SAE) models, are implemented for the classification and detection of the damages. To demonstrate the effectiveness of the ADD-MSGOEL technique, a series of experiments were conducted using the CODEBRIM dataset. The experimental validation of the ADD-MSGOEL technique portrayed a superior accuracy value of 97.59% over existing models. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Computational platforms and environments Damage Detection Visually Impaired People Social Life and Daily Activities CLAHE Ensemble Learning Seagull Optimizer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 23 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 10 Apr, 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. We do this by developing innovative software and high quality services for the global research community. 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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-5559574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443420377,"identity":"5c2a5c92-5931-4a04-a182-bb2cb33299f6","order_by":0,"name":"Sana Alazwari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdgjFw8DAfADEYGwgqIUZroUtsYEkLSBdhsRp4W9mfvyZp+aeDAP/me+PeRhsZDccYH/4AZ8WicNsBsY8x4p5GCRyNzbzMKQZbzjAYyyB15rDDAbJOWwJQC28IC2HE4FaGPBqkT/M/uFwzj+gFv4zD4Fa/gO1sD/+gU+LwWEew+bcNqAWhhxGoJYDQC0MZnhtMTzMU8z8ty+Bh00izXDmHINk45mHecws8GmRO96++eOMbwn2/PyHH3x4U2En23e8/fENfFrggA3iTgbkmBoFo2AUjIJRQC4AACEzQzbiMxoJAAAAAElFTkSuQmCC","orcid":"","institution":"Taif University","correspondingAuthor":true,"prefix":"","firstName":"Sana","middleName":"","lastName":"Alazwari","suffix":""},{"id":443420378,"identity":"de4debc6-5390-48fa-8077-8a3d48454026","order_by":1,"name":"Hussah Nasser AlEisa","email":"","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Hussah","middleName":"Nasser","lastName":"AlEisa","suffix":""},{"id":443420379,"identity":"45717ae5-884d-471b-836e-02eddd56445c","order_by":2,"name":"Mohammed Rizwanullah","email":"","orcid":"","institution":"Prince Sattam bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Rizwanullah","suffix":""},{"id":443420380,"identity":"94b21e3a-5329-4fb4-9a0a-a17599e24faa","order_by":3,"name":"Radwa Marzouk","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"prefix":"","firstName":"Radwa","middleName":"","lastName":"Marzouk","suffix":""}],"badges":[],"createdAt":"2024-12-01 17:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5559574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5559574/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-99849-1","type":"published","date":"2025-05-13T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067763,"identity":"0487755b-c030-450b-a733-873d8e862b95","added_by":"auto","created_at":"2025-05-19 16:05:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1695611,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript20250410T192525.2981.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5559574/v1_covered_e4e5ac7e-e876-425a-9a49-57415fd4879c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Smart Assistance with Enhancing Social Interaction and Daily Activities for Visually Impaired Individuals using Deep Learning with Modified Seagull Optimization","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":"Damage Detection, Visually Impaired People, Social Life and Daily Activities, CLAHE, Ensemble Learning, Seagull Optimizer","lastPublishedDoi":"10.21203/rs.3.rs-5559574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5559574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVisually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. These bridges, designed to withstand diverse loads such as seismic forces, traffic weight, and environmental factors, are significant for maintaining structural integrity. Damage detection comprises applying advanced structural health monitoring methods to identify and assess potential deterioration or damage in concrete bridge components. Machine learning (ML) models, pattern detection, and statistical analysis are extensively adopted to identify subtle changes and process sensor information in structural response that might indicate corrosion, cracks, or other structural problems. Earlier detection and continuous monitoring of damage enable prompt intervention, ensuring longevity and safety while reducing the need for extensive repairs or the risk of unexpected failures. This study proposes an Automated Damage Detection using a Modified Seagull Optimizer with Ensemble Learning (ADD-MSGOEL) method for visually impaired people. The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. Initially, the ADD-MSGOEL method utilizes contrast enhancement (CLAHE) to enhance the image quality. Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. At last, an ensemble of three models, namely long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and sparse autoencoder (SAE) models, are implemented for the classification and detection of the damages. To demonstrate the effectiveness of the ADD-MSGOEL technique, a series of experiments were conducted using the CODEBRIM dataset. The experimental validation of the ADD-MSGOEL technique portrayed a superior accuracy value of 97.59% over existing models.\u003c/p\u003e","manuscriptTitle":"Advanced Smart Assistance with Enhancing Social Interaction and Daily Activities for Visually Impaired Individuals using Deep Learning with Modified Seagull Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 07:55:06","doi":"10.21203/rs.3.rs-5559574/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-23T06:27:39+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"19915693930519533365812609126208898281","date":"2025-04-20T21:41:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T08:29:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278739582870919681774461578382640813161","date":"2025-04-17T08:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T16:57:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T04:39:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-10T16:29:55+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"8b6e3fac-61cd-4ec1-8f9a-9c0544c161fb","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47210837,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":47210838,"name":"Biological sciences/Computational biology and bioinformatics/Computational platforms and environments"}],"tags":[],"updatedAt":"2025-05-19T16:00:02+00:00","versionOfRecord":{"articleIdentity":"rs-5559574","link":"https://doi.org/10.1038/s41598-025-99849-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-13 15:57:20","publishedOnDateReadable":"May 13th, 2025"},"versionCreatedAt":"2025-04-16 07:55:06","video":"","vorDoi":"10.1038/s41598-025-99849-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-99849-1","workflowStages":[]},"version":"v1","identity":"rs-5559574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5559574","identity":"rs-5559574","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}