Particle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets

Particle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets | 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 Particle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets Clement Nyirenda, Taahir A. Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4729616/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 problem of vehicle hijacking is increasingly prevalent in many parts of the world, including South Africa, causing travelers to live in constant fear of becoming victims. To address this, this work employs the usage of social media data to identify hijacking incidents in urban environments by using the city of Cape Town in South Africa as the case study. More specifically, the incorporated data is obtained from the Twitter social media platform. However, this form of data tends to be informal and unstructured. Hence, this led to the issue of data relevance, which was addressed by using Machine Learning algorithms to classify tweets. Two deep learning algorithms were employed: a Convolutional Neural Network (CNN), and a Multi-layer Feed-forward Neural Network (MLFNN). However, due to model complexity, Particle Swarm Optimization (PSO) was also implemented to produce the model variations, PSOCNN and PSOMLFNN. It was found that the PSO-optimized models outperformed the standard non-PSO models. The PSOMLFNN approach was also identified to be the best-performing model amongst the employed selection by obtaining an accuracy of 96.15%. Hence, it was successfully employed along with the Tweet data to produce vehicle hijacking maps. In future work, further machine learning and optimization algorithms will be tested to identify the best-performing algorithms for this task as well as extend the hijacking maps to a mobile application for drivers on the South African roads. Artificial Intelligence and Machine Learning Machine Learning Hijacking Supervised Learning Particle Swarm Optimization Convolutional Multi- Layer Feed-Forward 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-4729616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326098384,"identity":"981b6de5-8404-463e-bfda-6ac06adc0d89","order_by":0,"name":"Clement Nyirenda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJCCA0DM2MDeQLIWngMk2sTYIJFApFL+2T2Ghysq7sj2z3xjJvGDwU6egf/wA7xaJO6cMTh45swz4xm3c8wkexiSDRsYjhng1WIgkWNwsLHtcGIDUMttBgbmBKAbidHy73Di/JtnQFrqExiY2T8QoaXhcOKGGzwgLYcTGNh48NsicSOt4GDDscPGG8+klf/sMThu2MbDU4BXC/+M5M0fG2oOy847fnizwY+Kanl+/uMb8GphYOBAdgaQzUZAPRCwPyCsZhSMglEwCkY2AAAgMkjYLdcmpAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4181-0478","institution":"University of the Western Cape","correspondingAuthor":true,"prefix":"","firstName":"Clement","middleName":"","lastName":"Nyirenda","suffix":""},{"id":326098714,"identity":"5d444772-703d-408f-b83c-a9da0c811871","order_by":1,"name":"Taahir A. Patel","email":"","orcid":"","institution":"University of the Western Cape","correspondingAuthor":false,"prefix":"","firstName":"Taahir","middleName":"A.","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2024-07-12 10:31:44","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-4729616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4729616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60272946,"identity":"d9c4a1bb-a434-4b24-bb41-5bd581a761a7","added_by":"auto","created_at":"2024-07-15 04:10:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":385005,"visible":true,"origin":"","legend":"","description":"","filename":"IEEEPSOPaper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4729616/v1_covered_2541bbe2-c40b-40c0-8258-ce4ff7dc0be7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eParticle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of the Western Cape","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":"Machine Learning, Hijacking, Supervised Learning, Particle Swarm Optimization, Convolutional, Multi- Layer Feed-Forward","lastPublishedDoi":"10.21203/rs.3.rs-4729616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4729616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe problem of vehicle hijacking is increasingly prevalent in many parts of the world, including South Africa, causing travelers to live in constant fear of becoming victims. To address this, this work employs the usage of social media data to identify hijacking incidents in urban environments by using the city of Cape Town in South Africa as the case study. More specifically, the incorporated data is obtained from the Twitter social media platform. However, this form of data tends to be informal and unstructured. Hence, this led to the issue of data relevance, which was addressed by using Machine Learning algorithms to classify tweets. Two deep learning algorithms were employed: a Convolutional Neural Network (CNN), and a Multi-layer Feed-forward Neural Network (MLFNN). However, due to model complexity, Particle Swarm Optimization (PSO) was also implemented to produce the model variations, PSOCNN and PSOMLFNN. It was found that the PSO-optimized models outperformed the standard non-PSO models. The PSOMLFNN approach was also identified to be the best-performing model amongst the employed selection by obtaining an accuracy of 96.15%. Hence, it was successfully employed along with the Tweet data to produce vehicle hijacking maps. In future work, further machine learning and optimization algorithms will be tested to identify the best-performing algorithms for this task as well as extend the hijacking maps to a mobile application for drivers on the South African roads.\u003c/p\u003e","manuscriptTitle":"Particle Swarm Optimization of Deep Learning Models for the Determination of Vehicle Hijacking Tweets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 04:02:29","doi":"10.21203/rs.3.rs-4729616/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"c1857348-e8b5-4219-885c-ac21a3a3f7fd","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34506538,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-07-15T04:02:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 04:02:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4729616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4729616","identity":"rs-4729616","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}