Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet

Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet | 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 Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet Epiphany Jebamalar Leavline, Vijayakanth Krishnasamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4196526/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 A human behaviour imitation in the computing industry can be obtained using Neural Network architectures. One of the popular and influential neural network architecture is multilayer back propagation network (MLBPN). This paper aims to design and implement the MLBPN model for analyzing medical images. Many earlier research works have focused on implementing machine and deep learning models for analyzing medical data. But the computational speed is less and requires more epochs to obtain accurate results. Some researchers have overcome the challenges by using different integrated circuit techniques which are not cost-effective. This paper implements the multi layer perceptron (MLP) architecture using a double gate metal oxide semiconductor field effect transistor (DGMOSFET) model. The physical and logical functionalities of the MOSFET are integrated with MLP to provide the similar electrical behaviour of the transistor to control the current and voltage. The proposed MLP with DG-MOSFET model is trained with datasets having mixed of disease cases which were taken from FDA-NCI Clinical Proteomics Program Databank and the testing process is evaluated. The simulation is carried out with MATLAB and Cadence software, and then the analysed values are compared with actual values. The output proves that the speed and accuracy are improved compared with single gate devices. Multilayer back propagation network Double Gate MOSFET ANN Simulation Multi-Layer Perceptron Full Text Additional Declarations No competing interests reported. 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-4196526","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310010193,"identity":"a0347898-b057-41d0-8018-4b55c28ed532","order_by":0,"name":"Epiphany Jebamalar Leavline","email":"","orcid":"","institution":"University College of Engineering, Anna University","correspondingAuthor":false,"prefix":"","firstName":"Epiphany","middleName":"Jebamalar","lastName":"Leavline","suffix":""},{"id":310010194,"identity":"657b5d1d-8afb-4f58-9809-91b4a37ae993","order_by":1,"name":"Vijayakanth Krishnasamy","email":"data:image/png;base64,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","orcid":"","institution":"Dhanalakshmi Srinivasan Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Vijayakanth","middleName":"","lastName":"Krishnasamy","suffix":""}],"badges":[],"createdAt":"2024-03-31 16:44:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4196526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4196526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59646963,"identity":"d6e4426e-8ca3-45e7-ba2b-8cbfbfc71876","added_by":"auto","created_at":"2024-07-04 09:00:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":473065,"visible":true,"origin":"","legend":"","description":"","filename":"PAPER2093402revised2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4196526/v1_covered_8fcd4b75-f35d-486c-83a5-5da75e9af979.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"[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":"Multilayer back propagation network, Double Gate MOSFET, ANN Simulation, Multi-Layer Perceptron","lastPublishedDoi":"10.21203/rs.3.rs-4196526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4196526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA human behaviour imitation in the computing industry can be obtained using Neural Network architectures. One of the popular and influential neural network architecture is multilayer back propagation network (MLBPN). This paper aims to design and implement the MLBPN model for analyzing medical images. Many earlier research works have focused on implementing machine and deep learning models for analyzing medical data. But the computational speed is less and requires more epochs to obtain accurate results. Some researchers have overcome the challenges by using different integrated circuit techniques which are not cost-effective. This paper implements the multi layer perceptron (MLP) architecture using a double gate metal oxide semiconductor field effect transistor (DGMOSFET) model. The physical and logical functionalities of the MOSFET are integrated with MLP to provide the similar electrical behaviour of the transistor to control the current and voltage. The proposed MLP with DG-MOSFET model is trained with datasets having mixed of disease cases which were taken from FDA-NCI Clinical Proteomics Program Databank and the testing process is evaluated. The simulation is carried out with MATLAB and Cadence software, and then the analysed values are compared with actual values. The output proves that the speed and accuracy are improved compared with single gate devices.\u003c/p\u003e","manuscriptTitle":"Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 18:24:12","doi":"10.21203/rs.3.rs-4196526/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":"f9586d46-a477-44b1-993e-5a1d338fc5f3","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-04T09:00:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-05 18:24:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4196526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4196526","identity":"rs-4196526","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}