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dc.contributor.authorAboamer, Mohamed Abdelkader
dc.contributor.authorSikkandar, Mohamed Yacin
dc.contributor.authorGupta, Sachin
dc.contributor.authorVives, Luis
dc.contributor.authorJoshi, Kapil
dc.contributor.authorOmarov, Batyrkhan
dc.contributor.authorSingh, Sitesh Kumar
dc.date.accessioned2022-07-10T16:25:12Z
dc.date.available2022-07-10T16:25:12Z
dc.date.issued2022-01-01
dc.identifier.issn01469428
dc.identifier.doi10.1155/2022/5845870
dc.identifier.urihttp://hdl.handle.net/10757/660276
dc.description.abstractDeep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, "convolutional neural network"or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0-7), filters (10-40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10-12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherHindawi Limitedes_PE
dc.relation.urlhttps://www.hindawi.com/journals/jfq/2022/5845870/es_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRepositorio Academico - UPCes_PE
dc.sourceUniversidad Peruana de Ciencias Aplicadas (UPC)es_PE
dc.subjectBiological organses_PE
dc.subjectConvolutional neural networkses_PE
dc.subjectDeep learninges_PE
dc.subjectDiseaseses_PE
dc.subjectForecastinges_PE
dc.subjectImage enhancementes_PE
dc.subjectPixelses_PE
dc.subjectRegression analysises_PE
dc.titleAn Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNNes_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.eissn17454557
dc.identifier.journalJournal of Food Qualityes_PE
dc.type.articleinfo:eu-repo/semantics/articlees_PE
dc.description.peerreviewRevisión por pareses_PE
dc.identifier.eid2-s2.0-85130400017
dc.identifier.scopusidSCOPUS_ID:85130400017
dc.source.journaltitleJournal of Food Quality
dc.source.volume2022
refterms.dateFOA2022-07-10T16:25:13Z
dc.identifier.isni0000 0001 2196 144X


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