COVID-19 and human development: An approach for classification of HDI with deep CNN

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dc.authoridKavuran, Gurkan/0000-0003-2651-5005
dc.authoridYeroglu, Celaleddin/0000-0002-6106-2374
dc.authorwosidKavuran, Gurkan/S-6935-2016
dc.contributor.authorKavuran, Gurkan
dc.contributor.authorGokhan, Seyma
dc.contributor.authorYeroglu, Celaleddin
dc.date.accessioned2024-08-04T20:53:14Z
dc.date.available2024-08-04T20:53:14Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe measures taken during the pandemic have had lasting effects on people's lives and perceptions of the ability of national and multilateral institutions to drive human development. Policies that changed people's behavior were at the heart of containing the spread of the virus. As a result, it has become a systemic human development crisis affecting health, the economy, education, social life, and accumulated gains. This study shows how the relationship of the Human Development Index (HDI), which has combined effects on health, education, and the economy, should be considered in the context of pandemic factors. First, COVID-19 data of the countries received from a public and credible source were extracted and organized into an acceptable structure. Then, we applied statistical feature selection to determine which variables are closely related to HDI and enabled the Deep Con-volutional Neural Network (DCNN) model to give more accurate results. The Continuous Wavelet Transform (CWT) and scalogram methods were used for the time-series data visualization. Three different images of each country are combined into a single image to penetrate each other for ease of processing. These images were made suitable for the input of the ResNet-50 network, which is a pre-trained DCNN model, by going through various preprocessing processes. After the training and validation processes, the feature vectors in the fc1000 layer of the network were drawn and given to the Support Vector Machine Classifier (SVMC) input. We achieved total performance metrics of specificity (88.2%), sensitivity (96.5%), precision (99%), F1 Score (94.9%) and MCC (85.9%).en_US
dc.description.sponsorshipInonu University Scientific Research Projects Management Unit [FYL-2021-2377]en_US
dc.description.sponsorshipThis study was funded by Inonu University Scientific Research Projects Management Unit with the project number FYL-2021-2377.en_US
dc.identifier.doi10.1016/j.bspc.2022.104499
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.pmid36530217en_US
dc.identifier.scopus2-s2.0-85143866657en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.104499
dc.identifier.urihttps://hdl.handle.net/11616/101055
dc.identifier.volume81en_US
dc.identifier.wosWOS:000898625200009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHuman Development Indexen_US
dc.subjectDeep learningen_US
dc.subjectCOVID-19en_US
dc.subjectContinuous wavelet transformen_US
dc.subjectArtificial intelligenceen_US
dc.subjectClassificationen_US
dc.titleCOVID-19 and human development: An approach for classification of HDI with deep CNNen_US
dc.typeArticleen_US

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