Graph Theory Based Large-Scale Machine Learning With Multi-Dimensional Constrained Optimization Approaches for Exact Epidemiological Modeling of Pandemic Diseases

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dc.authoridTutsoy, Onder/0000-0001-6385-3025
dc.contributor.authorTutsoy, Önder
dc.date.accessioned2025-01-06T17:44:04Z
dc.date.available2025-01-06T17:44:04Z
dc.date.issued2023
dc.description.abstractMulti-dimensional prediction models of the pandemic diseases should be constructed in a way to reflect their peculiar epidemiological characters. In this paper, a graph theory-based constrained multi-dimensional (CM) mathematical and meta-heuristic algorithms (MA) are formed to learn the unknown parameters of a large-scale epidemiological model. The specified parameter signs and the coupling parameters of the sub-models constitute the constraints of the optimization problem. In addition, magnitude constraints on the unknown parameters are imposed to proportionally weight the input-output data importance. To learn these parameters, a gradient-based CM recursive least square (CM-RLS) algorithm, and three search-based MAs; namely, the CM particle swarm optimization (CM-PSO), the CM success history-based adaptive differential evolution (CM-SHADE), and the CM-SHADEWO enriched with the whale optimization (WO) algorithms are constructed. The traditional SHADE algorithm was the winner of the 2018 IEEE congress on evolutionary computation (CEC) and its versions in this paper are modified to create more certain parameter search spaces. The results obtained under the equal conditions show that the mathematical optimization algorithm CM-RLS outperforms the MA algorithms, which is expected since it uses the available gradient information. However, the search-based CM-SHADEWO algorithm is able to capture the dominant character of the CM optimization solution and produce satisfactory estimates in the presence of the hard constraints, uncertainties and lack of gradient information.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK) [121E628]
dc.description.sponsorshipThis research is supported by the Scientific and Technological Research Council of Turkiye (TUBITAK) under 1002 program with project under Grant 121E628. Recommended for acceptance by O. Winther.
dc.identifier.doi10.1109/TPAMI.2023.3256421
dc.identifier.endpage9845
dc.identifier.issn0162-8828
dc.identifier.issn1939-3539
dc.identifier.issue8
dc.identifier.pmid37028303
dc.identifier.scopus2-s2.0-85151396195
dc.identifier.scopusqualityQ1
dc.identifier.startpage9836
dc.identifier.urihttps://doi.org/10.1109/TPAMI.2023.3256421
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2909
dc.identifier.volume45
dc.identifier.wosWOS:001022958600037
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.ispartofIeee Transactions on Pattern Analysis and Machine Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectPredictive models
dc.subjectPandemics
dc.subjectDiseases
dc.subjectCouplings
dc.subjectMathematical models
dc.subjectCOVID-19
dc.subjectBiological system modeling
dc.subjectBig Data
dc.subjectconstrained optimization
dc.subjectpandemic
dc.subjectparametric model
dc.subjectprediction
dc.subjectlarge scale
dc.subjectmachine learning
dc.subjectmeta-heuristic algorithms
dc.subjectrecursive least squares
dc.titleGraph Theory Based Large-Scale Machine Learning With Multi-Dimensional Constrained Optimization Approaches for Exact Epidemiological Modeling of Pandemic Diseases
dc.typeArticle

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