0000000000641559

AUTHOR

Minas E. Lemonis

showing 3 related works from this author

Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks

2021

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, …

Male0304 Medicinal and Biomolecular Chemistry 0601 Biochemistry and Cell Biology 1103 Clinical SciencesBiochemistry & Molecular BiologyGreeceModels GeneticThrombomodulinCOVID-19Complement System ProteinsCell BiologyMiddle AgedPolymorphism Single NucleotideHospitalizationSettore ICAR/09 - Tecnica Delle CostruzioniIntensive Care UnitsComplement Factor HHumansMolecular MedicineFemaleNeural Networks ComputerMorbidityartificial intelligence complement complement inhibition COVID-19 genetic susceptibility SARS-CoV2Complement ActivationJournal of Cellular and Molecular Medicine
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Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes

2021

An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly availa…

Building constructionCCFSThybridpredictionBuilding and ConstructionCCFST; hybrid; prediction; FFA; DE; FSDESettore ICAR/09 - Tecnica Delle CostruzioniFSCCFST DE FFA FS Hybrid PredictionArchitectureFFATH1-9745Civil and Structural Engineering
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Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

2022

In this study, a model for the estimation of the compressive strength of concretes incorporating metakaolin is developed and parametrically evaluated, using soft computing techniques. Metakaolin is a component extensively employed in recent decades as a means to reduce the requirement for cement in concrete. For the proposed models, six parameters are accounted for as input data. These are the age at testing, the metakaolin percentage in relation to the total binder, the water-to-binder ratio, the percentage of superplasticizer, the binder to sand ratio and the coarse to fine aggregate ratio. For training and verification of the developed models a database of 867 experimental specimens has …

Settore ICAR/09 - Tecnica Delle CostruzioniGeneral Materials ScienceBuilding and ConstructionArtificial neural networks Compressive strength Concrete Machine learning Metakaolin Mix designCivil and Structural Engineering
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