Search results for "Predictive Model"

showing 4 items of 74 documents

Importance of the Window Function Choice for the Predictive Modelling of Memristors

2021

Window functions are widely employed in memristor models to restrict the changes of the internal state variables to specified intervals. Here, we show that the actual choice of window function is of significant importance for the predictive modelling of memristors. Using a recently formulated theory of memristor attractors, we demonstrate that whether stable fixed points exist depends on the type of window function used in the model. Our main findings are formulated in terms of two memristor attractor theorems, which apply to broad classes of memristor models. As an example of our findings, we predict the existence of stable fixed points in Biolek window function memristors and their absenc…

State variableComputer science02 engineering and technologyMemristorType (model theory)Fixed pointTopologyWindow functionlaw.inventionPredictive modelsComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologiesMathematical modellawAttractor0202 electrical engineering electronic engineering information engineeringEvolution (biology)Electrical and Electronic EngineeringPolarity (mutual inductance)threshold voltage020208 electrical & electronic engineeringmemristive systemsBiological system modeling020206 networking & telecommunicationsWindow functionmemristorsIntegrated circuit modelingPredictive modellingIEEE Transactions on Circuits and Systems Ii-Express Briefs
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CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease

2023

AbstractThis study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alon…

Predictive modelsSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRadiomic featuresCognitive NeuroscienceClinical featuresModel explainabilityComputer Vision and Pattern RecognitionPericoronaric adipose fatCoronary artery diseaseMachine learning classifiersComputer Science ApplicationsCognitive Computation
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Computation of the regime configuration of a meandering stream

2004

Regime channel formation is a delicate adaptation to the imposed environmental conditions compatible with flow and sediment transport mechanisms. The present paper concerns the computation of the regime configuration of a meandering stream. It is supposed that the channel develops until it reaches the state of “final thermodynamic equilibrium”, where the ratio of the kinetic energy of the flow to its cross-sectional potential energy is minimum. An optimization procedure that allows the analysis of the regime channel formation is presented and it is checked using a real case.

predictive modelregime configurationriver’s plane-form evolutionpredictive model; regime configuration; river’s plane-form evolution
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Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study

2021

Abstract It is still unclear how genetic information, provided as single‐nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population‐based case‐cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo); selection of the most predictive SNPs among these literature‐co…

Oncologymedicine.medical_specialtyEpidemiologyFramingham Risk Score ; Metabochip ; Coronary Heart Disease ; Genomic Risk Prediction ; Priority-lassoPopulationCoronary DiseaseSingle-nucleotide polymorphismKoronare HerzkrankheitPolymorphism Single NucleotideRisk AssessmentCohort Studies03 medical and health sciencesRisk FactorsInternal medicinemedicineHumansgenomic risk predictionddc:610coronary heart diseaseMetabochipGenetikeducationGenotypingGenetics (clinical)030304 developmental biologypriority‐Lasso0303 health scienceseducation.field_of_studyFramingham Risk ScoreModels GeneticProportional hazards modelbusiness.industry030305 genetics & heredityGenomicsConfidence intervalddc:Coronary disease; GeneticsRisk factorsCohortFramingham risk scorebusinessDDC 610 / Medicine & healthPredictive modelling
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