Results 91 to 100 of about 38,671 (260)
Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization. [PDF]
Mangalampalli S +7 more
europepmc +1 more source
Objective The diagnosis of fibromyalgia (FM) is challenging due to the absence of definitive biomarkers, numerous overlapping comorbidities, and its reliance on patient‐reported symptoms. Discrepancies between diagnostic criteria and clinical practice imply the possibility of diagnostic biases, complicating timely and accurate identification.
Sung‐A Kim +2 more
wiley +1 more source
A task scheduling algorithm with deadline constraints for distributed clouds in smart cities. [PDF]
Zhou J, Liu B, Gao J.
europepmc +1 more source
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein +3 more
wiley +1 more source
Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing. [PDF]
Mangalampalli S +7 more
europepmc +1 more source
Objective We examined associations of post‐traumatic stress disorder (PTSD) and other mental health disorders (OMH) with rheumatoid arthritis (RA), accounting for effects of smoking. Methods We conducted a matched case‐control study, identifying incident RA cases and controls using national Veteran Health Administration data (2006‐2019).
Kelsey Coziahr +15 more
wiley +1 more source
In order to solve the problem of hybrid scheduling of multiple time-sensitive flows in time-sensitive network(TSN), a TSN joint routing and hybrid traffic scheduling algorithm based on time-aware and cyclic queued forwarding was proposed.
WANG Ying +4 more
doaj
K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization. [PDF]
Wen S +6 more
europepmc +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Retracted: Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video. [PDF]
Intelligence And Neuroscience C.
europepmc +1 more source

