Linear kernel: Power and Type I error for cluster evaluation metric simulations. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Examples of between- and within-community Beta distributions. حسب Alexandria M. Jensen (6494957)

الموضوعات:

P-values from analysis of ABIDE dataset for binary outcome of diagnostic group. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Examples of four simulated networks and adjacency matrices. حسب Alexandria M. Jensen (6494957)

الموضوعات:

P-values from analysis of ABIDE dataset for continuous outcome of VIQ. حسب Alexandria M. Jensen (6494957)

الموضوعات:

Code snippets to add user-specific custom functionality in PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

The benchmark functions. حسب Shuhui Yu (1535500)

Study proposed algorithm. حسب Ainhoa Pérez-Guerrero (21377457)

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"

Gillespie algorithm simulation parameters. حسب Nicholas H. Vitale (20469289)

"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"

Scheduling time of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Convergence speed of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

pone.0331193.t003 - حسب Carol Kotliar (22351525)

الموضوعات:

Baseline characteristics by group. حسب Carol Kotliar (22351525)

الموضوعات:

Modular architecture design of PyNoetic showing all its constituent functions. حسب Gursimran Singh (575288)

الموضوعات:

Spatial characterization and cell-type-specific genes in mouse brain tissue. حسب Huamei Li (8815955)

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases حسب Maru Song (22593561)

"…Crucially, we propose fitness functions based on approximate measures of the wave function compactness, which enable inexpensive genetic algorithm searches. …"

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. حسب Dovilė Kurmanavičiūtė (21072549)

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network حسب Rajesh Kumar Thangavel (20591164)

"…<p>Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. …"

Multi-algorithm comparison figure. حسب Dawei Wang (471687)

"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"

CEC2017 basic functions. حسب Tengfei Ma (597633)

"…During experimental evaluation, the efficiency of OP-ZOA was verified using the CEC2017 test functions, demonstrating superior performance compared to seven recently proposed meta-heuristic algorithms (Bloodsucking Leech Algorithm (BSLO), Parrot Optimization Algorithm (PO), Polar Lights Algorithm (PLO), Red-tailed Hawk Optimization Algorithm (RTH), Bitterling Fish Optimization Algorithm (BFO), Spider Wasp Optimization Algorithm (SWO) and Zebra Optimization Algorithm (ZOA)). …"