Convergence speed of five algorithms. by 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. …”

Pictorial representation of isomorphically scaling down spots within a fixed field of view and corresponding imaging configuration. by Nicholas H. Vitale (20469289)

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

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

Decoding results. by Sam Hall-McMaster (10343795)

Behavioral results. by Sam Hall-McMaster (10343795)

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”

Limitations of clustering approaches. by Katharina Waury (14203284)

Subjects:

System no-load torque response results under three control algorithms. by Haoran Cui (7235234)

Subjects:

CEC2017 basic functions. by 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)). …”

Both Ankle fNIRS MI dataset by Hammad Gilani (8060012)

“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

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

Subjects:

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

Subjects:

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

Subjects: