Prediction results of individual models. by Ahmed M. Elshewey (21463867)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

Wilcoxon test results for feature selection. by Amal H. Alharbi (21755906)

Feature selection metrics and their definitions. by Amal H. Alharbi (21755906)

Statistical summary of all models. by Amal H. Alharbi (21755906)

Feature selection results. by Amal H. Alharbi (21755906)

ANOVA test for feature selection. by Amal H. Alharbi (21755906)

Classification performance of ML and DL models. by Amal H. Alharbi (21755906)

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files by Suchismita Behera (22027316)

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Comparison in terms of the sensitivity. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

Parameter sensitivity of BIMGO. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

Details of the medical datasets. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

The flowchart of IMGO. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”