Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Representation of the feature selection algorithm. by Betty Wutzl (6945248)

Schematic view of the overall algorithm. by Betty Wutzl (6945248)

Dataset 2: Zip file containing the Tables of the presented method and results by Suchismita Behera (22027316)

“…<p dir="ltr">Tables present the experimental results for hyperspectral image (HSI) band selection using the Enhanced Binary Jaya Algorithm (EBJA) with mutation operator, followed by classification using the k-Nearest Neighbors (k-NN) classifier. …”

Illustration of unprocessed-image analysis and cropped-image analysis by the algorithm. by Seung Seog Han (4782486)

Effectiveness of image augmentation operations. by Hailang Li (12557543)

Min-Cut/Max-Flow Problem Instances for Benchmarking by Patrick Møller Jensen (7232042)

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

“…<p dir="ltr">Figures represented here illustrates the <b>metaheuristic-based band selection framework</b> for hyperspectral image classification using <b>Binary Jaya Algorithm enhanced with a mutation operator</b> to improve population diversity and avoid premature convergence. …”

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

UNet++ type architecture [68] used for all the problems in this paper. by Dylan D. Ray (3741823)

Visual representation of the combination of feature extraction approaches employed and classification algorithms to identify stomata. by Alexandre H. Aono (11603934)

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Confusion metrics using LR-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. …”

Confusion metrics using MNB-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. …”

Confusion metrics using DT-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. …”

Confusion metrics using SVM-HaPi algorithm. by Akib Mohi Ud Din Khanday (19065631)

“…All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. …”

Dataset selection process and exclusion criteria. by Seung Seog Han (4782486)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

The tissue detection pipeline. by Andrea Bizzego (6519749)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…The proposed multilabel approaches convert the original 8-class problem into a set of three binary problems to facilitate the use of the CSP algorithm. …”