The impact of the combination of image augmentation and feature augmentation operations. by Hailang Li (12557543)

Illustration of the four steps in constructing the point feature binary mask. by Luka Maisuradze (8791229)

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

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

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

“…The binary GWO algorithm identifies the most relevant features from </p><p dir="ltr">dermatological images, eliminating redundancy and reducing the computational burden. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

“…The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. …”

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

“…HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. …”

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

“…HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. …”

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

“…HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. …”

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

“…HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. …”

Feature statistical analysis. by Akib Mohi Ud Din Khanday (19065631)

“…HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. …”

Skeletonisation algorithm. by Jordan Hembrow (16908051)

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

“…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …”

Mean accuracies of the classifiers trained with image descriptor features for the stomata classification task. by Alexandre H. Aono (11603934)

Algorithm framework of this paper. by Xiaoqin Wu (470428)

“…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

Data_Sheet_1_Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.docx by Xueyan Zhang (277449)

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

“…In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. …”

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

“…In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. …”