An example of 2-dimensional non-linearly separable and non-separable data for binary classification. by Yuji Oyamada (11714754)

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

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. …”

Automatic Pre-Processing (APP). by Yuxiang Wu (748543)

Graphical representation of KerTAD algorithm. by Luka Maisuradze (8791229)

Illustration of the steps used to construct the mask for identifying corner regions in Hi-C maps. by Luka Maisuradze (8791229)

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

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p><br></p><p dir="ltr">In the fifth measurement technique, the numbers of sharp <b>surface projections/protrusions</b> were calculated by initially applying Canny's edge detection algorithm to generate an edge map of the cell mask image. …”

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

Sugarcane stem nodes based on the maximum value points of the vertical projection function by Jiqing Chen (616977)

“…Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. …”

Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx by Ali Nabavi (21097424)

“…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”

UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…<p dir="ltr">The objective of the UMAHand dataset is to provide a systematic, Internet-accessible benchmarking database for evaluating algorithms for the automatic identification of manual activities. …”