Heatmap of pre-post interview changes in system insights, health equity thinking, & healthy equity action. by Travis R. Moore (19532635)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. …”

S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

“…The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. …”

Steps to the final sample. by Vittorio Nicoletta (21367878)

“…<div><p>One major obstacle to advancing research and treatment for major psychiatric disorders is their substantial within-diagnosis heterogeneity in patient lifetime trajectories. Adapted research methods such as cluster analysis to define subgroups of patients are currently used. …”

The architecture of the SE-multi-input CNN model. by Bin Zheng (45359)

“…In this work, we propose a novel arrhythmia classification algorithm based on a multi-input convolutional neural network (CNN) enhanced with a Squeeze-and-Excitation (SE) attention mechanism. …”

Confusion matrix for the Multi-input CNN model. by Bin Zheng (45359)

“…In this work, we propose a novel arrhythmia classification algorithm based on a multi-input convolutional neural network (CNN) enhanced with a Squeeze-and-Excitation (SE) attention mechanism. …”

Confusion matrices for single-input CNN models. by Bin Zheng (45359)

“…In this work, we propose a novel arrhythmia classification algorithm based on a multi-input convolutional neural network (CNN) enhanced with a Squeeze-and-Excitation (SE) attention mechanism. …”

Confusion matrix for the Multi-input CNN model. by Bin Zheng (45359)

“…In this work, we propose a novel arrhythmia classification algorithm based on a multi-input convolutional neural network (CNN) enhanced with a Squeeze-and-Excitation (SE) attention mechanism. …”

Intuitionistic fuzzy number . by Hanan Alolaiyan (18536236)

“…In addition, the practical effect of the proposed methods is also investigated in this study. A mathematical mechanism is presented to implement the t-intuitionistic fuzzy RSA algorithm. …”

Experimental message into the integer. by Hanan Alolaiyan (18536236)

“…In addition, the practical effect of the proposed methods is also investigated in this study. A mathematical mechanism is presented to implement the t-intuitionistic fuzzy RSA algorithm. …”

Triangular . by Hanan Alolaiyan (18536236)

“…In addition, the practical effect of the proposed methods is also investigated in this study. A mathematical mechanism is presented to implement the t-intuitionistic fuzzy RSA algorithm. …”

Graphical representation of triangular 0.6- . by Hanan Alolaiyan (18536236)

“…In addition, the practical effect of the proposed methods is also investigated in this study. A mathematical mechanism is presented to implement the t-intuitionistic fuzzy RSA algorithm. …”

Graphical representation of . by Hanan Alolaiyan (18536236)

“…In addition, the practical effect of the proposed methods is also investigated in this study. A mathematical mechanism is presented to implement the t-intuitionistic fuzzy RSA algorithm. …”

Secondary search process based on ‘seed’ article used to generate the citation map, adapted from Costa et al. (2025) [48]. by Ana Michell García Varela (22574587)

Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory by Drippy Stone (21218843)

Subjects:

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Main module structure. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”