Comparison of deep partitioning models. حسب Hui Yang (91136)

Projection weight distribution. حسب Hui Yang (91136)

Horizontal comparison of models. حسب Hui Yang (91136)

Adaptive model BD rate analysis. حسب Hui Yang (91136)

Index analysis of deep partitioning strategy. حسب Hui Yang (91136)

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' حسب Fanzhi Su (21005174)

Example of time-frequency representation of sEMG signal. حسب Francesco Di Nardo (734450)

<b>The abundance of ground-level atmospheric ice-nucleating particles and aerosol properties </b><b>in the North Slope of Alaska</b> حسب Aidan Pantoya (19416546)

"…It is worth noting that we also flag the data based on wind speed and number concentration, and data mentor edits. The algorithm we use is described in this paper (Sheridan et al., 2016). …"

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery حسب M RN (9866504)

In each subplot we show in yellow the (vertical) for arm-2 as a function of the number of SNPs corresponding to each -threshold (horizontal, log-scale). حسب Caroline C. McGrouther (5216153)

Kleptographic Attacks Against Implicit Rejection حسب Antoine Joux (17288464)

Performance comparison of different clustering strategies. حسب Katharina Waury (14203284)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> حسب BRISC Dataset (22559540)

"…It provides high-quality, physician-validated pixel-level masks and a balanced multi-class classification split, suitable for benchmarking segmentation and classification algorithms as well as multi-task learning research.…"

Factors influencing the discovery of new genetic signatures. حسب Wei Liu (20030)

Ablation study visualization results. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Experimental parameter configuration. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

FLMP-YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

C2f structure. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Experimental environment configuration. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Ablation experiment results table. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"