MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Experimental hardware and software environment. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Classification model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Structural Diagrams of RF Model and ResNet Model. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN model convergence curve. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Structure Diagram. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCAECG-GAN K-fold experiment table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Pseudocode Table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Ablation Experiment Results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Comparison of the particular blood cell parameters across the various murine treatment groups. by Maryam Banar (3220221)

Table 1_Plant-based diets and total and cause-specific mortality: a meta-analysis of prospective studies.docx by Qiwang Mo (9091292)

“…Participants in the highest quintile of both the PDI and hPDI had a significantly decreased risk of all-cause mortality (pooled HR_PDI = 0.85; 95% CI: 0.80–0.90; pooled HR_hPDI = 0.86; 95% CI: 0.81–0.92) compared to participants in the lowest quintile. …”

Image 2_Plant-based diets and total and cause-specific mortality: a meta-analysis of prospective studies.tif by Qiwang Mo (9091292)

“…Participants in the highest quintile of both the PDI and hPDI had a significantly decreased risk of all-cause mortality (pooled HR_PDI = 0.85; 95% CI: 0.80–0.90; pooled HR_hPDI = 0.86; 95% CI: 0.81–0.92) compared to participants in the lowest quintile. …”

Image 1_Plant-based diets and total and cause-specific mortality: a meta-analysis of prospective studies.tif by Qiwang Mo (9091292)

“…Participants in the highest quintile of both the PDI and hPDI had a significantly decreased risk of all-cause mortality (pooled HR_PDI = 0.85; 95% CI: 0.80–0.90; pooled HR_hPDI = 0.86; 95% CI: 0.81–0.92) compared to participants in the lowest quintile. …”

The expression levels of tasiR-ARF3 and tasiR-ARF3 targets in empty vector (EV)-, p20-, p20 ^mAIM1 -, and p20 ^mAIM5 -infiltrated <i>Nicotiana benthamiana</... by Yongle Zhang (11355546)

Data Sheet 1_The impact of cell density variations on nanoparticle uptake across bioprinted A549 gradients.docx by Luigi Di Stolfo (21216599)

“…This inverse relationship correlated with greater average cell surface area in lower-density regions, while differences in the proliferation rates of the A549 cells at varying densities did not significantly impact uptake, did not significantly impact uptake.…”