Classification Report for Traditional Classification Models. حسب Jacqueline Jia Hsin Hu (22783501)

الموضوعات:

Summary of recent Alzheimer’s classification studies. حسب Muhammad Zubair (728141)

الموضوعات:

Classification model parameter settings. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

International Classification of Diseases Codes. حسب Jie Zhao (49409)

الموضوعات:

ROC curve for the proposed Classification model across AD, MCI, and CN. حسب Muhammad Zubair (728141)

الموضوعات:

Proposed hybrid framework for Alzheimer’s disease classification. حسب Muhammad Zubair (728141)

الموضوعات:

The classification reports for experiments with tweet-level classification models. حسب Ercan Canhasi (17457899)

الموضوعات:

Probability of correct classification for classification models with different SNRs. حسب Haoran Yin (5793305)

الموضوعات:

Classification of TVDI effector interactions. حسب Yuxin Wei (11510974)

الموضوعات:

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

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

Confusion Metrics: Presenting classification results on the test set. حسب Muhammad Zubair (728141)

الموضوعات:

The influence of training-time noise on classification accuracy and reliability حسب Eloy Geenjaar (21533195)

الموضوعات:

PLS-DA classification. حسب Jie Zeng (315334)

الموضوعات: "…vanillylmandelic acid increased…"

<i>AVI</i> classification. حسب Bo Tang (23472)

"…The findings of this research are as follows: Firstly, the sensitivity index and resilience index of the atmospheric environment of the PRD exhibit an overall upward trend with fluctuations, while the exposure index demonstrates a pattern of initial increase, followed by a decrease, and subsequent increase with significant interannual variability. …"

Comparison of classification models. حسب Mark Mets (18452224)

الموضوعات:

Accuracies of the classification models. حسب Semyon K. Kolmykov (7326089)

"…<p>Accuracies of the classification models.</p>…"

Models’ classification report. حسب Muhammad Muzzammil Auzine (18880720)

Best classification models. حسب Marie Arsalidou (8053202)

الموضوعات:

The performance of the classification models. حسب Yuki Shimizu (2545252)

Classification results of models. حسب Xu Yang (112496)

"…Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the principle of the three base colours, the R, G and B bands which have more characteristic information are selected to reconstruct the images with different texture and colour characteristics to generate a new dataset for seed segmentation, and finally, a comparison is made with the classification effect of the seven models. The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. …"