Optimized ODA-IAC model flow. by Sheng Li (107261)

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Dengue virus infection significantly increases cellular fatty acid contents. by Li Xu (108702)

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Improving cross-regional model transfer performance in crop classification by crop time series correction by Hengbin Wang (1348125)

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System performance metrics for tumor classification. by Abubakar Nadeem (20501386)

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Classification results for seed. by Xu Yang (112496)

“…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. …”

Classification results of six heterogeneities using different network models. by Fulong Liu (22614787)

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Hybrid-CS HSI tree classification model. by Jian Wang (5901)

Illustration of classification model and density map results. by Seonghwan Kim (758970)

Classification accuracy of tree species with different models (%). by Jian Wang (5901)

IDC dataset performance comparison with various existing models. by Muhammad Haris Abid (16469824)

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Performance of the five models on tissue classification inside ulcerations. by Che Wei Chang (12101782)

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Tissue classification from models with different labeling methods. by Che Wei Chang (12101782)

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Image confusion matrix results of two models. by Sheng Li (107261)

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Comparison of the classification performance of S²MMAM and five other semi-supervised medical image classification models. by Yuting Xue (8181303)

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Statistically significant linear increase in use of psychostimulants and nootropic drugs, anxiolytics, sedatives and hypnotics, gabapentinoids during the observed time period. by Jelena Filimonovic (22305307)

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Comparative analysis of image classification techniques. by Ashrafun Zannat (21807747)

The representation of DeepLens Classification and Detection Model (DCDM) architecture. by Asim Khan (1416178)

CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS by ÉRIKA BEATRIZ DE LIMA CASTRO (14119929)

“…<div><p>ABSTRACT Image analysis combined with machine learning models can be an excellent tool for classification of fava (Phaseolus lunatus L.) genotypes and is a low-cost system. …”

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification by Busra Emir (9930280)

“…<div><p>ABSTRACT Purpose: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels. …”

GM Mask image count after preprocessing. by Muhammad Zubair (728141)

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