Comparison of Average Recall Values Across Three Different Datasets. حسب Ming Gao (115719)

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Weight Distribution of Feature Words for Two Categories. حسب Ming Gao (115719)

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Comparison of Average F1 Scores Across Three Different Datasets. حسب Ming Gao (115719)

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Comparison Table of ATF-DF-NBM and TF-IDF-NBM. حسب Ming Gao (115719)

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Example of Class-typical Feature Vectors. حسب Ming Gao (115719)

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Data Categories of the Sogou Corpus. حسب Ming Gao (115719)

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Slicing aided hyper inference algorithm. حسب Chunhua Yang (346871)

"…The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution. …"

Data before and after applying SMOTE algorithm. حسب Xiangting Shi (21439579)

"…Our approach enhances comparison to existing methods of feature selection and classification accuracy. On the ECH dataset, RMGACNet achieves an accuracy of 0.9772, a precision of 0.9768, and an F1 score of 0.9770 measures; on the IEEE-CIS dataset, it achieves an accuracy of 0.9882, a precision of 0.9876 and an F1 score of 0.9879. …"

Image 1_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.tiff حسب Yixin Guo (3923948)

Image 3_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.tiff حسب Yixin Guo (3923948)

Table 2_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.docx حسب Yixin Guo (3923948)

Image 2_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.pdf حسب Yixin Guo (3923948)

Table 3_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.docx حسب Yixin Guo (3923948)

Table 1_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.docx حسب Yixin Guo (3923948)

Image 4_DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.tiff حسب Yixin Guo (3923948)

Table 1_Artificial intelligence in cataract grading system: a LOCS III-based hybrid model achieving high-precision classification.xlsx حسب Gege Tang (22211797)

This table compares the performance of RF, BR, CC, and CSMLP algorithms across various datasets and features. The evaluation uses Multi-Label Random Under-Sampling (MLRUS) and Multi-Label Random Over-Sampling (MLROS) techniques with sampling ratios of 10%, 20%, and 30%. Micro-averaged F1-scores, Precision and Recall are reported for each algorithm-dataset pair; each cell has F1 score on the top, then Precision in the middle and Recall at the bottom.... حسب Durgesh Ameta (21439753)

"…Micro-averaged F1-scores, Precision and Recall are reported for each algorithm-dataset pair; each cell has F1 score on the top, then Precision in the middle and Recall at the bottom. …"

Classification Report for the proposed model. حسب Khondokar Oliullah (21553289)

Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. حسب Daniel Suárez-Barrera (22676654)

Classification accuracies of the three models. حسب Yeping Shi (14279118)

"…In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. …"