Displacement cloud maps. by Maogang Tian (21485116)

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Displacement cloud map (geology). by Hui Cheng (195456)

“…The primary trend indicates an increase in clay content of loess, a decrease in grit content, a relative increase in cohesion, and a relative decrease in the internal friction angle. …”

The top 10 putative metabolites with the largest negative fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

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The top 10 putative metabolites with the largest positive fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

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CNN Model Architecture. by Mudhafar Jalil Jassim Ghrabat (22177655)

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Flowchart of Proposed SMO_CNN. by Mudhafar Jalil Jassim Ghrabat (22177655)

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CNN Model Layers Summary. by Mudhafar Jalil Jassim Ghrabat (22177655)

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Point cloud fusion instance effect. by Hongliang Zou (20707270)

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Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 1. by Hongliang Zou (20707270)

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Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 6. by Hongliang Zou (20707270)

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Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 5. by Hongliang Zou (20707270)

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Performance Results of Proposed SMO_CNN Model. by Mudhafar Jalil Jassim Ghrabat (22177655)

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CNN framework. by Sajid Mehmood (6689843)

“…These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. …”

CNN frameworks. by Sajid Mehmood (6689843)

“…These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. …”

CNN model. by Longfei Gao (698900)

“…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”

Differential miRNA target gene pathway enrichment bubble map. by Zhaowen Tan (12283502)

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Architecture of the proposed shallow CNN. by Shruti Atul Mali (21300851)

“…Classification analysis revealed that ComBat increased average AUC by 15.19%, whereas GAN decreased AUC by 2.56%.</p><p>Conclusion</p><p>While GAN qualitatively enhances image harmonization, ComBat provides superior statistical improvements in feature stability and classification performance, highlighting the importance of robust feature-level harmonization in radiomics.…”

Mitigation (decreasing) scenario. by Fabio Correddu (12501799)

CNN-LSTM action recognition process. by Longfei Gao (698900)

“…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”

Thermoneutrality decreased sepsis survival. by Goldia Chan (21453340)