The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

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

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ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

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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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Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

Subjects: “…significantly higher decrease…”

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

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AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Subjects: “…levels significantly decreased…”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Subjects: “…conversion significantly decreased…”

Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Subjects: “…conversion significantly decreased…”

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. …”

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

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Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

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AUC ROC curve. by Dieuwke Luijten (15282736)

“…</p><p>Results</p><p>During follow-up, 28 patients had residual PH (42%). A decrease in VG-RVPO after PEA was associated with decrease in mPAP or indexed RV mass post PEA (r = 0.55, p < 0.05 and r = 0.64, p < 0.05, respectively). …”

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)