<i>AR</i> of LSSVM with different optimization algorithms. by Yang Guo (399435)

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Data Sheet 2_UrbanAgri: a transfer learning-based plant stress identification framework for sustainable smart urban growth.pdf by Upinder Kaur (10805317)

“…Based on the capabilities of transfer learning, the model makes use of optimal feature extraction with small datasets, resolving the issue of data scarcity in cities. …”

Data Sheet 1_UrbanAgri: a transfer learning-based plant stress identification framework for sustainable smart urban growth.zip by Upinder Kaur (10805317)

“…Based on the capabilities of transfer learning, the model makes use of optimal feature extraction with small datasets, resolving the issue of data scarcity in cities. …”

Table 1_Intra-tumor and peritumoral radiomics and deep learning based on ultrasound for differentiating fibroadenoma and phyllodes tumor: a multicenter study.docx by Guoxiu Lu (15393095)

“…Purpose<p>To develop and validate an integrated intra-tumoral (ITR) and peritumoral (PTR) radiomics-deep learning model based on ultrasound (US) imaging for accurately differentiating fibroadenomas (FA) from phyllodes tumors (PT) and further classifying PT into benign, borderline, and malignant subtypes.…”

Primers for RT-qPCR. by Jiaoyang Li (2862269)

“…A critical interaction site was identified within the third polyproline-arginine motif in NSP2 (⁴⁵³PVPAPR⁴⁵⁸). …”

The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.72 g/cc and DTc = 51.47 us/ft. by Fahd Saeed Alakbari (10701871)

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Error boxplot and violin graphs for the previously published and proposed GPR models. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the testing dataset. by Fahd Saeed Alakbari (10701871)

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Residual error of fracture pressure for all studied models. by Fahd Saeed Alakbari (10701871)

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Group error analysis of bulk formation density for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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Trend analysis of the ν_s – DTs in the range found in the dataset used for the optimized GPR. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s error histogram of the training dataset. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s errors histogram of the testing dataset. by Fahd Saeed Alakbari (10701871)

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Group error analysis of shear time for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.27 g/cc and DTs =  248.99 us/ft. by Fahd Saeed Alakbari (10701871)

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The optimized parameters of the proposed GPR method. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s error histogram of the validation dataset. by Fahd Saeed Alakbari (10701871)

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Cross-plots of (a) the proposed GPR, Ranjbar-Karaml et al. [14], Christaras et al. [52], Feng et al. [16], Gowida et al. [17] models and (b) Khandelwal et al. [13], Brandås et al.... by Fahd Saeed Alakbari (10701871)

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Group error analysis of compressional time for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the validation dataset. by Fahd Saeed Alakbari (10701871)

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