Table 1_Leveraging data augmentation for machine learning models in predicting depression and anxiety using the Revised Child Anxiety and Depression Scale clinical reports.docx by Saleha Noor (22697930)

“…</p>Conclusion<p>The findings suggest that the Random Forest algorithm using 46 features suits the data well and has the potential to be further developed as a decision support system for the concerned professionals and improve the usual way of screening anxiety and depression in children and adolescents.…”

Table 2_Leveraging data augmentation for machine learning models in predicting depression and anxiety using the Revised Child Anxiety and Depression Scale clinical reports.docx by Saleha Noor (22697930)

“…</p>Conclusion<p>The findings suggest that the Random Forest algorithm using 46 features suits the data well and has the potential to be further developed as a decision support system for the concerned professionals and improve the usual way of screening anxiety and depression in children and adolescents.…”

Data for Figs 3–9. by Xiaogang Li (299205)

Fig 4 - by Ashutosh P. Raman (19825936)

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Fig 1 - by Ashutosh P. Raman (19825936)

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Emission wavelength and full width at half maximum (FWHM) values for various metabolically important endogenous fluorophores after 405 nm laser excitation [30–31]. by Ashutosh P. Raman (19825936)

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Fig 2 - by Ashutosh P. Raman (19825936)

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Fig 7 - by Ashutosh P. Raman (19825936)

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Plot of 5 most important principal components (PCs), defined as those PCs making up 95% of total dataset variance together, and their respective variance intensity at each waveleng... by Ashutosh P. Raman (19825936)

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Average spectra for both tissue classes, with standard error regions. by Ashutosh P. Raman (19825936)

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Fig 9 - by Ashutosh P. Raman (19825936)

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Fig 8 - by Ashutosh P. Raman (19825936)

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Specific configuration of autofluorescence device with freshly resected tissue sample prior to laser excitation and subsequent signal acquisition. by Ashutosh P. Raman (19825936)

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Specific hyperparameters optimized for each non-ANN model in the Nested Grid Search process. by Ashutosh P. Raman (19825936)

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The results of implementing the models. by Hanieh Alimiri Dehbaghi (20405015)

“…The used machine learning algorithms are Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM. …”

Table 1_GDP prediction of The Gambia using generative adversarial networks.docx by Haruna Jallow (20772470)

Technological Fitness and Coherence explanation. Schematic representation of the workings of the Fitness and Complexity algorithm and Coherence diversification. by Matteo Straccamore (15083799)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. …”

Table 1_Early stroke detection through machine learning in the prehospital setting.xlsx by María Ríos Delgado (22004942)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

“…<div><p>A fault diagnosis method for oil immersed transformers based on principal component analysis and SSA LightGBM is proposed to address the problem of low diagnostic accuracy caused by the complexity of current oil immersed transformer faults. Firstly, data on dissolved gases in oil is collected, and a 17 dimensional fault feature matrix is constructed using the uncoded ratio method. …”