Time-based splitting strategy and forecast horizons. by Thierry Jean (20691795)

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Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power.... by Medhat A. Tawfeek (22522087)

“…The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. …”

Training Data/Validation/Test. by Mudhafar Jalil Jassim Ghrabat (22177655)

“…The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. …”

Data correlated and used in the study. by Joseph Kagabo (20405114)

“…Analysis of collected data was performed using Xgboost (gain) and Random Forest (mean decrease in accuracy), machine learning techniques, to construct models that evaluate and categorize the importance of all physico-chemical properties on the presence and abundance of intermediate host snails (IHS).…”

Data Sheet 1_Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned.pdf by Marketa Ciharova (8991782)

“…Stress monitoring may be supported by valid and reliable machine-learning algorithms. However, investigation of algorithms detecting stress severity on a continuous scale is missing due to high demands on data quality for such analyses. …”

Data Preprocessing Steps for IDC Dataset. by Mudhafar Jalil Jassim Ghrabat (22177655)

“…The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. …”

Saliency maps were employed in order to elucidate which part of the B-scan images the machine learning focused on during training in Model 1. by Taku Toyama (102864)

Learn!Bio study: Grouping of Participants. by Katy Andrews (22311170)

“…This study aimed to evaluate bioscience students’ ability to adjust to a fast-evolving learning environment and to capture students’ journey building up resilience and graduate attributes. …”

Fig 8 - by Abdolhossein Boali (19996390)

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Fig 5 - by Abdolhossein Boali (19996390)

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The maps illustrating wind erosion in northeastern Iran for 2008 and 2023, generated using various models from the SDM package. by Abdolhossein Boali (19996390)

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Importance of variables in the study area in 2023, generated in the R 4.3.2 software (www.r-project.org) and Microsoft Office Excel 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

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Fig 1 - by Abdolhossein Boali (19996390)

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Wind erosion monitoring during the 15 years (2008–2023) using the ensemble model, generated in the ArcGIS 10.7.1 software (www.esri.com) and R 4.3.2 software (www.r-project.org). by Abdolhossein Boali (19996390)

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Methodological flowchart for this study, generated in Microsoft Office Word 2023 (https://www.microsof.com). by Abdolhossein Boali (19996390)

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Fig 4 - by Abdolhossein Boali (19996390)

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Social change: No decrease in singing rate upon first-time social isolation in the same environment. by Anja T. Zai (9225852)

Barbie audit data. by Cylie Williams (3446018)

Effectiveness of the proposed method to decrease chloroplast contamination. by Gabriela Madrid (11871401)

Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreas... by Eric Wang (30587)