Application of Convolution Neural Networks and Randomforest for Software Test

Sulaiman, Nour; Hasoon, Safwan O.

doi:10.1109/iccitm56309.2022.10031789

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figures 2, 4, and 7, Figures S1 and S2 in Supporting Information S1 were made with Matplotlib version 3.5.2 (Caswell et al, 2022;Hunter, 2007), available under the Matplotlib license at https://matplotlib.org/. Statistical analysis, machine learning and Figure 6 were based on R platform (Fortmann-Roe, 2015;Liaw and Wiener, 2022;Ripley et al, 2022;Wei et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…When analyzing the relative importance of predictor variables on the area of carbon flux footprints, we used the percentage increases in the mean square error (Increase in MSE(%), %IncMSE) of variables to quantify the importance of these predictors, with higher %IncMSE values implying more important predictors (Breiman, 2001;Jiao et al, 2018). The "rfPermute" (Liaw and Wiener, 2022) package was used to complete the significance assessment. Finally, the "A3" package (Fortmann-Roe, 2015) was used to assess the importance and the cross-validated coefficient of determination of the entire RF model.…”

Section: Random Forest Modelmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region

Gong

Wang

et al. 2023

JGR Atmospheres

View full text Add to dashboard Cite

Anthropogenic carbon dioxide (CO 2 ) emissions have been increasing rapidly since the industrial revolution, causing global warming and environmental change. CO 2 emissions in arid regions are only 30% of those in humid regions, but the warming rate is 20%-40% higher than that of humid regions (Yao et al., 2020). Such a significant spatial heterogeneity in warming rate and CO 2 emissions causes arid regions to face greater climate hazards than humid regions (Huang et al., 2012(Huang et al., , 2019Yao et al., 2020), and increasingly frequent and severe droughts threaten the ability of dryland ecosystems to sequester carbon and maintain biodiversity (Yao et al., 2020). Therefore, it is

show abstract