Feature importance comparison between the machine learning algorithms used in this study. by Sukanta Chakraborty (22693681)

Five-fold cross-validation results of CNN algorithm at different SNRs. by Jiuxiao Cao (21594403)

Five-fold cross-validation results of CNN algorithm at different SNRs. by Jiuxiao Cao (21594403)

Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. by Jiuxiao Cao (21594403)

Five-fold cross-validation results of mulit-scale GRU algorithm at different SNRs. by Jiuxiao Cao (21594403)

Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. by Jiuxiao Cao (21594403)

Five-fold cross-validation results of mulit-scale convolution GRU algorithm at different SNRs. by Jiuxiao Cao (21594403)

Comparison of recognition rates for three algorithms at different SNRs. by Jiuxiao Cao (21594403)

Comparison of recognition rates for three algorithms at different SNRs. by Jiuxiao Cao (21594403)

Cross validation of PRF and PRFGA models. by Taiwo A. Adekunle (22226868)

A data processing scheme for spatially explicit soil spectral calibration. by José L. Safanelli (20552654)

Supplementary file 1_Deep learning outperforms existing algorithms in glacier surface velocity estimation with high-resolution data – the example of Austerdalsbreen, Norway.pdf by Harald Zandler (12187328)

“…Therefore, we tested the potential of new deep learning-based image-matching algorithms for deriving glacier surface velocities across the ablation area of a glacier with strong spatial variability in surface velocities (<5 m/yr to >100 m/yr) and substantial changes in surface properties between image acquisitions. …”

Demonstration of the limitations of existing spatial analysis methods, namely nearest neighbor distance (NND), G-cross function and Morisita-Horn index. by Mai Chan Lau (3135303)

Dataset split for cross-validation methods among 5 iterations/folds. by Sulav Baral (22477755)

Spatial locations of reported FMD outbreaks by Umanga Gunasekera (22079381)

Data for "Machine Learning-based Prediction of Passive Gears from Vessel Tracking Data in Small-Scale Multi-gear Fisheries" by Pamela Lattanzi (16454871)

(Illustration) Directory structure showing train, validation, and fixed test splits across five folds/iterations, organized by class (ABNORMAL, NORMAL, PNEUMONIA) for cross-validat... by Sulav Baral (22477755)

Average success rates per condition, per axis. by Amber Maimon (14703286)

“…<div><p>This study explores spatial perception of depth by employing a novel proof of concept sensory substitution algorithm. …”

Average training durations for both conditions. by Amber Maimon (14703286)

“…<div><p>This study explores spatial perception of depth by employing a novel proof of concept sensory substitution algorithm. …”

Fig 2 - by Amber Maimon (14703286)