The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix …
Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …
it can influence decisions in multiple areas, including for example prognosis or therapies of …
Transfer learning to detect COVID‐19 automatically from X‐Ray images using convolutional neural networks
MM Taresh, N Zhu, TAA Ali… - … Journal of Biomedical …, 2021 - Wiley Online Library
The novel coronavirus disease 2019 (COVID‐19) is a contagious disease that has caused
thousands of deaths and infected millions worldwide. Thus, various technologies that allow …
thousands of deaths and infected millions worldwide. Thus, various technologies that allow …
[HTML][HTML] Empirical charging behavior of plug-in hybrid electric vehicles
Plug-in hybrid electric vehicles (PHEV) offer greenhouse gas emission reduction in car
usage if charged frequently and driven mainly on electricity. However, little is known about …
usage if charged frequently and driven mainly on electricity. However, little is known about …
Generic performance measure for multiclass-classifiers
The evaluation of classification performance is crucial for algorithm and model selection.
However, a performance measure for multiclass classification problems (ie, more than two …
However, a performance measure for multiclass classification problems (ie, more than two …
Prediction of absorption spectrum shifts in dyes adsorbed on titania
Dye adsorption on metal-oxide films often results in small to substantial absorption shifts
relative to the solution phase, with undesirable consequences for the performance of dye …
relative to the solution phase, with undesirable consequences for the performance of dye …
A new fault classification approach applied to Tennessee Eastman benchmark process
This study presents a data-based methodology for fault detection and isolation in dynamic
systems based on fuzzy/Bayesian approach for change point detection associated with a …
systems based on fuzzy/Bayesian approach for change point detection associated with a …
Ensemble of deep convolutional neural network for skin lesion classification in dermoscopy images
A Aldwgeri, NF Abubacker - … in Visual Informatics: 6th International Visual …, 2019 - Springer
Diagnosis of skin lesions is a very challenging task due to high inter-class similarities and
intra-class variations between lesions in terms of color, size, site and appearance. As a …
intra-class variations between lesions in terms of color, size, site and appearance. As a …
Automatic filtering and classification of low-density airborne laser scanner clouds in shrubland environments
The monitoring of shrublands plays a fundamental role, from an ecological and climatic point
of view, in biodiversity conservation, carbon stock estimates, and climate-change impact …
of view, in biodiversity conservation, carbon stock estimates, and climate-change impact …
Class-weighted evaluation metrics for imbalanced data classification
Class distribution skews in imbalanced datasets may lead to models with prediction bias
towards majority classes, making fair assessment of classifiers a challenging task. Balanced …
towards majority classes, making fair assessment of classifiers a challenging task. Balanced …
Affective brain-computer interfaces: Choosing a meaningful performance measuring metric
MR Mowla, RI Cano, KJ Dhuyvetter… - Computers in Biology and …, 2020 - Elsevier
Affective brain-computer interfaces are a relatively new area of research in affective
computing. Estimation of affective states can improve human-computer interaction as well as …
computing. Estimation of affective states can improve human-computer interaction as well as …