When investing money across markets, there are a variety of types of strategies that can be implemented. So far, we have discussed arbitrage trading, regression predictions, and machine learning models. These three types of strategies are known as alpha generation or buy-side methods — they crea...
Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame...
costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters’ opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram...
In machine learning, a few studies evaluated the impact of the sample sizes on accuracy [11]. For instance, Vabalas [12] investigated the impact of a range of simulated sub-datasets (20–1000) on the performance of support vector machine (SVM) and logistic regression (LR). They reported ...
Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties
MMEngine: OpenMMLab foundational library for training deep learning models. MIM: MIM installs OpenMMLab packages. MMCV: OpenMMLab foundational library for computer vision. MMClassification: OpenMMLab image classification toolbox and benchmark. MMDetection: OpenMMLab detection toolbox and benchmark. ...
et al. Hybrid machine learning models to predict the shear strength of discontinuities with different joint wall compressive strength. Nondestructive Testing and Evaluation, 2024. DOI:10.1080/10589759.2024.2381083 57. Wang, Y., Gao, H., Liu, S. et al. Landslide detection based on deep learning...
Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol. 2022;22(1):101. doi:10.1186/s12874-022-01577-xPubMedGoogle ScholarCrossref 3. Collins GS, Dhiman P, Andaur Navarro CL, et al. ...
Twelve digital features describing handwriting through different aspects (static, kinematic, pressure, and tilt) were extracted and used to create linear models to investigate handwriting acquisition throughout education. They found that three features (two kinematic and one static) showed a significant ...
and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by dr...