QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Abstract: In this work, the possibility of applying machine learning (ML) techniques to analyze and predict radio wave propagation losses in urban environments is explored. Thus, from a measurement ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity ...

Recentive Analytics, Inc. v. Fox Corp., No. 23-2437 (Fed. Cir. 2025) – On April 18, 2025, the Federal Circuit upheld the district court’s dismissal of the case on the ground that the patents were ...

Interpretability of Support Vector Machine (SVM) or Neural Networks (NN) models, examples of black-box models, is a field of study that has recently gained attention, especially for the significant ...

ABSTRACT: Forecasting future expected returns out-of-sample is challenging due to some statistical characteristics, such as the stochastic and dynamic nature in the time series. Conventional machine ...

Abstract: This paper introduces a generalized quantum-classical dual kernel learning method for Support Vector Machines (SVMs), extending its application to both classification and regression tasks.

ABSTRACT: Support vector regression (SVR) and computational fluid dynamics (CFD) techniques are applied to predict the performance of an automotive torque converter in the design process of turbine ...

Objective: In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP ...