A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...

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 ...

Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation ...

Abstract: Support vector machine (SVM) technology that can readily manage a very high proportion of complicated health data is the predictive modelling technique applied in this study. Although most ...

1 Faculty of Mathematics and Computer Science,, Felix Houphouët-Boigny University, Abidjan, Côte d’Ivoire. 2 Institute for Mathematical Research (IRMA), Abidjan, Côte d’Ivoire. 3 Higher Teacher ...

Ms. Mutcherson is a professor at Rutgers Law School. Right now in an Atlanta hospital room lies a 30-year-old nurse and mother, Adriana Smith. Ms. Smith, who is brain-dead, has been connected to life ...

I propose adding a Multiple Kernel Learning (MKL) module for kernel optimization in kernel-based methods (such as SVM) to scikit-learn. MKL is a more advanced approach compared to GridSearchCV, ...

Software defect prediction and cost estimation are critical challenges in software engineering, directly influencing software quality and project management efficiency. This study presents a ...

Introduction: Lung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early ...