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 ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...

Marc Santos is a Guides Staff Writer from the Philippines with a BA in Communication Arts and over six years of experience in writing gaming news and guides. He plays just about everything, from ...

Background: Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging ...

Abstract: Classification tasks have long been a central concern in the field of machine learning. Although deep neural network-based approaches offer a novel, versatile, and highly precise solution ...

In the field of machine learning, support vector machine (SVM) is popular for its powerful performance in classification tasks. However, this method could be adversely affected by data containing ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...

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 ...

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are ...