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 ...

Traditional machine learning algorithms for classification tasks operate under the assumption of balanced class distributions. However, this assumption only holds in some practical scenarios. In most ...

Microsoft has added official Python support to Aspire 13, expanding the platform beyond .NET and JavaScript for building and running distributed apps. Documented today in a Microsoft DevBlogs post, ...

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating ...

Out-of-hospital cardiac arrest (OHCA) represents a critical challenge for emergency medical services, with the necessity for rapid and accurate prediction of defibrillation outcomes to enhance patient ...

Abstract: Applying Support Vector Machine (SVM) theory to hyperspectral images classification can significantly mitigate the decline in classification performance caused by the curse of dimensionality ...

ABSTRACT: Industrial appearance anomaly detection (AD) focuses on accurately identifying and locating abnormal regions in images. However, due to issues such as scarce abnormal samples, complex ...