Abstract: A major challenge in machine learning is the accurate categorization of data in the presence of noise, outliers, and imbalanced class distributions. Fuzzy support vector machines and their ...
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 ...
Abstract: Electrical circuits play a vital role in industrial, automotive, and power systems, where even minor faults can lead to severe performance degradation or system failure. Traditional fault ...
Hệ thống sử dụng AI và Computer Vision để phát hiện tình trạng buồn ngủ của lái xe theo thời gian thực: ...
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 ...
Ticket Classification Workflow with AI automatic classification of tickets + Intercom integration to receive customer inputs + Slack Notifications + Pipedrive Integration for creating Deals according ...
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 group includes 41 residents, 8 fellows and 8 interns are part of a program that offers skill-building in approximately 40 areas of veterinary practice. The University of California-Davis (UC Davis ...
Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA
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 ...
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