Health Discovery Corp.

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Advancing Neurological Disease Prediction through Machine Learning Techniques

A study explores how AI and ML can improve early detection of neurological diseases, including Parkinson’s disease, ...
Visual Studio Magazine

Support Vector Regression with SGD Training Using C#

Support vector regression (SVR) predicts a single numeric value using a kernel function, usually RBF, that computes similarity between two data items. SVR makes predictions using support vectors, ...
IEEE

An Adaptive Robust Support Vector Machine with Sequential Minimal Optimization for Imbalanced Data Classification

Abstract: Class imbalanced classification presents a considerable difficulty in machine learning, as conventional algorithms typically exhibit bias towards the majority class, compromising minority ...
IEEE

Support Vector Machine-Integrated High-Definition MCPWM Technique for Improving Power Quality in Multilevel Inverters

Abstract: What if machine learning could predict inverter harmonics before prototyping? Conventional pulse width modulation (PWM) techniques in cascaded H-bridge (CHB) multilevel inverters (MLIs) ...
Searchenginejournal.com

Google’s New MUVERA Algorithm Improves Search

Google announced a new multi-vector retrieval algorithm called MUVERA that speeds up retrieval and ranking, and improves accuracy. The algorithm can be used for search, recommender systems (like ...
archive

A Web Application For Learning Support Vector Machine Algorithms In Computer Engineering

Keep the news in the Wayback Machine. Sign Fight for the Future's letter. Please Don't Scroll Past This Can you chip in? The Internet Archive partners with libraries, archives, and institutions across ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

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.