Visual Studio Magazine

Support Vector Regression with SGD Training Using C#

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
IEEE

Power Flow Optimization and Voltage Stability Using DSTATCOM with Deep Learning Techniques in PV-Connected Grids Under Varying Load Conditions

Abstract: As PV systems become more integrated into power grids, challenges arise in optimizing power flow and maintaining stability amid fluctuating load conditions. Variations in solar energy output ...
GitHub

Deep Neural Network Based Fault Classification and Location Detection in Power Transmission Lines

This project investigates the application of Deep Neural Networks (DNNs) for automated fault classification and fault location in power transmission lines. Using data generated from a simulated 4-bus ...
C&EN

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India

Department of Environmental, Water and Agricultural Engineering, Faculty of Civil & Environmental Engineering, Technion−Israel Institute of Technology, Haifa 3200003, Israel Environmental Physics ...
unite

10 Best Python Libraries for Machine Learning & AI

Python has grown in popularity over the years to become one of the most popular programming languages for machine learning (ML) and artificial intelligence (AI) tasks. It has replaced many of the ...
Frontiers

Marker-Less Motion Capture of Insect Locomotion With Deep Neural Networks Pre-trained on Synthetic Videos

Motion capture of unrestrained moving animals is a major analytic tool in neuroethology and behavioral physiology. At present, several motion capture methodologies have been developed, all of which ...
PNAS

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

We demonstrate that combining optical superresolution imaging with deep learning classification methods increases the speed and accuracy of assessing the biological affinities of fossil pollen taxa.
PNAS

Learning probabilistic neural representations with randomly connected circuits

We present a theory of neural circuits’ design and function, inspired by the random connectivity of real neural circuits and the mathematical power of random projections. Specifically, we introduce a ...