Abstract: In the realm of finance, accurate projections of stock prices carry immense significance. Multiple factors influence stock prices, including external variables such as influential opinions, ...

Abstract: This paper presents an analog RF-domain implementation of a Vanilla Recurrent Neural Network (RNN) for real-time anomaly detection in 5G and beyond wireless networks. Real-time analysis is ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

This repository contains the official implementation for the paper "Evolving Spatially Embedded Recurrent Spiking Neural Networks for Control Tasks." The code implements a framework for evolving ...

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...

1 School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India 2 Centre for e-Automation Technologies, Vellore Institute of Technology, Chennai, India Introduction: Friction Stir ...

Step-by-step coding a full deep neural network with zero libraries — just logic and Python. #NeuralNetwork #PythonCode #DeepLearning Trump announces two new national holidays, including one on ...

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ABSTRACT: With the development of the Industrial Internet of Things (IIoT) and cloud computing technologies, intelligent sensors in the field that can generate large volumes of time-series data ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...