Scientific Research Publishing

A Proposal of Genuine Computing Methods in Materials Informatics ()

In the world of Material Informatics (MI), conventional methods involve tremendous laboratory work or extensive simulations that may not yield the expected results. Our objectives are to contribute to ...
IEEE

Inverse Kinematics using Neural Networks of a 3RRR Planar Robot

Abstract: This study presents the development, modeling, and validation of a type 3-RRR planar parallel manipulator, with the primary objective of solving the direct kinematics problem using ...
GitHub

TfELM : Extreme Learning Machines Framework with Python and TensorFlow

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...
IEEE

Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays

Abstract: In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical ...
InfoWorld

How to build a neural network in Java

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java. Artificial neural networks are a form of deep learning ...
Frontiers

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial ...
InfoQ

Building Neural Networks with TensorFlow.NET

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow, allowing ...
Frontiers

Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks

While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking ...
Scientific Research Publishing

Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R ()

1 Department of Electronics, Computing and Mathematics, University of Derby, Derby, UK. 2 Department of Computer Science and Intelligent Systems, Iwate University, Morioka, Japan. 3 BAC International ...