Academic Articles & Research Papers

Neural Networks in Chemistry

Abstract The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's vario...

Neural Networks: Tricks of the Trade

Brain tumor grading based on Neural Networks and Convolutional Neural Networks

This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, wit...

Artificial Neural Networks

What are artificial neural networks?

Neural Networks and Deep Learning

An introduction to Neural Networks

Contents Preface 9 I FUNDAMENTALS 11 1 Introduction 13 2 Fundamentals 15 2.1 A framework for distributed representation : : : : : : : : : : : : : : : : : : : : : 15 2.1.1 Processing units : : : : : : : : : : : : : : : : : : : : : : : : : : ...

Artificial neural networks

Artificial neural networks are explained, and the different types are described. Three different tasks for which they are suitable are discussed. They are pattern classification and associative memory, self-organization and feature extracti...

Memristor-based neural networks

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent p...

Evolving artificial neural networks

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations betwee...

Neural Networks for Chemists: An Introduction

From the Publisher: This book gives chemists insight into the much discussed and often not fully understood concept of neural networks. The authors pinpoint the five most widely used neural networks and learning strategies, illustrating th...

Neural Networks in Materials Science.

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach o...

Neural networks for signal processing

This collection of essays explores neural networks applications in signal and image processing, function and estimation, robotics and control, associative memories, and electrical and optical networks. Intended as a companion to Neural Netw...

neuralnet: Training of Neural Networks

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...

The germ layer: foundation of mesenchymal stem cell therapeutic destiny.

Physiological APP processing is required for synaptic plasticity and cognition.

Block copolymer multistate material circuit.

Emergent Roles of RVLM(C1) Neurons in Cardiovascular Homeostasis and Systemic Physiological Regulation.

Whole-genome resequencing identifies a novel SNP associated with lumbar vertebrae number in Dezhou donkeys.