Events MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.

Event submissions

Published

This submission belongs to the session 03. USEDAT-02: USA-Europe Data Analysis Training Program Workshop, Cambridge, UK-Bilbao, Spain-Miami, USA, 2016 of the event MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.

Published date

04 Oct, 2016

Citation

Humberto González-Díaz, Alejandro Pazos, Cristian Robert Munteanu, Enrique Barreiro, Artificial Neural Network Schedulers for Food Webs, in Proceedings of MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed., 15 October–20 October 2022, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-02-05002

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Artificial Neural Network Schedulers for Food Webs

Humberto González-Díaz ¹

Alejandro Pazos ²

Cristian Robert Munteanu ²

Enrique Barreiro ²

1. Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Bilbao, Spain.

2. Department of Computer Sciences, University of A Coruña (UDC), A Coruña, 15071, A Coruña, Spain.

Abstract

In this work, we introduce by the first time a new type of algorithm aimed to predict the more promising topology of one ANN to be trained in order to model a given dataset of complex system. In so doing, we can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. Using information parameters as inputs, we developed one scheduler for 338050 outputs of 10 different ANNs for the respective 33805 pair of nodes in 73 Biological Networks. The overall accuracy of the SANN-HPC schedulers found was of >72% for Biological Networks; in training and validation series.

Keywords

Artificial Neural Networks

High Performance Computing

Scheduling

Poster

MOL2NET_2016_SANN for ecosystems.pdf

A prototype web application package for basic DNA and protein analysis using R language

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