University of Hertfordshire

From the same journal

By the same authors

View graph of relations
Original languageEnglish
Number of pages8
Pages (from-to)372-379
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal publication date13 Aug 2016
Volume9887
DOIs
StateE-pub ahead of print - 13 Aug 2016
Event25th International Conference on Artificial Neural Networks - Barcelona Tech, Universitat Politecnica de Catalunya, Barcelona, Spain
Duration: 6 Sep 20169 Sep 2016
http://icann2016.org/

Abstract

In this paper, we present a prediction model developed to
identify particles size of ice crystals in clouds. The proposed model combines a Feed Forward Multi-Layer Perceptron neural network withBayesian regularization backpropagation and other machine learning techniques for feature reduction with Principal Component Analysis androtation invariance with Fast Fourier Transform. The proposed solution is capable of predicting the particle sizes with normalized mean squared error around 0.007. However, the proposed network model is not able topredict the size of very small particles (between 3 and 10 µm size) with the same precision as for the larger particles. Therefore, in this work we also discuss some possible reasons for this problem and suggest future points that need to be analysed.

Notes

Daniel Priori, Giseli de Sousa, Mauro Roisenberg, Chris Stopford, Evelyn Hesse, Neil Davey and Yi Sun, 'Using Machine Learning Techniques to Recover Prismatic Cirrus Ice Crystal Size from 2-Dimensional Light Scattering Patterns', in Alessandro E. P. Villa, Paolo Masulli, and Antonio J. Pons Rivero eds., Proceedings of Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks. Universitat Politecnica de Catalunya, Barcelona, Spain, 6- 9 September 2016. ISBN 978-3-319-44780-3, e-ISBN 978-3-319-44781-0

ID: 13463185