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Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Read chapter Read first chapter

2019 | OriginalPaper | Chapter

19. Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment

Author : Nikola K. Kasabov

Published in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Publisher: Springer Berlin Heidelberg

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Abstract

This chapter presents methods for using eSNN and BI-SNN for deep, incremental learning and predictive modelling of streaming data and for deep knowledge representation. The methods are applied for predictive modelling in the areas of finance, ecology, transport and environment using respective multisensory streaming data. Each of these applications require specific model design in terms of data preparation, SNN model parameters, experimental setting and validation. Each of the methods are illustrated with case study problems and data, but their applicability can be extended to a wider class of problems where multisensory streaming data is available.

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previous chapter Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
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Appendix
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Metadata
Title
Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment
Author
Nikola K. Kasabov
Copyright Year
2019
Publisher
Springer Berlin Heidelberg
Book
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_19

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