posted by organizer: fbellavia || 4973 views || tracked by 6 users: [display]

MAES 2020 : Machine Learning Advances Environmental Science

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/maes-icpr2020/
 
When Jan 10, 2021 - Jan 15, 2021
Where online
Submission Deadline Oct 25, 2020
Notification Due Nov 10, 2020
Final Version Due Nov 15, 2020
Categories    computer science   machine learning   environmental science
 

Call For Papers

=== Aim & Scope ===

Environmental data are growing steadily in volume, complexity and diversity to Big Data mainly driven by advanced sensor technology. Machine learning can offer superior techniques for unravelling complexity, knowledge discovery and predictability of Big Data environmental science.

The aim of the workshop is to provide a state-of-the-art survey of environmental research topics that can benefit from Machine Learning methods and techniques. To this purpose the workshop welcomes papers on successful environmental applications of machine learning and pattern recognition techniques to diverse domains of Environmental Research, for instance, recognition of biodiversity in thermal, photo and acoustic images, natural hazards analysis and prediction, environmental remote sensing, estimation of environmental risks, prediction of the concentrations of pollutants in geographical areas, environmental threshold analysis and predictive modelling, estimation of Genetical Modified Organisms (GMO) effects on non-target species.

The workshop will be the place to make an analysis of the advances of Machine Learning for the Environmental Science and should indicate the open problems in environmental research that still have not properly benefited from Machine Learning.

Extended papers of this workshop will be published as a special issue in the journal of Environmental Modelling and Software, Elsevier.

*** Due to the COVID pandemic, the workshop will be taken fully virtual. All accepted papers will be published. ***


=== Invited Talk ===

"Harnessing big environmental data by machine learning", prof. Friedrich Recknagel, School of Biological Sciences, University of Adelaide, Australia

(prof. Recknagel's bio: http://www.adelaide.edu.au/directory/friedrich.recknagel)
(talk abstract: https://drive.google.com/file/d/12BFBiG4pwN-6TRKCy0OuGHOgue4YbOKJ/view?usp=sharing)


=== Important Dates ===

- 25 October 2020 - workshop submission deadline (*EXTENDED*)
- 10 November 2020 - author notification
- 15 November 2020 - camera-ready submission
- 1 December 2020 - finalized workshop program


=== Organizers ===

Francesco Camastra, Universita' di Napoli Parthenope, Italy
Friedrich Recknagel, University of Adelaide, Australia
Antonino Staiano, Universita' di Napoli Parthenope, Italy


== Publicity chair ==

Fabio Bellavia, Universita' di Palermo, Italy

_______________________________________________________________________

Contacts: antonino.staiano@uniparthenope.it
francesco.camastra@uniparthenope.it

Workshop: https://sites.google.com/view/maes-icpr2020/
ICPR2020: https://www.micc.unifi.it/icpr2020/


Related Resources

SI-MLT 2023   Special Issue on MACHINE LEARNING IN TOURISM - Int. J. of Machine Learning and Cybernetics (Springer)
ACM-Ei/Scopus-CWCBD 2023   2023 4th International Conference on Wireless Communications and Big Data (CWCBD 2023) -EI Compendex
DS 2023   Discovery Science 2023
ICMLT 2023   ACM--2023 8th International Conference on Machine Learning Technologies (ICMLT 2023)
Decision Making in Complex Systems 2023   Decision Making in Complex Systems
AIM@EPIA 2023   Artificial Intelligence in Medicine
ICDM 2023   International Conference on Data Mining
AAISS 2023   Special Issue on Advances in Artificial Intelligent Systems for the Scholarly Domain
IEEE Xplore-Ei/Scopus-CCCAI 2023   2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI 2023) -EI Compendex
ESANN 2023   European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning