posted by organizer: fbellavia || 4573 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

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
CVPR 2022   Computer Vision and Pattern Recognition
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
JCRAI 2021-Ei Compendex & Scopus 2021   2021 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2021)
SENSORS Special Issue 2022   'Next Generation of Secure and Resilient Healthcare Data Processing'
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
SI-DAMLE 2022   Special Issue on Data Analytics and Machine Learning in Education
EI-CFAIS 2021   2021 International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS 2021)
ICSMLE 2022   International Conference on Statistics and Machine Learning in Electronics
MLDM 2022   18th International Conference on Machine Learning and Data Mining