Deep and reinforcement learning in mosquito population control (CROSBI ID 720148)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | domaća recenzija
Podaci o odgovornosti
Hackenberger Kutuzović, Domagoj ; Đerđ, Tamara ; Hackenberger Kutuzović, Branimir
engleski
Deep and reinforcement learning in mosquito population control
Because of climate change, an increase in the risk of vector-borne diseases has been reported. Mosquitoes are dominant vectors of most of these diseases. Therefore, the rapid monitoring of mosquito population dynamics it is of the utmost importance. In this work, the framework of a multi-layered decision support system for the control of the mosquito population is described. Layers of the framework combine biological knowledge, Internet of Things (IoT), big data, and state- of-the-art AI techniques, such as deep and reinforcement learning, and population modeling, to make a decision support system that will be able to automatically survey mosquito population dynamics and devise a strategy for population control.
computer simulations ; optimization
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Podaci o prilogu
198-199.
2022.
objavljeno
Podaci o matičnoj publikaciji
6th Faculty of Science PhD Student Symposium - Book of Abstracts
Đaković, Marijana ; Korać, Petra ; Lukić, Aleksandar ; Marušić-Paloka, Eduard ; Novak, Predrag ; Pezelj, Đurđica ; Pikelj, Kristina ; Smolčić, Vernesa ; Schneider, Petra
Zagreb:
978-953-6076-93-2
Podaci o skupu
6. Simpozij studenata doktorskih studija PMF-a = 6th Faculty of Science PhD Student Symposium
poster
23.05.2022-24.05.2022
Zagreb, Hrvatska