Naslov
Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

Autori
Salem, Ziad ; Radspieler, Gerald ; Griparić, Karlo ; Schmickl, Thomas

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Machine Learning, Optimization, and Big Data / - : Springer, 2017, 309-321

Skup
International Workshop on Machine Learning, Optimization, and Big Data

Mjesto i datum
Volterra, Italija, 14.09.2017. - 17.09.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Machine learning, Data mining, Classification algorithms, Density estimation, Robots, Honeybees

Sažetak
The estimation of the density of a population of behaviourally diverse agents based on limited sensor data is a challenging task. We employed different machine learning algorithms and assessed their suitability for solving the task of finding the approximate number of honeybees in a circular arena based on data from an autonomous stationary robot’s short range proximity sensors that can only detect a small proportion of a group of bees at any given time. We investigate the application of different machine learning algorithms to classify datasets of pre-processed, highly variable sensor data. We present a new method for the estimation of the density of bees in an arena based on a set of rules generated by the algorithms and demonstrate that the algorithm can classify the density with good accuracy. This enabled us to create a robot society that is able to develop communication channels (heat, vibration and airflow stimuli) to an animal society (honeybees) on its own.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA