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Pregled bibliografske jedinice broj: 1177554

Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms


Salem, Ziad; Radspieler, Gerald; Griparić, Karlo; Schmickl, Thomas
Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms // Machine Learning, Optimization, and Big Data
Volterra, Italija: Springer International Publishing, 2017. str. 309-321 doi:10.1007/978-3-319-72926-8_26 (predavanje, međunarodna recenzija, sažetak, znanstveni)


CROSBI ID: 1177554 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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 International Publishing, 2017, 309-321

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

Mjesto i datum
Volterra, Italija, 14-17.09.2017

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


Ustanove:
Sveučilište Jurja Dobrile u Puli

Profili:

Avatar Url Karlo Griparić (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Salem, Ziad; Radspieler, Gerald; Griparić, Karlo; Schmickl, Thomas
Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms // Machine Learning, Optimization, and Big Data
Volterra, Italija: Springer International Publishing, 2017. str. 309-321 doi:10.1007/978-3-319-72926-8_26 (predavanje, međunarodna recenzija, sažetak, znanstveni)
Salem, Z., Radspieler, G., Griparić, K. & Schmickl, T. (2017) Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms. U: Machine Learning, Optimization, and Big Data doi:10.1007/978-3-319-72926-8_26.
@article{article, author = {Salem, Ziad and Radspieler, Gerald and Gripari\'{c}, Karlo and Schmickl, Thomas}, year = {2017}, pages = {309-321}, DOI = {10.1007/978-3-319-72926-8\_26}, keywords = {Machine learning, Data mining, Classification algorithms, Density estimation, Robots, Honeybees}, doi = {10.1007/978-3-319-72926-8\_26}, title = {Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms}, keyword = {Machine learning, Data mining, Classification algorithms, Density estimation, Robots, Honeybees}, publisher = {Springer International Publishing}, publisherplace = {Volterra, Italija} }
@article{article, author = {Salem, Ziad and Radspieler, Gerald and Gripari\'{c}, Karlo and Schmickl, Thomas}, year = {2017}, pages = {309-321}, DOI = {10.1007/978-3-319-72926-8\_26}, keywords = {Machine learning, Data mining, Classification algorithms, Density estimation, Robots, Honeybees}, doi = {10.1007/978-3-319-72926-8\_26}, title = {Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms}, keyword = {Machine learning, Data mining, Classification algorithms, Density estimation, Robots, Honeybees}, publisher = {Springer International Publishing}, publisherplace = {Volterra, Italija} }

Časopis indeksira:


  • Scopus


Citati:





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