HOME
Full Articles List
Search by Author
Title: APPLYING MACHINE LEARNING AGAINST BEEHIVES DATASET.
BibTex 
Open this article in the author's DB |
|
APPLYING MACHINE LEARNING AGAINST BEEHIVES DATASET.
|
|
|
|
|
|
|
|
||
|
|
|
|
|
|
1314-2704
|
|
|
|
||
|
|
English
|
|
|
|
18
|
|
|
|
6.2
|
|
|
|
|
|
|
|
||
|
|
With introduction of new and advanced technologies in beekeeping it is possible to design and use Internet of Things (IoT) sensor systems embedded in beehives. This provides the ability to collect heterogeneous data and to provide real-time remote monitoring. Complex prognostic models are applied after the data is manipulated and normalized. The obtained results are used to predict the processes occurring in beehives. For this purpose, the collected data must be divided into at least two parts for training and validation of the constructed model. Such approach is widely used in the field of modern machine learning. Applying it to datasets from beehives allows new patterns to be detected between the processes in the hive and the parameters of the surrounding environment such as temperature, humidity, atmospheric pressure, CO2 and others. The purpose of this article is to describe the preparation and processing of IoT heterogeneous data collected from beehives and the application of the appropriate machine learning algorithms against it.
|
|
|
|
conference
|
|
|
|
||
|
|
||
|
|
18th International Multidisciplinary Scientific GeoConference SGEM 2018
|
|
|
|
18th International Multidisciplinary Scientific GeoConference SGEM 2018, 02-08 July, 2018
|
|
|
|
Proceedings Paper
|
|
|
|
STEF92 Technology
|
|
|
|
International Multidisciplinary Scientific GeoConference-SGEM
|
|
|
|
Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
|
|
|
|
35-42
|
|
|
|
02-08 July, 2018
|
|
|
|
website
|
|
|
|
cdrom
|
|
|
|
1852
|
|
|
|
IoT; Beehives; Monitoring; Heterogeneous Data; Machine Learning.
|
|
Download PDF