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APPLYING MACHINE LEARNING AGAINST BEEHIVES DATASET.
Abstract
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.
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References11
Pilloni, V. How data will transform industrial processes: crowdensing, crowdsourcing and big data as pillars of industry 4.0, Future Internet 2018, vol.10, 24.
Stankovic, J.A. Research directions for the internet of things. IEEE Internet Things J. 2014, 1, pp. 3–9.
Jazdi, N. Cyber physical systems in the context of Industry 4.0. IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, 22–24 May 2014; pp. 1–4.
Steinhauer, N., Kulhanek, K., Antunez, K. Drivers of colony losses. Current opinion in Insect science, vol. 26, 2018, pp. 142-148.
Newton, P., Bristoll, H. SWOT Analysis – Strategy Skills. Team FME. 2013, pp. 33
Dineva, K., Atanasova, T., Model of Modular IoT-based Bee-Keeping System. EUROSIS-ETI, 2017, pp. 404-406.
Bonaccorso, G. Machine Learning Algorithms. Packt. 2017, pp. 330
Balabanov T., Zankinski, I., Barova, M. Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms, CIT, 2016, pp.80-88.
Herbrich, R., Graepel, T. Bayes Point Machine. JMLR, 2001, pp. 245-279.
Mease, D., Wyner, A. J. and Buja, A. Boosted Classification Trees and Class Probability Estimation. JMLR, 2007, pp. 409-439.
Christopher, D., Raghavan, P. Information Retrieval, Cambridge University Press, 2008, p. 175.
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