Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije
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Ponedeljkov seminar računalništva in informatike - Arhiv

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sobota, 28. november 2020 Challenges for Data Mining in Distributed Sensor Networks

V ponedeljek, 30. novembra 2020, bo ob 16.00 uri prek spletnih orodij na daljavo izvedeno predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 7. december 2020 ob 16.00 na daljavo

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PREDAVATELJ: Branko KAVŠEK
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Branko Kavšek has a PhD in Computer Science from the University of Ljubljana. He is an assistant professor at the University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, a researcher at the Department of Information Sciences and Technologies and member of the Artificial Intelligence Laboratory at the Jozef Stefan Institute in Ljubljana. His research fields include Artificial Intelligence, Data Mining, Machine Learning. In particular, the sub-fields of Supervised and Unsupervised Learning, Frequent Pattern Discovery and Association Rule Learning, Learning Probabilistic Models and Bayesian Networks Learning, Clustering, and Data Mining applied to Big Data.

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NASLOV: Challenges for Data Mining in Distributed Sensor Networks
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POVZETEK: 

In this seminar we will present an overview of Distributed Sensor Networks (DSNs) with particular emphasis on Wireless Sensor Networks (WSNs) and the use of Data Mining (DM) to analyze the data collected by WSNs. We will start by giving an overview of the history of sensor networks, stressing the technological issues, distributed data management and distributed hypothesis formation. In the second part we will present the application of simple DM methods on data collected during the ARRS project "Autonomic edge computing for air quality monitoring". The last part of the seminar will be devoted to presenting evolution opportunities and challenges.

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Predavanje bo potekalo v angleškem jeziku prek spletnega orodja Zoom.
Do predavanja dostopate tako, da se povežete prek sledeče povezave:

https://us02web.zoom.us/j/297328207

Vabljeni!