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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Lisbon
BEGIN:DAYLIGHT
DTSTART:20220327T020000
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DTSTART:20221030T010000
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BEGIN:VEVENT
DTSTAMP:20221229T150743Z
UID:DC1A841E-F38A-4F80-9D2B-C560C52EDE6A
DTSTART;TZID=Europe/Lisbon:20220615T170000
DTEND;TZID=Europe/Lisbon:20220615T180000
DESCRIPTION:Title: Implementation of virtual sensors and one-class classifi
 ers to detect anomalous events\n\nAuthor: Esteban Jove\, University of A C
 oruña\, Spain\n\nGeneral description: this conference deals with the anal
 ysis and implementation of anomaly detection systems based on intelligent 
 techniques. Specifically\, two of the most commonly used strategies for th
 is purpose are presented\, studied and analysed. The first consists of the
  virtual sensor development based on a hybrid intelligent model capable of
  detecting anomalous situations. The second strategy is based on the use o
 f one-class techniques\, used to implement anomaly detection classifiers. 
 Therefore\, an analysis and a comparison of both strategies are accomplish
 ed through different real applications\, highlighting the performance of e
 ach one.\n\nVirtual: https://events.vtools.ieee.org/m/317068
LOCATION:Virtual: https://events.vtools.ieee.org/m/317068
ORGANIZER:dalila.duraes@algoritmi.uminho.pt
SEQUENCE:6
SUMMARY:CI Lectures Series
URL;VALUE=URI:https://events.vtools.ieee.org/m/317068
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;Title: &lt;/strong&gt;Implementation of 
 virtual sensors and one-class classifiers to detect anomalous events&lt;/p&gt;\n
 &lt;p&gt;&lt;strong&gt;Author&lt;/strong&gt;: Esteban Jove\, University of A Coru&amp;ntilde\;a\
 , Spain&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;General description: &lt;/strong&gt;this conference deal
 s with the analysis and implementation of anomaly detection systems based 
 on intelligent techniques. Specifically\, two of the most commonly used st
 rategies for this purpose are presented\, studied and analysed. The first 
 consists of the virtual sensor development based on a hybrid intelligent m
 odel capable of detecting anomalous situations. The second strategy is bas
 ed on the use of one-class techniques\, used to implement anomaly detectio
 n classifiers. Therefore\, an analysis and a comparison of both strategies
  are accomplished through different real applications\, highlighting the p
 erformance of each one.&lt;/p&gt;
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