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PRODID:IEEE vTools.Events//EN
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TZID:America/Chicago
BEGIN:DAYLIGHT
DTSTART:20240310T030000
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DTSTART:20231105T010000
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BEGIN:VEVENT
DTSTAMP:20240201T161037Z
UID:3002696D-515A-4C43-82B5-2723CE0AAAB1
DTSTART;TZID=America/Chicago:20240131T090000
DTEND;TZID=America/Chicago:20240131T100000
DESCRIPTION:Designs of engineering components that are done through Machine
  Learning (ML)\, while easy because it is done through computer-assisted t
 raining software\, is difficult to verify and validate because it is not e
 xplainable. This lack of V&amp;V methodology is a major issue when the design 
 is implemented into software form\, and integrated into a more complex sys
 tem. This presentation will examine current ML-based applications\, and pr
 esent basic V&amp;V methodology that can extract engineering specifications of
  the ML-based components\, information that allows their integration to wo
 rk with other engineering components in a more complex system. In addition
 \, a quantitative level of confidence is established to assure consistent 
 performance of such ML-based components.\n\nCo-sponsored by: IMEKO TC17\n\
 nSpeaker(s): TRUNG\, \n\nVirtual: https://events.vtools.ieee.org/m/401737
LOCATION:Virtual: https://events.vtools.ieee.org/m/401737
ORGANIZER:ztaqvi@gmail.com
SEQUENCE:22
SUMMARY:&quot;V&amp;V of ML-Based Design: Engineering Concept of Explainable AI ”
URL;VALUE=URI:https://events.vtools.ieee.org/m/401737
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Designs of engineering components that are
  done through Machine Learning (ML)\, while easy because it is done throug
 h computer-assisted training software\, is difficult to verify and validat
 e because it is not explainable. This lack of V&amp;amp\;V methodology is a ma
 jor issue when the design is implemented into software form\, and integrat
 ed into a more complex system. This presentation will examine current ML-b
 ased applications\, and present basic V&amp;amp\;V methodology that can extrac
 t engineering specifications of the ML-based components\, information that
  allows their integration to work with other engineering components in a m
 ore complex system. In addition\, a quantitative level of confidence is es
 tablished to assure consistent performance of such ML-based components.&lt;/p
 &gt;
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