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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20260407T175051Z
UID:54CBFB00-1DB8-40A2-8592-15795EA61FB0
DTSTART;TZID=Asia/Kolkata:20260313T190000
DTEND;TZID=Asia/Kolkata:20260313T200000
DESCRIPTION:Deep learning has emerged as a transformative paradigm\, drivin
 g innovation across diverse domains such as healthcare and Industry 4.0. I
 n healthcare\, advanced neural architectures enable automated medical imag
 ing analysis\, predictive diagnostics\, drug discovery\, and personalized 
 treatment planning\, thereby improving accuracy\, efficiency\, and patient
  outcomes. In parallel\, Industry 4.0 leverages deep learning to enhance s
 mart manufacturing\, predictive maintenance\, supply chain optimization\, 
 and human–machine collaboration\, fostering resilient and adaptive indus
 trial ecosystems. This paper explores the convergence of deep learning tec
 hniques with healthcare and Industry 4.0 applications\, highlighting key m
 ethodologies\, challenges\, and opportunities. By synthesizing recent adva
 ncements\, we demonstrate how deep learning not only accelerates digital t
 ransformation but also establishes a foundation for intelligent\, data-dri
 ven decision-making in critical sectors. The discussion underscores the im
 portance of explainability\, scalability\, and ethical considerations to e
 nsure sustainable integration of deep learning into real-world systems.\n\
 nSpeaker(s): \, Dr. Celia Shahnaz\n\nVirtual: https://events.vtools.ieee.o
 rg/m/545042
LOCATION:Virtual: https://events.vtools.ieee.org/m/545042
ORGANIZER:amiya87@gmail.com
SEQUENCE:12
SUMMARY:Deep Learning for Health care and industry 4.0 Applications
URL;VALUE=URI:https://events.vtools.ieee.org/m/545042
X-ALT-DESC:Description: <br /><p>Deep learning has emerged as a transformat
 ive paradigm\, driving innovation across diverse domains such as healthcar
 e and Industry 4.0. In healthcare\, advanced neural architectures enable a
 utomated medical imaging analysis\, predictive diagnostics\, drug discover
 y\, and personalized treatment planning\, thereby improving accuracy\, eff
 iciency\, and patient outcomes. In parallel\, Industry 4.0 leverages deep 
 learning to enhance smart manufacturing\, predictive maintenance\, supply 
 chain optimization\, and human&ndash\;machine collaboration\, fostering re
 silient and adaptive industrial ecosystems. This paper explores the conver
 gence of deep learning techniques with healthcare and Industry 4.0 applica
 tions\, highlighting key methodologies\, challenges\, and opportunities. B
 y synthesizing recent advancements\, we demonstrate how deep learning not 
 only accelerates digital transformation but also establishes a foundation 
 for intelligent\, data-driven decision-making in critical sectors. The dis
 cussion underscores the importance of explainability\, scalability\, and e
 thical considerations to ensure sustainable integration of deep learning i
 nto real-world systems.</p>
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