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DTSTAMP:20260624T171341Z
LOCATION:Bldg. 6 - 003
DTSTART;TZID=Europe/Stockholm:20260701T153000
DTEND;TZID=Europe/Stockholm:20260701T160000
UID:submissions.pasc-conference.org_PASC26_sess117_msaSC102@linklings.com
SUMMARY:Building Trust in AI-Driven Systems: Scalable and Explainable Deep
  Learning Frameworks for Health and Agriculture on HPC
DESCRIPTION:Natasha Nigar (UET, Lahore, Pakistan)\n\nTransparency and inte
 rpretability are essential for deploying AI systems in domains where decis
 ions affect health, safety, and food security. This talk presents an integ
 rated approach to building trustworthy AI models for image-based classific
 ation by combining deep learning with Explainable Artificial Intelligence 
 (XAI) techniques. Applications in both skin lesion identification and plan
 t disease detection demonstrate how visual explanations highlight the feat
 ures influencing model predictions. These insights allow clinicians, agric
 ultural specialists, and other stakeholders to verify the reasoning behind
  AI outputs, detect potential biases, and gain confidence in model reliabi
 lity. The talk also addresses the role of high-performance computing in su
 pporting large-scale training and real-time interpretability. By aligning 
 predictive performance with transparent decision-making, this work outline
 s a practical framework for developing AI systems that are accurate, expla
 inable, and ready for real-world adoption.\n\nKeywords: High-Performance C
 omputing, Deep Learning, Image Processing, Health Informatics, Agricultura
 l Informatics, Explainable AI (XAI)\n\nDomain: Climate, Weather, and Earth
  Sciences, Applied Social Sciences and Humanities, Engineering, Computatio
 nal Methods and Applied Mathematics\n\nSession Chair: Tobias Hodel (Univer
 sity of Bern, Switzerland)\n\n
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