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DTSTAMP:20260421T090514Z
LOCATION:Bldg. 6 - Room 104
DTSTART;TZID=Europe/Stockholm:20260630T141500
DTEND;TZID=Europe/Stockholm:20260630T144500
UID:submissions.pasc-conference.org_PASC26_sess146_msa231@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)\n\nTransparency and interpretabili
 ty are essential for deploying AI systems in domains where decisions affec
 t health, safety, and food security. This talk presents an integrated appr
 oach to building trustworthy AI models for image-based classification by c
 ombining deep learning with Explainable Artificial Intelligence (XAI) tech
 niques. Applications in both skin lesion identification and plant disease 
 detection demonstrate how visual explanations highlight the features influ
 encing model predictions. These insights allow clinicians, agricultural sp
 ecialists, and other stakeholders to verify the reasoning behind AI output
 s, detect potential biases, and gain confidence in model reliability. The 
 talk also addresses the role of high-performance computing in supporting l
 arge-scale training and real-time interpretability. By aligning predictive
  performance with transparent decision-making, this work outlines a practi
 cal framework for developing AI systems that are accurate, explainable, an
 d ready for real-world adoption.\nKeywords: High-Performance Computing, De
 ep Learning, Image Processing, Health Informatics, Agricultural Informatic
 s, Explainable AI (XAI)\n\nDomain: Life Sciences, Computational Methods an
 d Applied Mathematics\n\nSession Chair: Elaine M. Raybourn (University of 
 Central Florida)\n\n
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