June 11, 2024, 4:49 a.m. | Ahmed Maged, Salah Haridy, Herman Shen

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.11597v2 Announce Type: replace-cross
Abstract: As the manufacturing industry advances with sensor integration and automation, the opaque nature of deep learning models in machine learning poses a significant challenge for fault detection and diagnosis. And despite the related predictive insights Artificial Intelligence (AI) can deliver, advanced machine learning engines often remain a black box. This paper reviews the eXplainable AI (XAI) tools and techniques in this context. We explore various XAI methodologies, focusing on their role in making AI decision-making …

abstract advanced advances artificial artificial intelligence arxiv automation challenge cs.ai cs.lg deep learning detection diagnosis explainable artificial intelligence industry insights integration intelligence machine machine learning manufacturing manufacturing industry nature opaque predictive predictive insights replace review sensor type

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