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AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications.
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy".
A Survey of Transformer Enabled Time Series Synthesis
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art.
Generating Synthetic Time Series Data for Cyber-Physical Systems
Data augmentation is an important facilitator of deep learning applications in the time series domain.
Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities
This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
