Author(s)

Daniel Tang ¹

Affiliation(s)

¹ Fairmont School, California, USA

Corresponding Author

Daniel Tang

ABSTRACT

In the world of enterprise management, receiving numerous text messages is an everyday thing. As businesses expand, the amount of text data multiplies rapidly, often causing an excess of information. This can delay management's response to urgent matters or problems faced by frontline staff. To solve this, we suggest creating a model that recognizes issues using daily employee data. This model can handle large amounts of text, drawing out both clear and hidden features to build a complex network model. Its main job is to give early warnings and predictions about unusual or significant events. This helps businesses operate more smoothly and make smarter choices based on what's likely to happen. We've tested this model, and the results show it works well. Its predictions demonstrate an ability to accurately identify problems and risks, making it a valuable resource for business management. 

KEYWORDS

Deep diffusion models, neural network, Natural language processing

CITE THIS PAPER

Daniel Tang, Anomaly Recognition Based on Deep Diffusion Neural Network. Automation and Machine Learning (2024) Vol. 5: 72-81. DOI: http://dx.doi.org/10.23977/autml.2024.050208.

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