Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions

Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions

This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals...

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Bibliographic Details
Main Authors: Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Fractal and Fractional
Subjects:
anomaly detection
fractional dimension
deep learning
occupational safety
Online Access:https://www.mdpi.com/2504-3110/8/10/604
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