Detection of Anomalies in Data Streams Using the LSTM-CNN Model

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1610 - 1610

Published: March 6, 2025

This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The results obtained on the popular Yahoo! Webscope S5 dataset are used for computational experiments. two commonly and recommended models literature, which basis this analysis, following: LSTM its more complicated variant, autoencoder. Additionally, usefulness an innovative LSTM-CNN approach is evaluated. indicate that can successfully be streams as performance compares favorably with mentioned standard models. For evaluation, F1score used.

Language: Английский

Comparison of energy consumption and global warming potential of electric and hydrogen-fueled vehicles across different product size

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(5)

Published: April 28, 2025

Language: Английский