AZoSensors

AI Maps Heat Inside Steelmaking’s Critical Sintering Process Beds

The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical ...
eLife

High-Fidelity Neural Speech Reconstruction through an Efficient Acoustic-Linguistic Dual-Pathway Framework

This study presents a valuable advance in reconstructing naturalistic speech from intracranial ECoG data using a dual-pathway model. The evidence supporting the claims of the authors is solid, ...
Scientific Research Publishing

Sebastian, G., Parmisano, A. and Erquiaga, M.J. (2020) IoT-23: A Labeled Dataset with Malicious and Benign IoT Network Traffic.

ABSTRACT: This work presents a new method to improve the security of Internet of Things (IoT) networks using a model combining an autoencoder LSTM (AE-LSTM) and a centralized multi-agent reinforcement ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
GitHub

Aikhusy/Progress-LSTM-AUTOENCODER

fw_load_avg_1_min fw_load_avg_5_min fw_load_avg_15_min fw_cpu_used mem_used root_used log_used fw_total_alloc model saya menemukan bahwa [31, 34, 47, 48, 49, 50, 51 ...
IEEE

Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection

Abstract: The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description ...
GitHub

README.md

This project implements a real-time anomaly detection system using an LSTM Autoencoder to analyze continuous data streams. The system is designed to detect unusual patterns, such as exceptional values ...