A Hybrid LSTM-ResNet Deep Neural Network for Noise Reduction and Classification of V-Band Receiver Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3147980
Abstract: Noise reduction is one of the most important process used for signal processing in communication systems. The signal-to-noise ratio (SNR) is a key parameter to consider for minimizing the bit error rate (BER). The inherent… read more here.
Keywords: neural network; noise; noise reduction; lstm autoencoder ... See more keywords
Detecting Web Attacks From HTTP Weblogs Using Variational LSTM Autoencoder Deviation Network
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Services Computing"
DOI: 10.1109/tsc.2024.3453748
Abstract: Web attacks penetrate the web applications’ security through unauthorized access to sensitive information, disrupting services, and stealing data. Conventionally, rule-based statistical methods distinguish attackers from legitimate users. However, the training through manually extracted weblog features… read more here.
Keywords: lstm autoencoder; variational lstm; web attacks; network ... See more keywords
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
Sign Up to like & getrecommendations! Published in 2024 at "Energies"
DOI: 10.3390/en17102340
Abstract: Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of… read more here.
Keywords: lstm autoencoder; autoencoder; deep learning; anomaly detection ... See more keywords