IEEE

Anomaly Detection in GeoLLM Using Variational Autoencoder

Abstract: Geospatial-based Large Language Models are an emerging technology based on the geospatial locations and the different applications based on the locations. From the survey, it is seen that ...
IEEE

Wavelet-Enhanced Temporal Convolutional Autoencoder with Self-Attention for Unsupervised Anomaly Detection in High-Performance Computing Systems

Abstract: We propose a novel unsupervised anomaly detection framework for high-performance computing (HPC) systems, integrating wavelet-based frequency augmentation with a self-attentive temporal ...
GitHub

autoencoder-anomaly-detection

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...
Scientific Research Publishing

Intelligent Agents in Cybersecurity: Deep Learning to Analyze User Behavior Applying ()

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...
GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...