ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...
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 ...
├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?
Information and communication technology (ICT) is crucial for maintaining efficient communications, enhancing processes, and enabling digital transformation. As ICT becomes increasingly significant in ...
ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
Abstract: Video anomaly detection (VAD) is of great importance for a variety of real-time applications in video surveillance. Most deep learning-based anomaly detection algorithms adopt a one-class ...
They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...
Magnetic data boundary detection is a key technology in potential field data processing, providing an effective basis for the division of geological units and fault structures. It holds significant ...
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