Abstract: Encrypted traffic classification is crucial for critical network management tasks such as traffic type identification, resource allocation, and risk mitigation, especially given that ...

Word Embedding (Python) is a technique to convert words into a vector representation. Computers cannot directly understand words/text as they only deal with numbers. So we need to convert words into ...

Abstract: Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support ...

Framework of DynIMTS. The model is a recurrent structure based on a spatial-temporal encoder and consists of three main components: embedding learning, spatial-temporal learning, and graph learning.

This project implements a drug-disease association prediction model using Graph Convolutional Networks (GCN) with advanced data augmentation techniques. The model predicts novel drug-disease ...

Whenever we try to save a keras-hub model after quantization, we are unable to load the quantized model. I've tried from_preset() method for that model, and also keras.models.load_model nothing works ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...