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

Abstract: In this letter, we propose a Graph-Attention-Recurrent Neural Network (Graph-ARNN) to improve UWB localization in complex environment by incorporating spatial, temporal and geometric ...

Abstract: In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...

ABSTRACT: To effectively evaluate a system that performs operations on UML class diagrams, it is essential to cover a large variety of different types of diagrams. The coverage of the diagram space ...

Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning! New details in Charlie Kirk shooting as his widow breaks her silence Trump ...

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...

Hands-on coding of a multiclass neural network from scratch, with softmax and one-hot encoding. #Softmax #MulticlassClassification #PythonAI The 2 House Republicans who voted no on Trump's sweeping ...