Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Qing Wei and colleagues from the College of Engineering, China Agricultural University, systematically elaborated on the ...

Fig. 1 shows the mapping of points from the training sample in the coordinates of the two main features – u1 and u2. The color of the point corresponds to the class (red – 0, aqua – 1). From the ...

Neural networks have emerged as a pivotal technology in enhancing the precision and reliability of depth of anaesthesia (DoA) monitoring. By integrating advanced signal processing techniques with ...

Multifunction radar systems have evolved to perform a host of tasks from surveillance to target tracking, realising complex electronic support measures with high precision. At the heart of these ...

Overview Books provide a deeper understanding of AI concepts beyond running code or tutorials.Hands-on examples and practical ...

Journal of Housing and the Built Environment, Vol. 18, No. 2 (2003), pp. 159-181 (23 pages) In recent years, the neural network modelling technique has become a serious alternative to and extension of ...

A topic that's often very confusing for beginners when using neural networks is data normalization and encoding. Because neural networks work internally with numeric data, binary data (such as sex, ...

While nanotechnology combines the knowledge of physics, chemistry and engineering, AI has heavily relied on biological inspiration to develop some of its most effective paradigms such as neural ...

Weight decay and weight restriction are two closely related, optional techniques that can be used when training a neural network. This article explains exactly what weight decay and weight restriction ...