An artificial neural network is a deep learning model made up of neurons that mimic the human brain. Techopedia explains the full meaning here.

Deep learning models go above and beyond traditional machine learning and can process data and recognize patterns much more ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

MIT researchers developed an automated system that optimizes deep-learning models by leveraging both sparsity and symmetry in ...

Spiking Neural Networks (SNNs ... CBSNN and VPSNN share the same network architecture. The corresponding results are shown in Table 3. • Iris (Fisher, 1936) is a machine learning dataset for multiple ...

In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks ... the model with three classical deep learning ...

The “deeper” that the deep neural network is, the more sophisticated patterns the network can learn. Fully connected networks can be combined with other machine learning functions ... In other words ...

As artificial intelligence (AI) continues to revolutionize many sectors, the vital field of machine ... and ensemble learning methods are applied to improve the accuracy of a prediction, but more ...

Researchers at FORTH have developed a new type of artificial neural network (ANN) that incorporates features of biological ...

Abstract: Recently, spiking neural networks (SNNs) have demonstrated substantial potential ... By this way, our ESDNet can effectively detect and analyze the characteristics of rain streaks by ...

Researchers have explored how integrating machine and deep learning techniques can create a standardized system for evaluating rock climbing routes to provide a difficulty grading scale that promotes ...