Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Medical image processing can lack images for diagnosis. Generative Adversarial Networks
(GANs) provide a method to train generative models for data augmentation. Synthesized …

Abstract The Multi-Layer Perceptron (MLP) has been applied into many real-world problems
as one of the most extensively used Neural Networks (NNs). It often suffers from local …

The situational awareness in intersection waterways should be improved given the
numerous accidents occurring in these areas. We propose a deep learning model for …

Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as
mode and discriminator collapse. Similar pathologies have been studied and addressed in …

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal
sequences. Designing an optimum deep RNN is difficult due to configuration and training …

Labeling images for classification can be expensive. Semi-Supervised Learning (SSL)
Generative Adversarial Network (GAN) methods train good classifiers with a few labeled …

Metaheuristic optimization algorithms play a crucial role in optimization problems. However,
the traditional identification methods have the following problems:(1) difficulties in nonlinear …

Recurrent neural networks (RNNs) are a powerful approach for time series prediction.
However, their performance is strongly affected by their architecture and hyperparameter …

Neural architecture search is a promising area of research dedicated to automating the
design of neural network models. This field is rapidly growing, with a surge of methodologies …