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 …

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …

Convolutional neural network (CNN) has more and more applications in image recognition.
However, the structure of CNN is often determined after a performance comparison among …

During the last decade, deep neural networks have shown a great performance in many
machine learning tasks such as classification and clustering. One of the most successful …

Over the last decade, deep neural networks have shown great success in the fields of
machine learning and computer vision. Currently, the CNN (convolutional neural network) is …

Over the years conventional neural networks has shown state-of-art performance on many
problems. However, their performance on recognition system is still not widely accepted in …

This paper proposes an improved stochastic second order learning algorithm for supervised
neural network training. The proposed algorithm, named bounded stochastic diagonal …

Developing a convolutional neural network (CNN) for medical image segmentation is a
complex task, especially when dealing with the limited number of available labelled medical …