Training optimization plays a vital role in the development of convolution neural network
(CNN). CNNs are hard to train because of the presence of multiple local minima. The …

Training of the convolution neural network (CNN) is a problem of global optimisation. This
study proposed a hybrid modified particle swarm optimisation (MPSO) and conjugate …

Neural network attracts plenty of researchers lately. Substantial number of renowned
universities have developed neural network for various both academically and industrially …

Deep neural networks have accomplished enormous progress in tackling many problems.
More specifically, convolutional neural network (CNN) is a category of deep networks that …

In recent years, Convolutional Neural Networks (CNNs) have been widely used in image
recognition due to their aptitude in large scale image processing. The CNN uses Back …

Recent advances in swarm inspired optimization algorithms have shown its extensive
acceptance in solving a wide range of different real-world problems. Particle Swarm …

Convolutional Neural Networks (CNNs) have demonstrated great potential in complex
image classification problems in past few years. CNNs have a large number of parameters …

Convolution neural network (CNN) is a kind of powerful and efficient deep learning
approach that has obtained great success in many real-world applications. However, due to …

CNN has recently gained popularity in the field of image processing. It has proven its niche
in the field of machine learning. Computational models which use biological computation …

Convolutional neural network (CNN) is one of the most frequently used deep learning
techniques. Various forms of models have been proposed and im-proved for learning at …