India has the largest deaf population in the world and sign language is the principal medium
for such persons to share information with normal people and among themselves. Yet, …

Stochastic Gradient Decent (SGD) is a very popular basic optimizer applied in the learning
algorithms of deep neural networks. However, it has fixed-sized steps for every epoch without …

Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large
volume datasets and applied in a wide range of applications. The DNNs are trained on …

Deep Neural Networks (DNNs) that aim to maximize accuracy and decrease loss can be
trained using optimization algorithms. One of the most significant fields of research is the …

For deep model training, an optimization technique is required that minimizes loss and
maximizes accuracy. The development of an effective optimization method is one of the most …

Training of deep learning models requires an optimization algorithm that minimizes error and
maximizes accuracy. The development of an efficient optimization algorithm is one of the …

Stochastic gradient-based optimizers are used to train convolutional neural networks (CNNs).
Due to its adaptable momentum, the Adam optimizer has recently gained a lot of attention …

The current paper has focused on finding the optimum cavity properties to enhance the mixing
phenomenon and combustion processes in supersonic flow using Computational Fluid …

For deep neural networks, gradient descent (GD) is the backbone. Slow convergence is an
issue with GD. Using momentum is the well-known method of overcoming delayed …

Deep learning [1, 2] simulates the way the human brain makes decisions, creates patterns,
and processes data using artificial neural network (ANN) to analyze the data. To accomplish it…