Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

Deep learning researchers have a keen interest in proposing two new novel activation
functions which can boost network performance. A good choice of activation function can …

Solar energy is one of the most prominent renewable energy sources today and it can
contribute to the energy market with accurate solar radiation estimation. This study proposes …

Deep Learning in the field of Big Data has become essential for the analysis and perception
of trends. Activation functions play a crucial role in the outcome of these deep learning …

The activation function has a critical influence on whether a convolutional neural network in
deep learning can converge or not; a proper activation function not only makes the …

Though single image super-resolution (SR) has witnessed incredible progress, the
increasing model complexity impairs its applications in memory-limited devices. To solve …

Activation functions are used to extract meaningful relationships from real-world problems
with the help of deep learning models. Thus, the development of activation functions which …

Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the
origin. Over the years, many smooth approximations of ReLU have been proposed using …

Deep learning (DL) models have shown promise for the accurate detection of atrial
fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet …

Recent research has found that the activation function (AF) plays a significant role in
introducing non-linearity to enhance the performance of deep learning networks …