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 …

Activation functions play an important role in deep learning models by introducing non-
linearity to the output of a neuron, enabling the network to learn complex patterns and non …

The continuous increase in solar power integration with energy systems can be attributed to
the push for cleaner energy use globally, highlighting the importance of accurate solar …

Efficient integration of wind energy requires accurate wind power forecasting. This prediction
is critical in optimising grid operation, energy trading, and effectively harnessing renewable …

This paper focuses on the growth, development, and future of various forms of fractional-
order neural networks. Multiple advances in structure, learning algorithms, and methods …

In this paper, a series of novel activation functions is presented, which is derived using the
improved Riemann–Liouville conformable fractional derivative (RL CFD). This study …

In real-world datasets, missed data is often expected due to sensor errors, environmental
conditions, communication errors, and other technical limitations. These factors can affect …

This study proposes a conformable artificial neural network model to improve the void
fraction prediction in helical heat exchangers. The obtained model had only one neuron in …

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 …

This paper introduces a novel Riemann–Liouville (RL) conformable fractional derivative
based Adaptable-Shifted-Fractional-Rectified-Linear-Unit, briefly called RL ASFReLU, and …