Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

One in every eight women is susceptible to breast cancer, at some point of time in her life.
Early detection and effective treatment is the only rescue to reduce breast cancer mortality …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

This paper introduces a proposed model for mid-term to short-term load forecasting (MTLF;
STLF) that can be used to forecast loads at different hours and on different days of each …

Cartesian genetic programming, a well-established method of genetic programming, is
approximately 20 years old. It represents solutions to computational problems as graphs. Its …

In healthcare industry, Neural Network has attained a milestone in solving many real-life
classification problems varies from very simple to complex and from linear to non-linear. To …

The purpose of this research is to provide power grid energy efficiency solutions. In this
paper, a comprehensive review and its optimal solution is proposed considering the various …

We introduce SPLASH units, a class of learnable activation functions shown to
simultaneously improve the accuracy of deep neural networks while also improving their …

In the last decade, an active area of research has been devoted to design novel activation
functions that are able to help deep neural networks to converge, obtaining better …