Resistive random access memory is very well known for its potential application in in-
memory and neural computing. However, they often have different types of device-to-device …

In recent years, cellular neural networks (CNNs) have become a popular apparatus for
simulations in neuroscience, biology, medicine, computer sciences and engineering. In …

This study investigates the prediction of the California Bearing Ratio (CBR) for nano-silica
and bio-char stabilized soft sub-grade soils using explainable machine learning (ML) …

The global energy demand is rising, driven by population growth, economic development,
and industrialization. Shifting towards renewable energy, like solar energy, is gaining …

In order to gain more insight into the nature of the disease and fill in numerous gaps in
predictions and treatment, the problem of Alzheimer's disease (AD) has been extensively …

In the rapidly evolving world of the energy sector, harnessing the power of neural networks
and machine learning becomes crucial. This chapter deals with the intricate dimensions of …

The cost optimization of structures is one of the options employers consider when designing
and analyzing them. Shear walls are one of the factors that influence the total cost of a …

This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as
Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico …

In thermal analysis modeling, the finite element method (FEM) is commonly used; however,
it incurs high computational costs and complicates the global optimization of thermal …

The Artificial rabbit optimization (ARO) algorithm replicates the survival skills of rabbits in the
wild. However, like other metaheuristic approaches it possesses significant drawbacks in …