Modeling accuracy of DC-DC converters may deviate largely in the presence of different
variation levels of constant power loads (CPLs), hence is well acknowledged as a main …

Recently, enormous datasets have made power dissipation and area usage lie at the heart
of designs for artificial neural networks (ANNs). Considering the significant role of activation …

This paper presents a hybrid machine learning method for the prediction of concrete
expansion induced by alkali-silica reaction (ASR) and assembles a comprehensive and …

Crowd counting is very important in many tasks such as video surveillance, traffic
monitoring, public security, and urban planning, so it is a very important part of the intelligent …

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron
model (DNM). Neural networks become larger and larger, thus consuming more and more …

One metaheuristic algorithm recently introduced is atom search optimization (ASO), inspired
by the physical movement of atoms based on the molecular dynamics in nature. ASO …

The manufacturing of nanomaterials by the electrospinning process requires accurate and
meticulous inspection of related scanning electron microscope (SEM) images of the …

Effort estimation is the most critical activity for the success of overall solution delivery in
software engineering projects. In this context, the paper's main contributions to the literature …

Multiparameter water quality trend prediction technique is one of the important tools for
water environment management and regulation. This study proposes a new water quality …

Feature selection (FS), which aims to select informative feature subsets and improve
classification performance, is a crucial data-mining technique. Recently, swarm intelligence …