Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review
The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …
and deep learning (DL) architectures is considered one of the most challenging machine …
Overcoming the limits of cross-sensitivity: pattern recognition methods for chemiresistive gas sensor array
As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are
often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases …
often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
An ensemble of differential evolution and Adam for training feed-forward neural networks
Adam is an adaptive gradient descent approach that is commonly used in back-propagation
(BP) algorithms for training feed-forward neural networks (FFNNs). However, it has the …
(BP) algorithms for training feed-forward neural networks (FFNNs). However, it has the …
Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems
In this paper, we propose a new metaheuristic algorithm based on Lévy flight called Lévy
flight distribution (LFD) for solving real optimization problems. The LFD algorithm is inspired …
flight distribution (LFD) for solving real optimization problems. The LFD algorithm is inspired …
A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns
The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
Estimating construction waste generation in the Greater Bay Area, China using machine learning
Reliable construction waste generation data is a prerequisite for any evidence-based waste
management effort, but such data remains scarce in many develo** economies owing to …
management effort, but such data remains scarce in many develo** economies owing to …
Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm
J **a, D Yang, H Zhou, Y Chen, H Zhang, T Liu… - Computers in Biology …, 2022 - Elsevier
Kernel extreme learning machine (KELM) has been widely used in the fields of classification
and identification since it was proposed. As the parameters in the KELM model have a …
and identification since it was proposed. As the parameters in the KELM model have a …
An efficient hybrid multilayer perceptron neural network with grasshopper optimization
This paper proposes a new hybrid stochastic training algorithm using the recently proposed
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …
Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy
Since its introduction, kernel extreme learning machine (KELM) has been widely used in a
number of areas. The parameters in the model have an important influence on the …
number of areas. The parameters in the model have an important influence on the …