Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review
M Kaveh, MS Mesgari - Neural Processing Letters, 2023 - Springer
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 …
Eight years of AutoML: categorisation, review and trends
Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …
applied in a large number of application domains. However, apart from the required …
[HTML][HTML] CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images
CB Gonçalves, JR Souza, H Fernandes - Computers in Biology and …, 2022 - Elsevier
The early detection of breast cancer is a vital factor when it comes to improving cure and
recovery rates in patients. Among such early detection factors, one finds thermography, an …
recovery rates in patients. Among such early detection factors, one finds thermography, an …
Simplified swarm optimization for hyperparameters of convolutional neural networks
Convolutional neural networks (CNNs) are widely used in image recognition. Numerous
CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been developed …
CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been developed …
A combination of deep learning and genetic algorithm for predicting the compressive strength of high‐performance concrete
This article presented an efficient deep learning technique to predict the compressive
strength of high‐performance concrete (HPC). This technique combined the convolutional …
strength of high‐performance concrete (HPC). This technique combined the convolutional …
Smart systems for real-time bearing faults diagnosis by using vibro-acoustics sensor fusion with Bayesian optimised 1-D CNNs
A Mamun, D Guerra-Zubiaga… - Nondestructive Testing and …, 2024 - Taylor & Francis
Diagnosis of bearing faults in real-time is challenging when healthy bearing conditions are
mixed with faulty ones, affecting the overall system of rotating machinery. Deep Learning …
mixed with faulty ones, affecting the overall system of rotating machinery. Deep Learning …
Streamline video-based automatic fabric pattern recognition using Bayesian-optimized convolutional neural network
Examining fabric weave patterns (FWPs) is connected to image-based surface texture
feature (STF) acquisition, which can be difficult due to the structural complexity of woven …
feature (STF) acquisition, which can be difficult due to the structural complexity of woven …
Convolution neural network hyperparameter optimization using simplified swarm optimization
Convolutional neural networks (CNNs) are widely used in image recognition. Numerous
CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been proposed …
CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been proposed …
Two-level genetic algorithm for evolving convolutional neural networks for pattern recognition
DA Montecino, CA Perez, KW Bowyer - IEEE Access, 2021 - ieeexplore.ieee.org
The aim of Neuroevolution is to find neural networks and convolutional neural network
(CNN) architectures automatically through evolutionary algorithms. A crucial problem in …
(CNN) architectures automatically through evolutionary algorithms. A crucial problem in …
White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO2 Emission Design of Retaining Structures
This study proposes a new metaheuristic optimization algorithm, namely the white-tailed
eagle algorithm (WEA), for global optimization and optimum design of retaining structures …
eagle algorithm (WEA), for global optimization and optimum design of retaining structures …