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 …
Binary chimp optimization algorithm (BChOA): a new binary meta-heuristic for solving optimization problems
Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired
by chimps' individual intelligence and sexual motivation in their group hunting. The …
by chimps' individual intelligence and sexual motivation in their group hunting. The …
Improving the thermo-electro-mechanical responses of MEMS resonant accelerometers via a novel multi-layer perceptron neural network
S Lu, S Li, M Habibi, H Safarpour - Measurement, 2023 - Elsevier
Microelectromechanical systems (MEMS) accelerometers have been considerably
developed since their wide use in current industrial applications. MEMS resonant …
developed since their wide use in current industrial applications. MEMS resonant …
A Novel hybrid artificial bee colony-based deep convolutional neural network to improve the detection performance of backscatter communication systems
Backscatter communication (BC) is a promising technology for low-power and low-data-rate
applications, though the signal detection performance is limited since the backscattered …
applications, though the signal detection performance is limited since the backscattered …
A novel multi-objective binary chimp optimization algorithm for optimal feature selection: Application of deep-learning-based approaches for SAR image classification
Removing redundant features and improving classifier performance necessitates the use of
meta-heuristic and deep learning (DL) algorithms in feature selection and classification …
meta-heuristic and deep learning (DL) algorithms in feature selection and classification …
Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a …
Land cover classification is one of the most important applications of POLSAR images. In
this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) …
this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) …
Optimizing long short-term memory network for air pollution prediction using a novel binary chimp optimization algorithm
Elevated levels of fine particulate matter (PM2. 5) in the atmosphere present substantial risks
to human health and welfare. The accurate assessment of PM2. 5 concentrations plays a …
to human health and welfare. The accurate assessment of PM2. 5 concentrations plays a …
Optimal band selection using evolutionary machine learning to improve the accuracy of hyper-spectral images classification: A novel migration-based particle swarm …
In the domain of real-world concept learning, feature selection plays a crucial role in
accelerating learning processes and enhancing the quality of classification concepts …
accelerating learning processes and enhancing the quality of classification concepts …
[PDF][PDF] A Novel Attack on Complex APUFs Using the Evolutionary Deep Convolutional Neural Network.
AA Shahrakht, P Hajirahimi, O Rostami… - Intelligent Automation & …, 2023 - researchgate.net
As the internet of things (IoT) continues to expand rapidly, the significance of its security
concerns has grown in recent years. To address these concerns, physical unclonable …
concerns has grown in recent years. To address these concerns, physical unclonable …
[PDF][PDF] A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications.
For many years, researchers have explored power allocation (PA) algorithms driven by
models in wireless networks where multiple-user communications with interference are …
models in wireless networks where multiple-user communications with interference are …