Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
A comprehensive survey on optimizing deep learning models by metaheuristics
Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn
higher levels of feature hierarchy established by lower level features by transforming the raw …
higher levels of feature hierarchy established by lower level features by transforming the raw …
Hybrid binary grey wolf with Harris hawks optimizer for feature selection
Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in
local optima areas may still be a concern. Several significant GWO factors can be explored …
local optima areas may still be a concern. Several significant GWO factors can be explored …
Adaptive multifactorial evolutionary optimization for multitask reinforcement learning
Evolutionary computation has largely exhibited its potential to complement conventional
learning algorithms in a variety of machine learning tasks, especially those related to …
learning algorithms in a variety of machine learning tasks, especially those related to …
Enhancing internet of things network security using hybrid CNN and xgboost model tuned via modified reptile search algorithm
This paper addresses the critical security challenges in the internet of things (IoT) landscape
by implementing an innovative solution that combines convolutional neural networks …
by implementing an innovative solution that combines convolutional neural networks …
A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances
Deep learning is the most dominant area to perform the complex challenging tasks such as
image classification and recognition. Earlier researchers have been proposed various …
image classification and recognition. Earlier researchers have been proposed various …
Optimization of convolutional neural network using the linearly decreasing weight particle swarm optimization
T Serizawa, H Fujita - arxiv preprint arxiv:2001.05670, 2020 - arxiv.org
Convolutional neural network (CNN) is one of the most frequently used deep learning
techniques. Various forms of models have been proposed and im-proved for learning at …
techniques. Various forms of models have been proposed and im-proved for learning at …
Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges
Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …
Learning models for several purposes: from the discovery of network topologies and …
Hyper-parameter selection in convolutional neural networks using microcanonical optimization algorithm
The success of Convolutional Neural Networks is highly dependent on the selected
architecture and the hyper-parameters. The need for the automatic design of the networks is …
architecture and the hyper-parameters. The need for the automatic design of the networks is …
A genetic algorithm for convolutional network structure optimization for concrete crack detection
A genetic algorithm (GA), is used to optimize the many parameters of a convolutional neural
network (CNN) that control the structure of the network. CNNs are used in image …
network (CNN) that control the structure of the network. CNNs are used in image …