Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
From federated learning to federated neural architecture search: a survey
Federated learning is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …
preservation, which has found a wide range of applications where data privacy is of primary …
Federated neural architecture search for medical data security
X Liu, J Zhao, J Li, B Cao, Z Lv - IEEE transactions on industrial …, 2022 - ieeexplore.ieee.org
Medical data widely exist in the hospital and personal life, usually across institutions and
regions. They have essential diagnostic value and therapeutic significance. The disclosure …
regions. They have essential diagnostic value and therapeutic significance. The disclosure …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
A review of deep learning algorithms and their applications in healthcare
H Abdel-Jaber, D Devassy, A Al Salam, L Hidaytallah… - Algorithms, 2022 - mdpi.com
Deep learning uses artificial neural networks to recognize patterns and learn from them to
make decisions. Deep learning is a type of machine learning that uses artificial neural …
make decisions. Deep learning is a type of machine learning that uses artificial neural …
Graph representation learning meets computer vision: A survey
A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …
information about individuals but also capture the interactions between individuals for …
[HTML][HTML] Learning deep neural networks' architectures using differential evolution. Case study: medical imaging processing
S Belciug - Computers in biology and medicine, 2022 - Elsevier
The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and
obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal …
obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal …
A review on convolutional neural network encodings for neuroevolution
GA Vargas-Hakim, E Mezura-Montes… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have shown outstanding results in different
application tasks. However, the best performance is obtained when customized CNNs …
application tasks. However, the best performance is obtained when customized CNNs …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
A survey of designing convolutional neural network using evolutionary algorithms
Convolutional neural networks (CNN) are highly effective for image classification and
computer vision activities. The accuracy of CNN architecture depends on the design and …
computer vision activities. The accuracy of CNN architecture depends on the design and …