A review on community detection in large complex networks from conventional to deep learning methods: A call for the use of parallel meta-heuristic algorithms
Complex networks (CNs) have gained much attention in recent years due to their
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks
Abstract In recent years, Deep Learning models have shown a great performance in
complex optimization problems. They generally require large training datasets, which is a …
complex optimization problems. They generally require large training datasets, which is a …
Evolutionary architectural search for generative adversarial networks
The Generative Adversarial Network (GAN) has shown powerfulness in various real-world
artificial intelligence applications. However, its network architecture is generally designed …
artificial intelligence applications. However, its network architecture is generally designed …
Fitness and distance based local search with adaptive differential evolution for multimodal optimization problems
Local search has been regarded as a promising technique in multimodal algorithms to refine
the accuracy of found multiple optima. However, how to execute the local search operations …
the accuracy of found multiple optima. However, how to execute the local search operations …
Proposal of svm utility kernel for breast cancer survival estimation
The advancement of medical research in the field of cancer prognosis and diagnosis using
various modalities has put oncologists under tremendous stress. The complexity and …
various modalities has put oncologists under tremendous stress. The complexity and …
MSGCA: Drug-disease associations prediction based on multi-similarities graph convolutional autoencoder
Y Wang, YL Gao, J Wang, F Li… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Identifying drug-disease associations (DDAs) is critical to the development of drugs.
Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is …
Traditional methods to determine DDAs are expensive and inefficient. Therefore, it is …
Satellite image recognition using ensemble neural networks and difference gradient positive-negative momentum
The modern machine learning theory finds application in many areas of human activity. One
of the most dispersed tasks is pattern recognition on satellite images. It is difficult for a …
of the most dispersed tasks is pattern recognition on satellite images. It is difficult for a …
A systematic approach to find the hyperparameters of artificial neural networks applied to damage detection in composite materials
MJ Fogaça, EL Cardoso, R de Medeiros - Journal of the Brazilian Society …, 2023 - Springer
Abstract Artificial Neural Networks applied to Structural Health Monitoring (SHM) have been
used to detect damage in composite structures. However, tuning the ANN architecture and …
used to detect damage in composite structures. However, tuning the ANN architecture and …
Bio-optimization of deep learning network architectures
P Shanmugavadivu, P Chitra… - Security and …, 2022 - search.proquest.com
Deep learning is reaching new heights as a result of its cutting-edge performance in a
variety of fields, including computer vision, natural language processing, time series …
variety of fields, including computer vision, natural language processing, time series …
Data-Driven Fracture Morphology Prognosis from High Pressured Modified Proppants Based on Stochastic-Adam-RMSprop Optimizers; tf. NNR Study
Data-driven models with some evolutionary optimization algorithms, such as particle swarm
optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale …
optimization (PSO) and ant colony optimization (ACO) for hydraulic fracturing of shale …