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An evolutionary algorithm of linear complexity: application to training of de...
The performance of deep neural networks, such as Deep Belief Networks formed by Restricted Boltzmann Machines (RBMs), strongly depends on their training, which is the process of... -
Comparison of Parallel Versions of SA and GA for Optimizing the Performance o...
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving... -
Efficient training of deep learning models through improved adaptive sampling
Training of Deep Neural Networks (DNNs) is very computationally demanding and resources are typically spent on training-instances that do not provide the most benefit to a... -
Robust parameter estimation of a PEMFC via optimization based on probabilisti...
Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive... -
Homicide forecasting for the state of Guanajuato using LSTM and geospatial in...
In the last years, intentional homicides have increased significantly in Mexico. A proven strategy to confront the problem is applying predictive methods used to anticipate the... -
Feature Analysis for Urban-land Change of Morelia City Via the TOC Curve
The feature analysis for feature selection is an important step for defining the inputs of the simulation models for urban growth. The simulation models are valuable tools for... -
Enhancing Epidemic Prediction Using Simulated Annealing for Parameter Optimiz...
Understanding and predicting outbreaks of epidemics has become a major focus since COVID-19. Researchers have explored various methods, from basic curve fitting to complex...
