Recent advances in surrogate modeling methods for uncertainty quantification and propagation
C Wang, X Qiang, M Xu, T Wu - Symmetry, 2022 - mdpi.com
Surrogate-model-assisted uncertainty treatment practices have been the subject of
increasing attention and investigations in recent decades for many symmetrical engineering …
increasing attention and investigations in recent decades for many symmetrical engineering …
A Fine‐Tuned BERT‐Based Transfer Learning Approach for Text Classification
Text Classification problem has been thoroughly studied in information retrieval problems
and data mining tasks. It is beneficial in multiple tasks including medical diagnose health …
and data mining tasks. It is beneficial in multiple tasks including medical diagnose health …
Analysis of brain imaging data for the detection of early age autism spectrum disorder using transfer learning approaches for internet of things
In recent years, advanced magnetic resonance imaging (MRI) methods including as
functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging …
functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging …
Genetic algorithms: Brief review on genetic algorithms for global optimization problems
An intelligent bionic algorithm with great global optimization potential, the genetic algorithm
evolved in a manner analogous to the natural process of genetic evolution in living …
evolved in a manner analogous to the natural process of genetic evolution in living …
Detection of Fake News Text Classification on COVID‐19 Using Deep Learning Approaches
A vast amount of data is generated every second for microblogs, content sharing via social
media sites, and social networking. Twitter is an essential popular microblog where people …
media sites, and social networking. Twitter is an essential popular microblog where people …
Constructing domain ontology for Alzheimer disease using deep learning based approach
Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A
range of diverse values can be produced by mining ontological approaches for …
range of diverse values can be produced by mining ontological approaches for …
An improved particle swarm optimization algorithm for data classification
Optimisation-based methods are enormously used in the field of data classification. Particle
Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely …
Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely …
Improved opposition-based particle swarm optimization algorithm for global optimization
Particle Swarm Optimization (PSO) has been widely used to solve various types of
optimization problems. An efficient algorithm must have symmetry of information between …
optimization problems. An efficient algorithm must have symmetry of information between …
A review of the use of quasi-random number generators to initialize the population in meta-heuristic algorithms
Different computational tools require random numbers to operate; this is the case with Meta-
heuristic Algorithms (MA's). Many studies in the literature have demonstrated that the spatial …
heuristic Algorithms (MA's). Many studies in the literature have demonstrated that the spatial …
Deep learning at the service of metaheuristics for solving numerical optimization problems
Integrating deep learning methods into metaheuristic algorithms has gained attention for
addressing design-related issues and enhancing performance. The primary objective is to …
addressing design-related issues and enhancing performance. The primary objective is to …