With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …

The slime mould algorithm (SMA) is a population-based optimization algorithm that mimics
the foraging behavior of slime moulds with a simple structure and few hyperparameters …

The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy
to understand and has a strong optimization capability. However, the SMA is not suitable for …

This research's genesis is in two aspects: first, a guaranteed solution for mitigating the grey
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …

Introduction The main feature selection methods include filter, wrapper-based, and
embedded methods. Because of its characteristics, the wrapper method must include a …

Continuous ant colony optimization was a population-based heuristic search algorithm
inspired by the pathfinding behavior of ant colonies with a simple structure and few control …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

We propose an algorithm for seismic trace interpolation using generative adversarial
networks, a type of deep neural network. The method extracts feature vectors from the …

We propose to adopt a deep learning based framework using generative adversarial
networks for ground‐roll attenuation in land seismic data. Accounting for the non‐stationary …

We have developed a method to combine unsupervised and supervised deep-learning
approaches for seismic ground roll attenuation. The method consists of three components …