The identification of biomarkers plays a crucial role in personalized medicine, both in the
clinical and research settings. However, the contrast between predictive and prognostic …

Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with
remarkable performance when solving diverse optimization problems. However, PSO faces …

Single-solution-based optimization algorithms have gained little to no attention by the
research community, unlike population-based approaches. This paper proposes a novel …

The emergence and application of geological models have contributed to new assessment
schemes for oil and gas reservoir development. The model simulates stratigraphic …

Survival models exist to study relationships between biomarkers and treatment effects. Deep
learning-powered survival models supersede the classical Cox proportional hazards …

The most crucial aspect in determining field development plans is the oil recovery factor
(RF). However, RF has a complex relationship with the reservoir rock and fluid properties …

Generative Adversarial Networks (GANs) are neural networks that allow models to learn
deep representations without requiring a large amount of training data. Semi-Supervised …

Features selection methods not only reduce the dimensionality, but also improve
significantly the classification results. In this study, the effect of the initialization population …

In computer vision, convolutional neural networks have a wide range of uses. Images
represent most of today's data, so it's important to know how to handle these large amounts …

Efficiently classifying potential areas of remaining oil is essential for enhancing the recovery
in high water cut reservoir. The distribution of remaining oil is complex and challenging to …