The combination of renewable energy sources and prosumer-based smart grid is a
sustainable solution to cater to the problem of energy demand management. A pressing …

We propose SiCaGCN, a learning system to predict the similarity of a given software code to
a set of codes that are permitted to run on a computational resource, such as a …

The holistic analysis and understanding of the latent (that is, not directly observable)
variables and patterns buried in large datasets is crucial for data-driven science, decision …

This paper presents a novel incentive-based load shedding management scheme within a
microgrid environment equipped with the required IoT infrastructure. The proposed …

In machine learning, reducing the complexity of a model can help to improve its
computational efficiency and avoid overfitting. In genetic programming (GP), the model …

Background Identifying one or more biologically-active/native decoys from millions of non-
native decoys is one of the major challenges in computational structural biology. The …

The computational complexity of Evolutionary Algorithms (EAs) is a well-known concern.
This paper is concerned with the resource consumption of GELAB, a novel Grammatical …

A central challenge in protein modeling research and protein structure prediction in
particular is known as decoy selection. The problem refers to selecting biologically …

The energy landscape that organizes microstates of a molecular system and governs the
underlying molecular dynamics exposes the relationship between molecular form/structure …

Traditionally, reducing complexity in Machine Learning promises benefits such as less
overfitting. However, complexity control in Genetic Programming (GP) often means reducing …