The application of artificial intelligence in everyday life is becoming all-pervasive and
unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired …

Abstract Background Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent,
and deadly type of liver cancer. With the advent of deep learning techniques, significant …

Feature selection (FS) is the activity of defining the most contributing feature subset among
all used features to improve the superiority of datasets with a large number of dimensions by …

Studies focusing on the optimization of waste-to-energy systems using metaheuristic particle
swarm optimization (PSO) are crucial in our pursuit of sustainable and renewable energy …

Feature selection (FS) constitutes a crucial endeavor in classification procedures, aiming to
identify the minimal subset of features that maximizes classification accuracy. In the realm of …

Directly applying high-dimensional data to machine learning leads to dimensionality
disasters and may induce model overfitting. Feature selection can effectively reduce feature …

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization
Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The …

Feature selection is becoming a relevant problem within the field of machine learning. The
feature selection problem focuses on the selection of the small, necessary, and sufficient …

Contemporary advancements in technology provide vast quantities of data with large
dimensions, leading to high computing burdens. These big data quantities suffer from …

Feature selection algorithms are crucial technologies for reducing the dimensionality of high-
dimensional data. However, the exponential expansion of the decision space due to high …