Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …

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 …

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

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 …

To explore the complicated functional relationship between key parameters such as the
recycled aggregate properties, mix proportion and compressive strength of recycled …

Due to the global progress, the optimization problems are becoming more and more
complex. Hence, deterministic and heuristic approaches are no longer adequate for dealing …

Recently, many optimization algorithms have been applied for Feature selection (FS)
problems and show a clear outperformance in comparison with traditional FS methods …

The rapid expansion of information science has caused the issue of “the curse of
dimensionality”, which will negatively affect the performance of the machine learning model …

Grey Wolf Optimizer (GWO) simulates the grey wolves' nature in leadership and hunting
manners. GWO showed a good performance in the literature as a meta-heuristic algorithm …