Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

Feature subset selection is essential for identifying relevant and non-redundant features,
which enhances classification accuracy and simplifies machine learning models. Given the …

Feature selection is of great importance in Machine Learning, where it can be used to
reduce the dimensionality of classification, ranking and prediction problems. The removal of …

With the rapid development of quantum computers, researchers have shown quantum
advantages in physics-oriented problems. Quantum algorithms tackling computational …

The problem of selecting an appropriate number of features in supervised learning problems
is investigated. Starting with common methods in machine learning, the feature selection …

Effective access points (APs) selection is a crucial step in localization systems. It directly
affects both localization accuracy and computational efficiency. Classical APs selection …

Feature selection is critical in machine learning to reduce dimensionality and improve model
accuracy and efficiency. The exponential growth in feature space dimensionality for modern …

Quantum computing sets the foundation for new ways of designing algorithms, thanks to the
peculiar properties inherited by quantum mechanics. The exploration of this new paradigm …

We present the models implemented by the NICA group for the Quantum Computing
(QuantumCLEF) Shared Task at CLEF 2024. Our participation focused on Task 1A: Feature …

The intricate nature of lung cancer treatment poses considerable challenges upon
diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality …