The principle of any approach for solving feature selection problem is to find a subset of the
original features. Since finding a minimal subset of the features is an NP-hard problem, it is …

A search method that finds a minimal subset of features (over a feature space) that yields
maximum classification accuracy is proposed. This method employs rough set theory (RST) …

Purpose The purpose of this paper is to present a novel tactile sensor and a visual-tactile
recognition framework to reduce the uncertainty of the visual recognition of transparent …

Mobile roadside units have crucial role in ensuring efficient communication, computing, and
caching services in internet of vehicles (IoVs) for vehicles traversing urban landscapes. The …

In this chapter, we suggest a hybrid binary algorithm, namely, binary particle swarm
optimization (PSO) with flower pollination algorithm (FPA), and call it by BPSOFPA. In …

Metaheuristic search methods have proven to be essential tools for tackling complex
optimization challenges, but their full potential is often constrained by conventional …

Batch Normalization (BN) is an important preprocessing step to many deep learning
applications. Since it is a data-dependent process, for some homogeneous datasets it is a …

In this chapter, a new hybrid binary version of the bat algorithm (BA) is suggested to solve
feature selection problems. In particular, BA is integrated with an enhanced version of the …

Traditional K-means clustering assumes, to some extent, a uniform distribution of data
around predefined centroids, which limits its effectiveness for many realistic datasets. In this …

The rough set model has two symmetry approximations called upper approximation and
lower approximation, which correspond to a concept's intension and extension, respectively …