Recurrent neural networks (RNNs) are capable of learning features and long term
dependencies from sequential and time-series data. The RNNs have a stack of non-linear …

Differential evolution (DE) is one of the highly acknowledged population-based optimization
algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems …

Exploration, the process of visiting a new region in a search space, and exploitation, the
process of searching in the neighborhood of previously visited regions, are two centerpieces …

Random convolution kernel transform (Rocket) is a fast, efficient, and novel approach for
time series feature extraction using a large number of independent randomly initialized 1-D …

In the last three decades, the field of computational intelligence has seen a profusion of
population‐based metaheuristics applied to a variety of problems, where they achieved …

Objective: Locomotion mode recognition (LMR) enables seamless and natural transitions
between low-level control systems in a powered prosthesis. We present a new optimization …

This paper proposes a new differential evolution (DE) algorithm for unconstrained
continuous optimisation problems, termed μ μ JADE, that uses a small or 'micro'(μ μ) …

Differential evolution (DE) algorithm suffers from high computational time due to slow nature
of evaluation. Micro-DE (MDE) algorithms utilize a very small population size, which can …

Identification of differentially expressed genes, lying beneath the carcinogenic expression, is
still very crucial for accurate detection and treatment of the disease. The challenge of a large …

One of the major challenges in analyzing modern biological data is dealing with ill-posed
problems and missing data. In this paper, we propose a genotype imputation and phenotype …