This paper provides an extensive review of learning-based short-term forecasting models for
smart grid applications. In addition to this, the paper also explores forecasting models …

Systematic reviews (SRs) constitute a critical foundation for evidence-based decision-
making and policy formulation across various disciplines, particularly in healthcare and …

Background: In the current global market, supply chains are increasingly complex,
necessitating agile and sustainable management strategies. Traditional analytical methods …

There is a recognized need to forecast lithium-ion batteries' state of health (SOH) to
guarantee their safety and reliability. However, the selected health indicators highly …

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new
challenges to the research community. Artificial intelligence (AI) driven methods can be …

Penalized regression methods, such as L 1 regularization, are routinely used in high-
dimensional applications, and there is a rich literature on optimality properties under sparsity …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

The dense network of interconnected cellular signalling responses that are quantifiable in
peripheral immune cells provides a wealth of actionable immunological insights. Although …

We propose the Bayesian bridge estimator for regularized regression and classification. Two
key mixture representations for the Bayesian bridge model are developed: a scale mixture of …

The rapid development of digitalization has brought disruptive changes to the economy and
life. The effect of digitalization on energy efficiency is explored using a time series dataset …