Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years
have had a transformative effect on biomedical research, enabling the profiling and analysis …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …

Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …

T follicular helper (TFH) cells are a distinct type of CD4+ T cells that are essential for most
antibody and B lymphocyte responses. TFH cell regulation and dysregulation is involved in …

In this paper, we discuss how the clustering analysis technique can be applied to analyze
functional magnetic resonance imaging (fMRI) time-series data in the context of …

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …

The dynamic nature of chemical processes and manufacturing environments, along with
numerous machines, their unique activity states, and mutual interactions, render challenges …