Research Topic · Peer-Reviewed

Machine Learning

Machine learning is a branch of artificial intelligence in which computational systems learn patterns from data and improve their performance on a task without being explicitly programmed with rules for it. Algorithms infer relationships from examples and use them to make predictions or decisions on new inputs. The …

Curated from this journal's research 📚 9 peer-reviewed articles cited Cited 50× across the literature 🔖 ISSN 2470-5020 🗓 Reviewed July 2026

Overview

Machine learning is a branch of artificial intelligence in which computational systems learn patterns from data and improve their performance on a task without being explicitly programmed with rules for it. Algorithms infer relationships from examples and use them to make predictions or decisions on new inputs. The field is commonly divided into supervised learning, which maps inputs to labelled outputs for classification or regression; unsupervised learning, which discovers structure such as clusters in unlabelled data; and reinforcement learning, which optimises actions through feedback. Methods range from decision-tree ensembles and statistical time-series models to deep neural networks and transfer learning, the last reusing knowledge from one task to accelerate another. In biomedical and neurological research, machine learning supports analysis of complex, high-dimensional data, for example characterising functional brain connectivity, grading tumours, and predicting clinical risk for conditions such as diabetes. Beyond health, the same techniques address problems in agriculture, including automated detection of weeds and crop-leaf diseases from images, and forecasting tasks such as epidemic prediction. Cross-cutting concerns include model validation, data quality, interpretability, fairness, and the ethical implications of automated decision-making. By extracting predictive structure from data, machine learning has become a general-purpose tool across scientific and applied domains.

Research published in this journal

9 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 9 articles above have been cited 50 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Machine Learning, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Neurological Research and Therapy (ISSN 2470-5020).

Journal editorial board
Ian J Martins · Australia Giuseppe Lanza · Italy Ion Codreanu · United States

This page summarises published research for orientation; it is not medical or professional advice.