Advances in Artificial Intelligence and Machine Learning (ISSN: 2582-9793; Scopus Source ID: 21101164612) is a global, peer-reviewed, open-access Artificial Intelligence journal indexed in Web of Science Core Collection (Journal Impact Factor 2025: 0.8) and Scopus (CiteScore 2025: 1.5, Q3 in Artificial Intelligence, CiteScore Percentile: 33rd, H-Index: 9). The journal is committed to the timely publication of original work in Artificial Intelligence and Machine Learning. In addition to original research, the journal encourages review papers, including mini reviews focused on a very specific topic. As a trusted journal of artificial intelligence, its mission is to provide a context for the study of Artificial Intelligence and Human Wellbeing, Human Cognition, Artificial Systems, and Autonomous Machines, among other areas. From 2026 onwards, the journal publishes 6 issues per year (February, April, June, August, October, December).
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Welcome to the Advances in Artificial Intelligence and Machine Learning (AAIML) journal. Launched in February 2021, the journal has seen a steady increase in the number of high-quality submissions. Thanks are due to the authors, editors, and anonymous reviewers for their contributions to the successful publication of interesting research articles.
AAIML aims to publish original contributions in artificial intelligence and machine learning. Authors are invited to contribute full-length research papers, systematic review papers, and proposals for special issues. As an established journal of artificial intelligence research, AAIML is published six times a year and remains committed to a timely peer review process of six to ten weeks from initial paper submission.
Sincerely,
Anca Ralescu
Anca.Ralescu@uc.edu
Senior Member, IEEE
EECS Department, University of Cincinnati, ML 0030
Cincinnati, OH 45221-0030, USA
Editor-in-Chief
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.png "NeuroInsight-PD: Early Detection and Classification of Parkinson’s Disease")
.png "An Intelligent Speech Analytics Framework for Depression Detection: Comparative Assessment of Machine Learning Architectures")
.png "Comparative Performance Evaluation of a Proposed AI-Based Text Mining Framework for Human Sentiment Analysis Using Reliability and Accuracy Metrics")
.png "OCT Medical Image Enhancement for Improving Diagnosis Accuracy: A Wavelet-Tikhonov and Deep Learning Networks")
