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Aims and Scope

International Journal of Machine Learning is an open access journal that focuses on publishing original and peer-reviewed research papers on all aspects of machine learning. The subjects covered by the journal include machine learning theory, algorithms, approaches, models, and applications. The topics include but are not limited to:

Natural language processing (NLP)
Artificial intelligence
Deep learning
Data mining
Computer vision
Intelligent systems
Neural networks
AI-based software engineering,
Bioinformatics and its applications in engineering, medicine, biology, education, business and social sciences

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net