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Journal Photo for Machine Learning: Science and Technology
Peer reviewed only Open Access

Machine Learning: Science and Technology (MLST)

Publisher : IOP Publishing
Science Technology Physics
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e-ISSN 2632-2153
Issue Frequency Quarterly
Impact Factor 6.3
Est. Year 2020
Mobile 4401179297481
DOI YES
Country Afghanistan
Language English
APC YES
Impact Factor Assignee Google Scholar
Email mlst@ioppublishing.org

Journal Descriptions

Machine Learning: Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Machine Learning: Science and Technology™ is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: i) advance the state of machine learning-driven applications in the sciences, or ii) make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.

Machine Learning: Science and Technology (MLST) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Science, Technology, Physics, Biology, Computer Science, Artificial Intelligence , Online , Quarterly Journal

  • UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2632-2153, Established: 2020, Impact Factor: 6.3
  • Provides Crossref DOI

  • Indexed in: Scopus, WoS, DOAJ, PubMed

  • Not indexed in UGC CARE

Indexing

Publications of MLST

ABCMB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps
Pietro Liò December, 2024
The existence of a cosmic background of primordial gravitational waves (PGWB) is a robust prediction of inflationary cosmology, but it has so far evaded discovery. The most promising avenue ...