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Journal Photo for Technologies
Peer reviewed only Open Access

Technologies (Technologies)

Publisher : Multidisciplinary Digital Publishing Institute (MDPI)
Computer Science
e-ISSN 2227-7080
Issue Frequency Monthly
Impact Factor 4.2
Est. Year 2013
Mobile 41616837734
Country Switzerland
Language English, Computer Science (miscellaneous)
APC YES
Impact Factor Assignee Google Scholar
Email technologies@mdpi.com

Journal Descriptions

Technologies (ISSN 2227-7080) is an open access, peer-reviewed journal that provides an advanced forum for latest in technological studies to both researchers and professionals in an array of related disciplines. It publishes research papers, reviews, and communications as well as Special Issues on targeted subjects of current and future relevance. The aim of Technologies is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, the journal has no restriction on the maximum length of the papers. Full experimental details should be provided so that the results can be reproduced. Electronic files and software regarding the full details of calculations or experimental procedures, if unable to be published in a normal way, can be deposited as supplementary electronic material. Scope: • Internet of things • Neural networks • Machine learning • Advances in Materials science • Assistive Technologies (Adaptive device, healthcare; robotics, wearable, etc.) • Advances in electronics • Environmental technologies (environmental monitoring, model and conserve, etc.) • Medical technologies (diagnosing, monitoring and treating, etc.)

Technologies (Technologies) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Computer Science , Online , Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2227-7080, Established: 2013, Impact Factor: 4.2
  • Does Not Provide Crossref DOI
  • Indexed in: Scopus, DOAJ

  • Not indexed in WoS, PubMed, UGC CARE

Indexing

Publications of Technologies

Ketan Kotecha January, 2022
IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendatio...