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JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (JETNR)

Publisher :

IJ Publication

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Multidisciplinary
e-ISSN :

2984-9276

Issue Frequency :

Monthly

Impact Factor :

260.1

Est. Year :

2023

Mobile :

7048421102

DOI :

YES

Country :

India

Language :

Multilanguage

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

editor@jetnr.org

Role In Research Journal

Prof Hitesh D. Patel
Darshan Patel

Associate Editor

Prof Hitesh D. Patel
Rajas Paresh Kshirsagar

Editorial Board Member

Prof Hitesh D. Patel
Ramya Ramachandran

Editorial Board Member

Journal Descriptions

UGC CARE recently discontinued the UGC-CARE Journal Listing & Introduction of Suggestive Parameters for choosing peer-reviewed journals that UGC-CARE (India) recommends publishing papers in Transparent Peer Reviewed journals (or Refereed Journals) JETNR is a Transparent Peer-Reviewed Open Access Journal (Refereed Journal), aligning with New UGC and UGC CARE recommendations. As an international, open-access, and online journal, JETNR ensures that published research reaches a broad and diverse audience worldwide. For more details, refer to the official notice: UGC Public Notice. Link: https://www.ugc.gov.in/pdfnews/9678711_PUBLIC-NOTICE-CARE.pdf


JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (JETNR) is :

International, Peer-Reviewed, Open Access, Refereed, Multidisciplinary , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2984-9276, Established in - 2023, Impact Factor - 260.1

Provide Crossref DOI

Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Publications of JETNR

The Art of Hacking

The invention of computers and smartphones is one of the biggest achievements in the world of technology. Smartphones today can do almost everything: faster networking, file management, and...

Explore Transparent Peer Reviews of JETNR

Advanced Machine Learning Techniques for Water Quality Prediction and Management: A Comprehensive Review

The incorporation of IoT, machine learning, and geospatial technologies has rapidized pace in data-driven approaches in water quality monitoring. The approach calls for data-driven methods i...

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