Welcome to the Journal of Advanced Computing Systems (JACS)

The Journal of Advanced Computing Systems (JACS) is an international, peer-reviewed open-access journal dedicated to publishing high-quality research in advanced computing technologies and their applications across diverse industries. We are proud to announce that JACS is now indexed in the Directory of Open Access Journals (DOAJ), further validating our commitment to open access, research integrity, and global scholarly visibility.

Our Mission

At JACS, our mission is to bridge the gap between theoretical advancements and practical implementations in advanced computing. We provide a trusted platform for researchers, practitioners, and industry experts to disseminate innovative ideas, groundbreaking discoveries, and impactful applications. By fostering open access and maintaining rigorous editorial standards, we aim to contribute to the global advancement of computing technologies and their transformative role in addressing complex societal and industrial challenges.

Why Publish with JACS?

  • Indexed in DOAJ ensuring enhanced visibility and credibility.
  • Fully open access with unrestricted global reach.
  • Rigorous peer review process ensuring quality and integrity.
  • Fast publication with continuous online availability.

Important Journal Details

Title: Journal of Advanced Computing Systems
Short Name: JACS
ISSN: 3066-3962
ESTD: 2021
Subject Category: Computer Science, Advanced Computing Technologies
Journal Type: International Peer-Reviewed Journal, Open Access
Issue Frequency: Bimonthly
Publication Guidelines: Adheres to COPE (Committee on Publication Ethics) Guidelines
Copyright: Creative Commons
Publication Format: Online
Language: English
Website URL: https://scipublication.com/
Email ID: contact@scipublication.com
Chief Editor Name: Dr. Padmashree T
Full Address: Associate Professor, Computer Science and Engg

Current Issue

Vol. 5 No. 10 (2025): Advanced Computing Systems
Published: 2025-10-01

Articles

  • Machine Learning-Based Network Performance Monitoring and Prediction for Distributed AI Training Workloads

    Juan Li,  Wenkun Ren (Author)
    1-17
    DOI: https://doi.org/10.69987/
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