Current Computer Science

Aims and Scope:
Current Computer Science publishes original research articles, letters, reviews/mini-reviews, and guest-edited thematic issues dealing with various topics related to Computer Science.

Current Computer Science is not limited to a specific aspect of the field but is instead devoted to a wide range of sub-fields in the field. Articles of an interdisciplinary nature are particularly welcome. Submissions in the following areas are of special interest to the readers of this journal:

      - Artificial Intelligence
      - Communication and Security
      - Computational Science and Numerical Methods
      - Computer Architecture and Organization
      - Computer Engineering
      - Computer Ethics
      - Computer Graphics and Multimedia
      - Computer Networks
      - Computer Vision
      - Cybersecurity and Cryptography
      - Data Management and Data Mining
      - Evolutionary Computing/Quantum Computing
      - Human-Computer Interaction
      - Machine Learning
      - Operating Systems
      - Programming Paradigms and Languages
      - Relational Databases
      - Software Engineering
      - Telecommunications Engineering
      - Theoretical Computer Science

Current Computer Science is an international, peer-reviewed journal on all aspects of computer science published continuously (print & online) by Bentham Science Publishers.
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Editor-in-Chief:

  • Jun Ye Department of Electrical and Information Engineering
    Ningbo University
    Ningbo
    China

ISSN: 2950-3779 (Print)

eISSN: 2950-3787 (Online)

Special Issues With Active Call for Papers

Submission closes on: Oct 17, 2026
Federated and Trustworthy AI: Privacy-Preserving, Secure, and Responsible Learning Systems

Artificial Intelligence is rapidly transforming diverse domains such as healthcare, finance, education, and smart cities. However, the increasing reliance on large-scale data poses major challenges related to privacy, security, and trustworthiness. Federated learning has emerged as a promising paradigm that enables decentralized training across distributed data sources without compromising data privacy. Coupled with advances in cryptography, differential privacy, explainability, and governance frameworks, federated learning has the potential to drive the next generation of trustworthy AI... see more

Submission closes on: Oct 12, 2026
Artificial Intelligence and Machine Learning Applications in Smart Healthcare Systems

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in healthcare systems represents one of the most transformative evelopments in modern medicine. This special issue aims to explore the cutting-edge applications, methodologies, and innovations that are reshaping healthcare delivery through intelligent systems. Healthcare systems worldwide are experiencing unprecedented challenges, including aging populations, rising healthcare costs, and the need for personalized medicine. AI and ML technologies offer promising solutions by enabling predictive analytics, automated... see more