Journal of Electronic & Information Systems publishes original research papers that offer professional review and publication to freely disseminate research findings in areas of IT applications, Artificial Intelligence (AI), Circuits and Systems, information and control, Data Management and Processing, and Human–Computer Interaction, etc. The Journal focuses on innovations of research methods at all stages and is committed to providing theoretical and practical experience for all those who are involved in these fields.

Journal of Electronic & Information Systems aims to discover innovative methods, theories, and studies in its field by publishing original articles, case studies, and comprehensive reviews.

The scope of the papers in this journal includes, but is not limited to:

  • Mobile applications for development
  • Diverse Communications options, including optical, acoustic, and radio
  • AI, Machine learning, and Human–Computer Interaction
  • Systems Engineering (applied to data, information, and intelligence challenges)
  • Analogue Integrated Circuits, Digital Integrated Circuits, Digital System Design
  • Distributed Circuits and Systems, Distributed Sensor Networks and Systems
  • VLSI Systems and Applications
  • Circuits and Systems for Communication and Computing
  • Complex Network Systems
  • Nondestructive Testing
  • Control System Analysis and Design
  • Information System Theory and Applications
  • Data Management and Interoperability, Data Processing
  • Spectral Analysis, Filtering, Pattern Recognition
  • Analogue and Digital Signal Processing
  • Image Processing, Optical and Ultrasonic Imaging

Vol. 8 , Iss. 2 (October 2026)

  • ARTICLE

    Enhancing Software Defect Prediction Security via Federated BLSTM with Differential Privacy

    Aunik Hasan Mridul, Raihan Jamil, Md Injamul Haque, Niazi Mahrab, Sartaz Islam, Mohammad Abdullah Al Nayeem Khan, Nowreen Ahsan, Md Abdur Rakib
    1-13

    Article ID: 13303    DOI:https://doi.org/10.30564/jeis.v8i2.13303
    69  (Abstract) 23  (Download)

    Abstract: Software Defect Prediction (SDP) is an issue of paramount importance for improvement of software quality but data isolation and privacy concerns across organizations affects efficacy of SDP. By making use of a Federated Learning (FL) framework in combination with Differential Privacy (DP) for collective learning without exchanging any raw data, we provide a unique solution... More

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