International Peer-Reviewed Academic Journal • Open Access (CC BY 4.0)

International Journal of Computational Science and Digital Innovation

A high-standard international venue for original theoretical and applied research in Computer Science, Artificial Intelligence, Cybersecurity, Machine Learning, and Next-Generation Computing Technologies.

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Review Model

Double-Blind Peer Review

Publication Model

Open Access (CC BY 4.0)

Frequency

Quarterly (4 Issues/Yr)

Initial Decision

~5 Days Desk Screening

Verified Publication Metrics

Real-time indicators compiled strictly from published database records

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Articles Published

Volumes & Issues

Contributing Authors

Countries Represented

Active Peer Reviewers

Aims & Scope

Publishing rigorous computational investigations that bridge theoretical computer science with applied intelligence.

Artificial Intelligence & ML

Deep learning, reinforcement learning, explainable AI, neural-symbolic systems.

Cybersecurity & Cryptography

Zero-trust models, post-quantum crypto, automated vulnerability audits.

Data Science & Big Data

Distributed mining, graph algorithms, stream analytics, predictive models.

Cloud & Edge Intelligence

Serverless architectures, IoT edge inference, low-power decentralized compute.

Computer Vision & NLP

Multimodal foundation models, medical imaging, multilingual language representation.

Software Systems Engineering

Formal verification, high-performance distributed systems, fault-tolerant consensus.

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Latest Published Articles

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ORIGINAL RESEARCH•Vol. 1, Issue 1•Jan 15, 2027

Fault-Tolerant Consensus for Decentralized Edge Intelligence using Zero-Knowledge Proofs

Prof. Kenji Sato [DEMO]

Modern distributed edge applications demand trustless coordination among heterogeneous compute nodes with minimal communication overhead. We introduce an asymmetric zero-knowledge consensus protocol optimized for constrained devices. Our empirical evaluation shows consensus latency below 18ms on ARM-based cluster nodes.

DOI: 10.5555/IJCSDI.2027.0002Read Article

ORIGINAL RESEARCH•Vol. 1, Issue 1•Jan 15, 2027

Privacy-Preserving Federated Learning via Adaptive Gradient Perturbation in Edge Computing Networks

Dr. Evelyn Author [DEMO], Dr. Liam Zhang [DEMO]

Federated learning enables decentralized model training across distributed clients without exposing raw local data. However, vulnerability to gradient inversion attacks remains a primary bottleneck in edge deployments. In this paper, we propose an adaptive gradient perturbation mechanism with dynamic differential privacy guarantees. Experiments demonstrate a 41% reduction in privacy leakage with less than 1.2% degradation in model convergence across standard vision benchmarks.

DOI: 10.5555/IJCSDI.2027.0001Read Article

CURRENT ISSUE2027

Volume 1, Issue 1

Welcome to the inaugural issue of IJCSDI. This volume presents groundbreaking advances in privacy-preserving AI, quantum-resistant cryptography, and edge computer vision.

Explore Current Issue Table of Contents