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Computing&AI Connect

ISSN: 3104-4719

Moussa Ayyash
Editor-in-Chief

Moussa Ayyash
Editor-in-Chief

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Computing&AI Connect is a peer-reviewed, open-access journal committed to fostering advancements and innovation in computing sciences and technologies, and artificial intelligence (AI). The journal encompasses a wide spectrum of topics including Computing Paradigms, Artificial Intelligence, Interdisciplinary Applications, Human-Computer Interaction, Data Science and Analytics, Emerging Technologies, Cloud Computing and Virtualization, Intelligent and Smart Systems, Educational Initiatives, Industry Trends, Open Challenges, and Future Directions. 

Volumes 3
Articles 34
Volume: 3, 2026
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Open Access Policy:
All articles in the journal Computing&AI Connect are Gold Open Access, immediately available without any embargo period, and licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. This license facilitates the unrestricted dissemination of articles, encouraging widespread sharing and utilization of valuable research contributions.

Publication Frequency:
Computing&AI Connect follows a continuous publication model and is licensed under Creative Commons Attribution 4.0 (CC BY 4.0).

Publication Charges:
There will be no article processing charges (APCs) for submissions made by December 31, 2026, and all articles will be published as Open Access. Furthermore, the journal does not levy any additional fees on authors, including editorial processing charges, language editing fees, colour charges, submission fees, membership fees, or any other supplementary charges.

Subject Categories:
Artificial Intelligence, Computer Science

Article Template:
You are encouraged to use the template to format your article according to our guidelines. However, this is not mandatory, as our layout team will ensure all necessary adjustments are made after acceptance and prior to final publication.

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Target Audience:
This journal is designed for researchers, academics, practitioners, and industry leaders actively involved in the ever-evolving landscape of computing and artificial intelligence.

Ethics Statement:
All articles published by Computing&AI Connect adhere strictly to the ethical guidelines suggested by the Committee on Publication Ethics (COPE).  We execute a stringent peer-review procedure that meticulously assesses the originality, quality, and precision of the research we disseminate. We employ advanced tools to scrutinize the legitimacy of submitted content and maintain a firm zero-tolerance stance toward instances of plagiarism, data manipulation, or inappropriate authorship.
For further details, please review our comprehensive Editorial Policies & Ethical Oversight.

Insights

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45 Days

Time to First Peer Review Decision

87 Days

Time to Final Acceptance

2 Days

Acceptance to First Online

Recent Updates

All Updates
Acknowledging Our Reviewers’ Contributions (2024–2025)
2026-01-05
Published Articles in Computing&AI Connect Now Indexed in Scilit
2024-08-27
Published Articles in Computing&AI Connect Now Indexed in J-Gate
2024-07-19

Recent Articles

All Articles
open-access Research Article

Available Online: 14 Apr 2026

Attention-Based LSTM for Sign Language Recognition Leveraging Spatiotemporal Keypoint

Volume 3

https://doi.org/10.69709/CAIC.2026.130891
open-access Research Article

Published: 12 May 2026

A Comparative Evaluation of Imputation Techniques for Missing Data: A Simulation-Based Analysis

Volume 3

Missing data are a common occurrence in research and, if not appropriately addressed prior to analysis, may compromise the validity of study findings. This article evaluates the effectiveness of various imputation techniques as formal approaches for handling missing covariate data. Root Mean Squared Error (RMSE) was computed for each imputation method under Missing Completely at Random (MCAR) and Missing at..

https://doi.org/10.69709/CAIC.2026.100221
open-access Research Article

Published: 02 Apr 2026

Temporal DNA-Based Key Derivation and Metadata Fingerprinting for Secure On-Device Digital Forensics in IoT Systems

Volume 3

https://doi.org/10.69709/CAIC.2026.165654
open-access Mini-Review

Published: 26 Mar 2026

Fine-Tuning vs. RAG: A Position Paper from the Perspective of LLM-Based Cybersecurity Modeling

Volume 3

Large Language Models (LLMs) have revolutionized natural language processing (NLP) tasks, enabling critical capabilities across domains such as business, finance, and cybersecurity analysis. In particular, LLMs are becoming increasingly important in cybersecurity by supporting threat and vulnerability analysis, intelligent response generation, pattern recognition, and automated threat detection. Although LLMs are pre-trained with a large amount of knowledge, their application to..

https://doi.org/10.69709/CAIC.2026.155073
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