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Computing&AI Connect

eISSN: 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 42
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, enabling articles to be published promptly once they have completed the editorial and production process, without waiting for the completion of a full issue. 

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, Computer Science Applications, Computer Networks and Communications

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
Computing&AI Connect Indexed in DOAJ: A Key Milestone for Scifiniti
2026-07-01
Acknowledging Our Reviewers’ Contributions (2024–2025)
2026-01-05
Published Articles in Computing&AI Connect Now Indexed in Scilit
2024-08-27

Recent Articles

All Articles
open-access Research Article

Available Online: 15 Sep 2026

IncrConsist: Efficient Incremental Consistency Maintenance for LLM-Maintained Knowledge Bases

Volume 3

Background: LLM-based agents are increasingly deployed not only to answer queries but to maintain persistent, evolving knowledge bases—continuously ingesting sources, revising assertions, and reconciling contradictions. After every edit, the knowledge base must be verified for internal consistency. However, naive full re-verification requires O(n²) pairwise comparisons, exceeding 90 seconds per edit step at 500-page scale and rendering it impractical for continuous maintenance...

https://doi.org/Registering DOI
open-access Perspective

Available Online: 27 Aug 2026

AI-Guided Self-Driving Laboratories for Advanced Materials Discovery

Volume 3

https://doi.org/10.69709/CAIC.2026.133382
open-access Research Article

Available Online: 17 Aug 2026

Machine Learning-Driven Multimodal Forensic Data Fusion for Enhanced Disaster Victim Identification: Integrating DNA, Dental, and Facial Image Data

Volume 3

https://doi.org/10.69709/CAIC.2026.122445
open-access Research Article

Available Online: 10 Aug 2026

Performance Optimization of High-Speed Wireless Communication Systems Using Machine Learning

Volume 3

High speed wireless communication systems support the data-intensive requirements of contemporary 5G networks and emerging 6G technologies. However, maintaining high throughput, low latency, and reliable connectivity in rapidly varying wireless channels remains a significant engineering challenge. This study presents a machine-learning-driven framework that integrates a deep reinforcement learning (DRL) scheduler with a hybrid CNN-LSTM channel predictor to jointly optimize radio..

https://doi.org/10.69709/CAIC.2026.129929
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