CloudAI Journal of Applied AI & Data
CloudAI Journal of Applied AI & Data is an open‑access, peer‑reviewed venue of CloudAI Academy & Research Lab (cloudaiacademy.ca). We publish applied, reproducible work at the intersection of AI, data, cloud, and real‑world impact, with a special focus on Libya and emerging markets.
Aims & Scope
- Applied machine learning, MLOps, and data engineering.
- Cloud, edge, and IoT architectures for real‑world systems.
- Knowledge graphs, information retrieval, and intelligent services.
- AI for marketplaces, public services, finance, health, and local ecosystems.
- Sustainability, carbon analytics, and smart infrastructure.
- AI education, capacity building, and inclusive digital skills.
What We Publish
- Short research articles (4–10 pages).
- Technical reports and system architecture papers.
- Case studies from industry, startups, public sector, and NGOs.
- Datasets, benchmarks, and reproducible notebooks.
- Education & pedagogy pieces on teaching AI, cloud, and data.
Open Access & Ethics
The journal is diamond open access: no fees to read, no fees to publish. Authors retain copyright and grant CloudAI Journal the right to host and index their work. All submissions are screened for originality, research integrity, and responsible use of AI tools.
Peer Review & Process
- Initial check for scope, clarity, and originality.
- Single‑blind or open review by at least one reviewer (aiming for two when possible).
- Constructive feedback with a clear accept / revise / reject decision.
- Accepted articles are published online on a rolling basis and grouped into issues.
How to Submit
We are now inviting submissions for Volume 1. Prepare your manuscript using your preferred LaTeX or Word template and send it as a PDF (with any source files or code links) to:
Email: journal@cloudaiacademy.ca
- Language: English, Arabic, or bilingual.
- Include title, authors, affiliations, abstract, and keywords.
- Clearly state contributions, methods, datasets, and limitations.
- Disclose any use of AI tools in writing or experiments.
- If code/data are available, include links (GitHub, etc.).
Editorial Board
- Editor‑in‑Chief: Fateh Adhnouss, CloudAI Academy & Research Lab.
- Advisory Editors: To be announced – we welcome collaborators from academia and industry.
Issues & Articles
Volume 1 will feature technical reports and applied AI studies originating from CloudAI Academy projects and partner organizations. Early accepted articles will appear here once published.
No published articles yet.