What to Look for When Choosing a Journal
Before submitting your research, evaluate journals on these key criteria:
- Data science scope — big data, analytics, machine learning pipelines, data systems
- Engineering relevance — data engineering, database systems, distributed computing
- CrossRef DOI and Google Scholar indexing for academic citation visibility
- Open access for broad practitioner and academic readership
- Fast peer review — 2–4 weeks for a field where timeliness matters
- Transparent, affordable APC with clear publication fee policies
Recommended Journals — Cluster 2: Engineering, Technology & Applied Sciences
All journals below are published by Keith Publications — open access, peer-reviewed, and indexed for global discoverability.
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Applied Sciences, Engineering, and Technology Journal
(ASETJ)
Applied sciences, engineering disciplines, technology innovation, and interdisciplinary research bridging theory with real-world application.
Applied Sciences Engineering Research Technology Innovation Interdisciplinary Studies -
Columbia Journal of Engineering and Technology
(CJET)
Electrical, mechanical, civil, computer, software, environmental, and industrial engineering with a focus on emerging technologies.
Electrical Engineering Mechanical Engineering Civil Engineering Computer Engineering -
Journal of Medical Technology and Innovation
(JMTI)
Medical technology, biomedical engineering, health informatics, clinical innovation, and translational research at the medicine-technology interface.
Medical Technology Biomedical Engineering Health Informatics Clinical Innovation
Fast Publication: All Keith Publications journals complete initial review within 2–5 business days and full peer review within 2–4 weeks. Articles are published online within 5–10 days of acceptance — making us one of the fastest open access publishers in the engineering and technology field.
Data Science and Engineering at Keith Publications
Keith Publications engineering journals accept data science and data engineering research including: data mining and knowledge discovery, machine learning pipelines and MLOps, big data infrastructure and processing, database design and NoSQL systems, data warehousing and ETL processes, stream processing and real-time analytics, data visualisation, cloud data architecture, data quality and governance, spatial data systems, and data-driven applications in healthcare, smart cities, manufacturing, and finance.
Data-Driven Engineering Research
Data-driven engineering represents a significant evolution in how engineering problems are analysed and solved. Traditional simulation and analytical models are increasingly being complemented — or replaced — by machine learning models trained on sensor data, simulation outputs, or experimental results. Keith Publications welcomes data-driven engineering research that demonstrates the integration of data science methods into engineering practice, including predictive maintenance, structural health monitoring, energy system optimisation, and manufacturing process control.
Tips to Maximise Your Acceptance Chances
- Align your topic with the journal's scope — read the aims and scope carefully before submitting.
- Write a compelling abstract — it's the first thing editors and reviewers read. Make it clear, concise, and keyword-rich.
- Ensure strong methodology — clearly describe your research design, experimental setup, or computational approach.
- Use current and relevant citations — include recent publications (within the last 5 years) alongside seminal engineering works.
- Follow formatting guidelines exactly — non-compliant manuscripts are often desk-rejected without review.
- Present results clearly — use tables, figures, and graphs to communicate quantitative findings effectively.
- State practical implications — explain how your engineering or technology findings can be applied in practice.
Frequently Asked Questions
Related Research Guides
Explore other research journal guides in the Engineering, Technology & Applied Sciences cluster: