About the Journal

Applied AI & Data Science for Health, Agriculture & Environment (AIDSAE) is an open access, peer-reviewed journal published by EcoScribe Publishers Company Limited. AIDSAE exists to serve researchers and practitioners working at the intersection of AI, machine learning, statistics, and modern data science with real-world applications in health, agriculture, environment, climate, and One Health systems.

 

 

AIDSAE provides a platform for work that is both innovative and credible. We welcome manuscripts that demonstrate strong methodological discipline—clear dataset provenance, robust validation strategies, meaningful baseline comparisons, appropriate metrics, and honest discussion of limitations. We are especially interested in research that can inform decision-making, strengthen services, and improve outcomes in diverse settings, including low-resource contexts where data challenges are common and careful evaluation is essential.

Our Mission

To publish high-quality applied AI and data science research that strengthens knowledge and practice across health, agriculture, and environmental domains.

Our Vision

To become a trusted multidisciplinary outlet known for methodological rigor, transparent reporting, and practical relevance.

What We Publish

AIDSAE considers the following article types:

  • Original Research Articles (full applied studies with robust validation)
  • Short Reports / Technical Notes (compact, well-evaluated applied results)
  • Systematic Reviews / Scoping Reviews (explicit methods; PRISMA encouraged where applicable)
  • Data Papers (datasets with clear documentation and access conditions)
  • Methods / Pipeline Papers (methods evaluated on real data with baselines)
  • Applied Case Studies (implementation evidence, lessons learned, and limitations)

Areas of Interest

Topics include (but are not limited to):

  • health prediction models, decision support, and clinical/public health analytics
  • surveillance and outbreak detection, risk mapping, and health systems data science
  • agricultural analytics: yield forecasting, pest/disease detection, remote sensing, precision agriculture
  • environmental and climate analytics: hazards, land-use change, biodiversity, air/water quality modeling
  • explainable AI, fairness, bias, and responsible AI in applied settings
  • reproducible pipelines, deployment challenges, monitoring, and model maintenance

Peer Review and Standards

AIDSAE uses a double-blind peer review system and typically invites at least two independent reviewers for research articles. Submissions undergo initial editorial screening for scope fit, completeness, reporting quality, and integrity checks before external review. To support credibility and reduce avoidable errors, AIDSAE expects clear reporting of: dataset sources, preprocessing steps, validation design, baseline comparisons, evaluation metrics, leakage safeguards, and error analysis/failure modes.

Open Access and APC

AIDSAE is fully open access: all published articles are freely available online upon publication. To support editorial operations, hosting, and production, AIDSAE charges an Article Processing Charge (APC) of USD 50 for accepted manuscripts only (no submission fee). Payment methods include bank transfer, mobile money, and PayPal. For security, payment details are provided only after acceptance in the official payment request email.

Publisher

AIDSAE is published by EcoScribe Publishers Company Limited, supporting scholarly publishing with a focus on strong editorial processes, research integrity, and accessibility.

Contact

For editorial enquiries, submissions, or policy clarifications, contact: editorialoffice@ecoscribepublishers.com

Ready to submit?

If your work applies AI and data science to solve real problems in health, agriculture, or environmental systems—and is backed by solid validation and transparent reporting—we invite you to submit to AIDSAE.