Focus and Scope

Applied AI & Data Science for Health, Agriculture & Environment (AIDSAE) is a peer-reviewed, open access journal published by EcoScribe Publishers Company Limited. AIDSAE publishes high-quality research that applies artificial intelligence (AI), machine learning, statistics, and modern data science to practical challenges in health systems, agriculture and food systems, environmental management, and climate resilience.

AIDSAE prioritizes manuscripts that move beyond “trying an algorithm” to deliver credible evidence. Submissions should present clear data provenance, appropriate validation, meaningful baselines, well-chosen metrics, and transparent discussion of limitations and real-world applicability.

Core Focus Areas

AIDSAE welcomes work in (but not limited to) the following areas:

1) AI & Data Science for Health and One Health

  • predictive modelling for disease risk, treatment outcomes, or service delivery
  • epidemiological modelling, surveillance analytics, outbreak detection, and risk mapping
  • decision support systems and digital health analytics
  • health systems performance analytics and resource optimization
  • responsible AI in health: bias/fairness, explainability, generalisability, privacy-preserving methods

2) AI & Data Science for Agriculture and Food Systems

  • crop yield prediction, farm advisory analytics, and precision agriculture
  • pest/disease detection and early warning systems
  • remote sensing and geospatial analytics for farming systems
  • postharvest, supply chain, and market analytics
  • climate-smart agriculture modelling and risk analytics

3) AI & Data Science for Environment and Climate

  • land-use/land-cover change modelling and ecosystem monitoring
  • environmental quality analytics (air, water, soil)
  • climate risk modelling, hazard prediction, early warning systems
  • biodiversity and conservation analytics
  • geospatial and Earth observation analytics for environmental decision-making

Methodological and Data Themes (cross-cutting)

AIDSAE also encourages papers on:

  • reproducible pipelines, MLOps, model monitoring, and deployment challenges
  • causal inference and robust observational analysis in applied settings
  • uncertainty quantification and calibration
  • explainable AI and interpretability for decision-making
  • synthetic data, privacy-preserving analytics, federated learning (where relevant)
  • evaluation frameworks, benchmarking, and error analysis for real-world AI

Article Types Considered

  • Original Research Articles
  • Short Reports / Technical Notes
  • Data Papers
  • Methods / Pipeline Papers (must include real-data evaluation)
  • Systematic Reviews / Scoping Reviews
  • Applied Case Studies with implementation evidence

What is Usually Not Considered

To protect the quality of the journal, AIDSAE generally does not consider:

  • purely theoretical or conceptual AI papers without applied validation
  • manuscripts lacking a clear dataset, validation strategy, baseline comparator, and evaluation metrics
  • papers with unverifiable claims, invented references, or weak reporting
  • submissions that do not meet AIDSAE’s minimum standards for reproducibility and integrity

AIDSAE aims to publish research that is not only innovative, but also auditable, reproducible in principle, and useful for practice across health, agriculture, and environmental domains.