About the Journal

Sage Science Review of Applied Machine Learning

Advancing Intelligent Systems Through Peer-Reviewed Scholarship

About the Journal

The Sage Science Review of Applied Machine Learning is a premier peer-reviewed journal publishing original research articles, comprehensive literature reviews, and empirical case studies in machine learning applications. Established as a nexus between academic research and industrial practice, the journal maintains an impact factor of 4.78 according to the 2023 Journal Citation Reports.

Scope and Research Topics

Core Methodological Focus

  • Deep Neural Architecture Design
  • Explainable AI Systems (XAI)
  • Multimodal Learning Systems
  • Reinforcement Learning Frameworks
  • Bayesian Machine Learning
  • Quantum Machine Learning

Applied Domains

Healthcare: Predictive diagnostics, Medical imaging analysis
Finance: Algorithmic trading, Risk modeling
Industry 4.0: Predictive maintenance, Quality control
Social Science: Computational sociology, Network analysis

Submission Guidelines

✓ Manuscript Requirements: Submissions must be original, unpublished works (8-12k words) following APA 7th edition formatting. Include structured abstracts (250 words) with explicit methodology and impact statements.

✓ Review Process: All submissions undergo rigorous double-blind review by at least three domain experts, with an average response time of 42 days. High-quality replication studies and negative results are particularly encouraged.

Indexing and Impact

Indexed in:
• Web of Science (SCIE)
• Scopus 
• PubMed Central
• DBLP Computer Science

Metrics:
• 2023 Impact Factor: 4.78
• 5-Year IF: 5.12
• Avg. Decision Time: 42 days
• Acceptance Rate: 18%