On-location / Digital Conference

International Conference on Statistical Learning and Artificial Intelligence (ICSL-AI-27)

11th - 12th Jan 2027,Johannesburg, South Africa

In Association With:

Call for Paper


Important Dates


Early Bird Registration

12th Dec 2026

Paper Submission Deadline

17th December 2026

Registration Deadline

27th December 2026

Conference Date

11th - 12th Jan 2027

Conference Updates:

"Stay updated with Science Cite Conference news."

  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in Johannesburg ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in Johannesburg conference.
  • Peer Review Process:
    The peer review process will begin soon for Johannesburg conference.
  • Networking with Global Experts:
    Join global experts at our conference in Johannesburg.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICSL-AI bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Statistical Learning and Artificial Intelligence, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Statistical learning in artificial intelligence
  • Applications of AI in statistical modeling
  • Machine learning techniques for data analysis
  • Statistical methods for predictive modeling
  • Deep learning and statistical inference
  • Statistical challenges in AI research
  • Causal inference in statistical learning
  • Data-driven approaches to AI development
  • Statistical evaluation of machine learning models
  • Feature engineering in statistical learning
  • Bayesian methods in AI applications
  • Statistical frameworks for AI ethics
  • Statistical techniques for big data analysis
  • Unsupervised learning and statistical methods
  • Statistical power analysis in AI studies
  • Reinforcement learning and statistical approaches
  • Statistical tools for AI interpretability
  • Data privacy issues in statistical learning
  • Statistical methods for time series forecasting
  • Statistical education for AI practitioners

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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