AFTER

Call For Papers

The ICSLDSAI emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Statistical Learning In Data Science And Ai are explored to promote cross-domain knowledge exchange and collaborative innovation.

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

Statistical learning algorithms in AI
Supervised vs unsupervised learning methods
Deep learning applications in statistics
Statistical learning for predictive modeling
Feature selection in statistical learning
Statistical learning in natural language processing
Reinforcement learning and statistics
Statistical learning in computer vision
Model evaluation in statistical learning
Statistical learning for big data
Ethics in AI and statistical learning
Transfer learning in statistical applications
Statistical learning for time series forecasting
Hybrid models in statistical learning
Statistical learning in healthcare analytics
Statistical learning for anomaly detection
Applications of statistical learning in finance
Statistical learning frameworks and tools
Challenges in statistical learning research
Future trends in statistical learning

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.