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Call for Paper

The ICSLMLI 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 Statistics 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:

01
Integration of statistical learning and machine learning
02
Applications of machine learning in statistics
03
Statistical methods for predictive modeling
04
Bayesian statistics and machine learning synergy
05
Statistical learning techniques for big data
06
Feature selection methods in statistical learning
07
Statistical validation of machine learning models
08
Deep learning applications in statistical analysis
09
Statistical approaches to model interpretability
10
Ensemble methods in statistical learning
11
Statistical methods for time series forecasting
12
Applications of neural networks in statistics
13
Statistical learning in bioinformatics
14
Causal inference in machine learning contexts
15
Statistical frameworks for unsupervised learning
16
Statistical software for machine learning applications
17
Challenges in integrating statistics and machine learning
18
Statistical methods for anomaly detection
19
Ethics in statistical machine learning applications
20
Future directions in statistical learning research

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.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

👉 Register Now

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.

👉 Submit Your Research Work

Event Highlights

  • Keynote sessions by experienced speakers
  • Technical paper presentations across multiple topics
  • Opportunities for academic and professional networking
  • Discussions on current research developments

Contact Us

    Have questions about submission, registration, or publication? We’re happy to help.

  • Email: [email protected]
  • Phone:+91 9940952512

We look forward to your participation and contribution to the ASPHER Conference.