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Call for Paper

The ICSTMMLA 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,Data Science 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
Machine learning algorithms for statistical analysis
02
Statistical techniques in AI model evaluation
03
Feature selection methods in machine learning
04
Statistical learning theory applications
05
Data preprocessing for machine learning models
06
Ensemble methods in statistical learning
07
Deep learning and statistical inference
08
Bayesian statistics in AI applications
09
Statistical methods for big data analytics
10
Interpretability of machine learning models
11
Statistical challenges in AI deployment
12
Reinforcement learning and statistical methods
13
Statistical evaluation of AI systems
14
Transfer learning in statistical contexts
15
Statistical methods for time series analysis
16
Unsupervised learning and statistical techniques
17
Statistical issues in data privacy
18
Statistical frameworks for AI ethics
19
Applications of statistics in natural language processing
20
Statistical modeling of complex systems

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.