Hybrid Conferencee

International Conference on Computational Methods in Statistical Learning (ICCMSL - 27)

15th - 16th June 2027 | Kobe, Japan
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Expand the Academic Reach of Your Research - a Q1-ranked and Scopus-indexed journal publication opportunity

Journal consideration and publication are subject to editorial review, peer review and applicable journal policies.

Conference Notifications:

"Be sure to check this section regularly for all Research Plus International Conference updates. We’ll keep you informed about deadlines, event details, and more important notifications."

Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Kobe. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Kobe event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Kobe conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Kobe, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Kobe."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICCMSL aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Statistics, Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

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

01
Computational methods in statistical learning
02
Algorithms for high-dimensional data
03
Statistical learning theory applications
04
Model selection in statistical learning
05
Statistical learning for time series analysis
06
Deep learning and statistical methods
07
Regularization techniques in statistical learning
08
Statistical learning in genomics
09
Bayesian approaches to statistical learning
10
Statistical learning for image analysis
11
Ensemble methods in statistical learning
12
Statistical learning for text classification
13
Robustness in statistical learning models
14
Statistical learning for network data
15
Applications of statistical learning in finance
16
Statistical learning in social sciences
17
Interpretable models in statistical learning
18
Statistical learning for causal inference
19
Computational challenges in statistical learning
20
Future directions in statistical learning