Hybrid Conferencee

International Conference on Optimization Algorithms in Data Science (ICOADS - 26)

13th - 14th July 2026 | Kathmandu, Nepal
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Conference Brochure
Sample Full Paper
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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Kathmandu. 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 Kathmandu 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 Kathmandu conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Kathmandu, 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 Kathmandu."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICOADS 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 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
Optimization algorithms for data science applications
02
Applications of optimization in engineering problems
03
Challenges in large-scale optimization
04
Machine learning and optimization techniques
05
Real-time optimization for engineering systems
06
Case studies of optimization in practice
07
Data-driven optimization strategies
08
Ethical considerations in optimization algorithms
09
Future trends in optimization for data science
10
User experience design for optimization tools
11
Collaborative optimization approaches
12
Integrating optimization with machine learning
13
Scalability issues in optimization algorithms
14
Multi-objective optimization in engineering
15
Visualization techniques for optimization results
16
Impact of optimization on engineering efficiency
17
Frameworks for evaluating optimization performance
18
Dynamic optimization for changing environments
19
Interdisciplinary approaches to optimization
20
Benchmarking optimization algorithms in practice