Hybrid Conferencee

International Conference on Data Mining in Engineering Sciences (ICDMES - 26)

7th - 8th August 2026 | Edinburgh, UK
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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 Edinburgh. 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 Edinburgh 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 Edinburgh conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Edinburgh, 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 Edinburgh."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICDMES 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 Mining, 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
Data mining techniques in engineering applications
02
Predictive analytics for engineering processes
03
Big data challenges in engineering sciences
04
Machine learning for engineering problem solving
05
Data visualization methods in engineering
06
Statistical methods for engineering data analysis
07
Data mining for quality control in engineering
08
Optimization algorithms in engineering design
09
Real-time data processing in engineering
10
Data mining for predictive maintenance
11
Text mining applications in engineering research
12
Data-driven decision making in engineering
13
Data mining for energy efficiency in engineering
14
Integration of IoT and data mining
15
Data mining for risk assessment in engineering
16
Ethical considerations in engineering data mining
17
Case studies of data mining in engineering
18
Collaborative data mining in engineering projects
19
Data mining for sustainability in engineering
20
Future trends in engineering data mining