Hybrid Conferencee

International Conference on Control Systems Engineering and Data Mining (ICCSEDM - 26)

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

Call for Paper

The ICCSEDM 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 in control systems engineering
02
Machine learning for control system optimization
03
Predictive analytics for system performance
04
Data-driven approaches to system stability
05
Big data challenges in control engineering
06
Data visualization for control system analysis
07
Real-time monitoring in control systems
08
Data mining for fault detection in systems
09
Integration of AI in control engineering
10
Collaborative control systems through data sharing
11
Case studies in control systems analytics
12
Data-driven methodologies in control engineering
13
Ethics in control systems data usage
14
Future trends in control systems engineering
15
Data mining for process control applications
16
Machine learning for adaptive control systems
17
Data-driven innovations in automation
18
Data mining for robotics control systems
19
Applications of data mining in control systems
20
Data-driven decision making in control engineering