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International Conference on Engineering Design and Simulation using Data Mining · Registering as Listener

ICEDSDM
📅 26 – 27 Dec 2026 📍 Washington DC, USA 👥 Standard / Physical Participation
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$185
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Registration summary

ConferenceInternational Conference on Engineering Design and Simulation using Data Mining
ModeStandard / Physical
ParticipationListener
Registration fee$185.00
Bank charges (5.8%)$10.73
Total payable $195.73

Includes all bank processing charges — the amount above is exactly what will be charged.

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Benefits of Registering as Listener

👥Access to Conference Sessions
🔗Networking Opportunities
🎖Certificate of Participation
Invitation Letter Support
📚Conference Kit / Materials
🎤Access to Keynote Sessions
• Conference Session Tracks •
SDG

SDG-Aligned Research Themes

Conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 9 SDG 12 SDG 17

This track focuses on the latest methodologies in predictive modeling that enhance engineering design processes. Participants will explore case studies demonstrating the integration of predictive analytics in design workflows.

This session will delve into innovative data mining techniques that facilitate the integration of Computer-Aided Design and Computer-Aided Engineering. Discussions will highlight the impact of these techniques on design efficiency and accuracy.

This track aims to showcase data-driven strategies for optimizing engineering processes. Presentations will cover various optimization algorithms and their applications in real-world engineering scenarios.

This session will explore the role of design analytics in extracting actionable insights from engineering data. Attendees will learn about tools and methodologies that enhance decision-making in design.

This track focuses on the use of virtual prototyping and simulation in the engineering design process. Participants will discuss advancements in simulation technologies that improve product development cycles.

This session will investigate the applications of machine learning in various aspects of engineering design. Case studies will illustrate how machine learning algorithms can enhance design accuracy and efficiency.

This track will cover methodologies for performance analysis in engineering systems using data mining techniques. Participants will discuss metrics and tools for evaluating system performance in design contexts.

This session will highlight innovative approaches to design simulation that leverage data mining for improved outcomes. Attendees will explore new simulation frameworks and their implications for engineering design.

This track will examine the implications of big data on engineering design and simulation practices. Discussions will focus on the challenges and opportunities presented by large datasets in the design process.

This session will explore the importance of collaboration and data sharing in engineering design. Participants will discuss frameworks that facilitate effective data exchange among design teams.

This track will address emerging trends in engineering design and data mining that are shaping the future of the field. Experts will share insights on the potential impact of these trends on engineering practices.

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