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International Conference on Electrical Engineering and Data Mining Integration

ICEEDMI

17th Jun – 18th Jun 2027 Larnaca, Cyprus

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

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Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

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Terms & Condition

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Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

SDG 4 SDG 7 SDG 9 SDG 11
01 Innovations in Smart Grid Technologies +
This track focuses on the latest advancements in smart grid technologies, emphasizing their integration with data mining techniques. Researchers are encouraged to present novel approaches that enhance grid efficiency and reliability.
SDG 7 SDG 9
02 Predictive Maintenance Strategies in Electrical Systems +
This session explores data-driven predictive maintenance methodologies for electrical engineering applications. Contributions should highlight the role of machine learning in forecasting system failures and optimizing maintenance schedules.
SDG 9
03 Fault Detection and Diagnosis in Power Systems +
This track addresses innovative data mining approaches for fault detection and diagnosis in electrical power systems. Papers should discuss algorithms and techniques that improve the accuracy and speed of fault identification.
SDG 9 SDG 11
04 Energy Analytics and Consumption Forecasting +
This session invites research on energy analytics, focusing on data mining methods for consumption forecasting. Contributions should demonstrate how predictive models can aid in energy management and sustainability efforts.
SDG 7 SDG 13
05 Machine Learning Applications in Electrical Engineering +
This track highlights the application of machine learning techniques in various domains of electrical engineering. Authors are encouraged to share case studies and experimental results that showcase the effectiveness of these methods.
SDG 9
06 Optimization Techniques for Electrical Systems +
This session focuses on optimization techniques applied to electrical engineering challenges, including system performance and resource allocation. Papers should present innovative solutions that leverage data mining for enhanced system optimization.
SDG 9 SDG 12
07 Sensor Data Analysis for Smart Infrastructure +
This track emphasizes the analysis of sensor data in the context of smart infrastructure development. Researchers are invited to present methodologies that utilize data mining to extract actionable insights from sensor networks.
SDG 9 SDG 11
08 Data Mining for Renewable Energy Integration +
This session explores the role of data mining in the integration of renewable energy sources into existing power systems. Contributions should focus on techniques that facilitate the management and optimization of renewable energy utilization.
SDG 7 SDG 13
09 Real-time Monitoring and Control of Electrical Systems +
This track addresses the challenges and solutions related to real-time monitoring and control in electrical systems. Papers should discuss the use of data mining and machine learning for enhancing system responsiveness and reliability.
SDG 9
10 Data-Driven Decision Making in Electrical Engineering +
This session focuses on the impact of data-driven decision-making processes in electrical engineering. Researchers are encouraged to present frameworks and case studies that demonstrate the benefits of integrating data mining into engineering practices.
SDG 4 SDG 9
11 Trends in Electrical Engineering Education and Data Mining +
This track examines the intersection of electrical engineering education and data mining methodologies. Contributions should explore innovative teaching strategies that incorporate data analytics into engineering curricula.
SDG 4 SDG 9