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International Conference on Data Mining and Machine Learning

ICDMM

7th Sep – 8th Sep 2026 Hamburg, Germany

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

Connect with global educators & researchers

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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Use Coupon Code → EARLY10
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Terms & Condition

Conference Session Tracks

UN SDG Wheel

Aligned with UN Sustainable Development Goals

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

SDG 9 SDG 12
01 Advancements in Supervised Learning Techniques +
This track focuses on the latest developments in supervised learning methodologies, emphasizing their application in engineering contexts. Researchers are invited to present novel algorithms and models that enhance predictive accuracy and efficiency.
SDG 9 SDG 12
02 Unsupervised Learning for Complex Data Structures +
This session explores the application of unsupervised learning techniques in identifying patterns within complex datasets. Contributions may include innovative clustering algorithms and their implications for engineering problems.
SDG 9 SDG 12
03 Deep Learning Architectures in Engineering Applications +
This track highlights the role of deep learning architectures in solving engineering challenges, including image and signal processing. Presentations should focus on novel neural network designs and their performance in real-world scenarios.
SDG 9 SDG 12
04 Feature Extraction and Dimensionality Reduction Techniques +
This session addresses the critical role of feature extraction and dimensionality reduction in enhancing model performance. Researchers are encouraged to share techniques that optimize data representation for machine learning tasks.
SDG 9 SDG 12
05 Predictive Analytics in Engineering Decision-Making +
This track examines the use of predictive analytics to inform engineering decision-making processes. Papers should discuss methodologies that leverage historical data to forecast future trends and outcomes.
SDG 9 SDG 12
06 Anomaly Detection in Big Data Environments +
This session focuses on the challenges and solutions related to anomaly detection in large-scale datasets. Contributions should highlight innovative approaches that improve the identification of outliers in engineering applications.
SDG 9 SDG 12
07 Ensemble Methods for Enhanced Classification +
This track investigates the effectiveness of ensemble methods in improving classification performance across various engineering domains. Researchers are invited to present empirical studies and theoretical advancements in this area.
SDG 9 SDG 12
08 Association Rule Mining in Engineering Data +
This session explores the application of association rule mining techniques to uncover hidden relationships within engineering datasets. Contributions should demonstrate practical applications and the impact of these findings on engineering practices.
SDG 9 SDG 12
09 Regression Analysis for Engineering Predictions +
This track focuses on the application of regression analysis in modeling and predicting engineering phenomena. Papers should present novel approaches and case studies that illustrate the utility of regression techniques.
SDG 9 SDG 12
10 Knowledge Discovery in Engineering Systems +
This session emphasizes the process of knowledge discovery from engineering data, highlighting methodologies that transform raw data into actionable insights. Researchers are encouraged to share their findings on effective data mining strategies.
SDG 9 SDG 12
11 Data Preprocessing Techniques for Machine Learning +
This track addresses the importance of data preprocessing in the machine learning pipeline, focusing on techniques that enhance data quality and model performance. Contributions should explore innovative methods for cleaning, transforming, and preparing data for analysis.
SDG 9 SDG 12