International Conference on Machine Learning Algorithms for Big Data

ICMLABD

15th Dec – 16th Dec 2026 Budapest, Hungary

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Registration Summary

Conference ICMLABD

Date 15th Dec – 16th Dec 2026

Location Budapest, Hungary

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

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Total Payable USD 0.00

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Conference Session Tracks

Aligned with UN Sustainable Development Goals

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

01 Advancements in Machine Learning Algorithms +

This track focuses on the latest developments in machine learning algorithms tailored for big data applications. Researchers are encouraged to present novel methodologies that enhance predictive accuracy and computational efficiency.

SDG 4 SDG 9 SDG 17

02 Big Data Processing Techniques +

This session will explore innovative techniques for processing and analyzing large datasets. Contributions that address scalability and performance optimization in big data environments are particularly welcome.

SDG 9 SDG 11

03 AI and Intelligent Systems in Engineering +

This track examines the integration of artificial intelligence within intelligent systems in engineering contexts. Papers should highlight practical applications and case studies that demonstrate the impact of AI on engineering processes.

SDG 4 SDG 9 SDG 11

04 Data Engineering and Infrastructure +

This session addresses the critical aspects of data engineering and the underlying IT infrastructure necessary for big data analytics. Topics include data integration, storage solutions, and the role of cloud computing in enhancing data accessibility.

SDG 9 SDG 11

05 Predictive Analytics in Industry +

This track will showcase research on the use of predictive analytics to drive decision-making in various industries. Submissions should focus on real-world applications and the effectiveness of machine learning models in predicting outcomes.

SDG 8 SDG 9 SDG 12

06 Optimization Techniques for Machine Learning +

This session will delve into optimization strategies that improve the performance of machine learning models. Contributions that propose novel optimization algorithms or frameworks are encouraged.

SDG 9 SDG 12

07 Analytics Frameworks for Big Data +

This track focuses on the development and evaluation of analytics frameworks designed for big data environments. Papers should discuss the architecture, scalability, and usability of these frameworks in practical scenarios.

SDG 9 SDG 11

08 Automation in Data Science +

This session explores the role of automation in streamlining data science workflows. Contributions that highlight automated machine learning processes and their implications for efficiency and accuracy are welcome.

SDG 9 SDG 12

09 Business Intelligence and Data Visualization +

This track will investigate the intersection of business intelligence and data visualization techniques. Papers should present innovative approaches to visualizing complex data sets and their impact on business decision-making.

SDG 9 SDG 12

10 Scalable Computing Solutions for Big Data +

This session addresses the challenges and solutions associated with scalable computing in the context of big data. Contributions that discuss distributed computing frameworks and their applications are particularly encouraged.

SDG 9 SDG 11

11 Integration of AI in IT Innovation +

This track examines the transformative role of AI in driving IT innovation. Papers should explore case studies and theoretical frameworks that illustrate the synergy between AI technologies and IT advancements.

SDG 9 SDG 11