International Conference on Machine Learning for IT Infrastructure and Big Data

ICMLITIBD

15th Dec – 16th Dec 2026 Tokyo, Japan

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Registration Summary

Conference ICMLITIBD

Date 15th Dec – 16th Dec 2026

Location Tokyo, Japan

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

Calculated based on 5.8% of the registration fee

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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 for Big Data +

This track focuses on the latest developments in machine learning algorithms specifically designed for processing and analyzing large-scale datasets. Contributions that explore novel approaches and enhancements in algorithmic efficiency and accuracy are particularly welcome.

SDG 9 SDG 4

02 Cloud Computing Solutions for Scalable IT Infrastructure +

This session will examine the role of cloud computing in creating scalable IT infrastructures that can handle big data workloads. Papers discussing architectural innovations, deployment strategies, and performance optimization in cloud environments are encouraged.

SDG 9 SDG 11

03 Data Engineering Techniques for Enhanced Analytics +

This track aims to highlight innovative data engineering methodologies that facilitate effective data preparation and processing for analytics. Contributions should address challenges in data integration, transformation, and storage solutions.

SDG 9 SDG 12

04 Predictive Analytics in IT Solutions +

This session will explore the application of predictive analytics in developing robust IT solutions that enhance decision-making processes. Papers that demonstrate the impact of predictive models on operational efficiency and strategic planning are sought.

SDG 8 SDG 9

05 Intelligent Systems for Automation in IT Infrastructure +

This track focuses on the integration of intelligent systems and automation technologies within IT infrastructure. Contributions that showcase the benefits of AI-driven automation in optimizing system performance and resource management are encouraged.

SDG 9 SDG 12

06 Business Intelligence Frameworks Leveraging Big Data +

This session will discuss the development of business intelligence frameworks that utilize big data for actionable insights. Papers that illustrate the intersection of data analytics and business strategy are particularly welcome.

SDG 8 SDG 9

07 Data Governance and Compliance in Big Data Environments +

This track aims to address the critical aspects of data governance and compliance in the context of big data. Contributions should focus on frameworks, policies, and best practices that ensure data integrity and security.

SDG 16 SDG 17

08 AI-Driven Optimization Techniques in IT Infrastructure +

This session will explore AI-driven optimization techniques that enhance the performance and efficiency of IT infrastructure. Papers that present case studies or novel methodologies for resource allocation and system tuning are encouraged.

SDG 9 SDG 12

09 Analytics Tools for Real-Time Data Processing +

This track focuses on the development and application of analytics tools designed for real-time data processing in IT systems. Contributions that demonstrate innovative approaches to handling streaming data and immediate analytics are particularly welcome.

SDG 9 SDG 11

10 Integration of Machine Learning in Data-Driven Insights +

This session will explore how machine learning techniques can be integrated into data-driven insights for improved decision-making. Papers that highlight practical applications and case studies in various industries are encouraged.

SDG 9 SDG 11

11 Future Trends in IT Strategy and Analytics Frameworks +

This track aims to investigate emerging trends in IT strategy and the development of analytics frameworks that support organizational goals. Contributions should focus on the evolving landscape of technology and its implications for future IT practices.

SDG 9 SDG 17