International Conference on Scalable Machine Learning for Big Data in IT

ICSMLBDIT

23rd Jan – 24th Jan 2027 Lisbon, Portugal

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Registration Summary

Conference ICSMLBDIT

Date 23rd Jan – 24th Jan 2027

Location Lisbon, Portugal

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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Conference Session Tracks

Aligned with UN Sustainable Development Goals

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

01 Advancements in Scalable Machine Learning Algorithms +

This track focuses on the latest developments in scalable machine learning algorithms tailored for big data applications. Researchers are invited to present innovative approaches that enhance the efficiency and effectiveness of machine learning in diverse IT environments.

SDG 9 SDG 12

02 Big Data Analytics Frameworks and Tools +

This session will explore various frameworks and tools designed for big data analytics, emphasizing their scalability and performance. Contributions that demonstrate practical implementations and case studies are particularly welcome.

SDG 9 SDG 17

03 Cloud Computing for Intelligent Systems +

This track examines the intersection of cloud computing and intelligent systems, focusing on how cloud infrastructure can support scalable machine learning solutions. Papers that discuss architectural designs, deployment strategies, and real-world applications are encouraged.

SDG 9 SDG 11

04 Predictive Analytics in Information Technology +

This session highlights the role of predictive analytics in enhancing IT decision-making processes. Submissions should address methodologies, case studies, and the impact of predictive models on business outcomes.

SDG 8 SDG 9

05 Data Integration Techniques for Big Data +

This track delves into innovative data integration techniques that facilitate the seamless amalgamation of heterogeneous data sources. Researchers are invited to share their findings on improving data quality and accessibility in big data environments.

SDG 9 SDG 16

06 Performance Monitoring in Scalable Systems +

This session focuses on performance monitoring techniques for scalable machine learning systems, emphasizing the importance of real-time analytics. Contributions that present novel metrics, tools, or frameworks for performance evaluation are highly encouraged.

SDG 9 SDG 12

07 Automation in Data Processing Workflows +

This track explores the role of automation in optimizing data processing workflows within big data contexts. Papers that discuss automated systems, tools, and their impact on efficiency and accuracy are welcome.

SDG 9 SDG 12

08 System Optimization for Machine Learning Applications +

This session addresses system optimization strategies specifically designed for machine learning applications in big data settings. Researchers are invited to present techniques that enhance computational efficiency and resource utilization.

SDG 9 SDG 12

09 AI Algorithms for Enhanced Data Analytics +

This track focuses on the development and application of AI algorithms that improve data analytics capabilities. Contributions that demonstrate the integration of AI techniques in traditional analytics processes are encouraged.

SDG 9 SDG 12

10 Innovations in IT Infrastructure for Big Data +

This session examines the latest innovations in IT infrastructure that support big data processing and analysis. Papers discussing hardware advancements, network architectures, and their implications for scalability are welcome.

SDG 9 SDG 12

11 Case Studies in Scalable Machine Learning Implementations +

This track invites case studies that showcase successful implementations of scalable machine learning solutions across various industries. Submissions should highlight challenges faced, solutions implemented, and the resulting impact on organizational performance.

SDG 9 SDG 12