Aligned with
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
This track focuses on the latest advancements in big data analytics techniques and tools. Researchers are invited to present their findings on novel methodologies that enhance data interpretation and decision-making.
This session explores the integration of machine learning algorithms in various engineering domains. Contributions that demonstrate practical applications and case studies are highly encouraged.
This track examines the role of cloud computing in facilitating big data processing and storage. Papers should address scalability, performance, and cost-effectiveness of cloud-based solutions.
This session focuses on the development of intelligent systems that leverage big data for automation. Contributions should highlight innovative approaches to system design and optimization.
This track investigates the application of predictive analytics in enhancing IT infrastructure management. Researchers are invited to discuss techniques that improve reliability and efficiency.
This session addresses challenges and solutions related to data integration in big data environments. Contributions should explore methodologies that ensure seamless data flow across diverse systems.
This track focuses on the development and application of AI algorithms that support decision-making processes. Papers should present innovative approaches that improve accuracy and efficiency.
This session examines frameworks that enable scalable computing for big data applications. Contributions should discuss architectural designs and performance evaluations of these frameworks.
This track explores the intersection of business intelligence and big data analytics. Researchers are encouraged to present studies that demonstrate how big data can inform strategic business decisions.
This session focuses on innovative data processing techniques that enhance machine learning outcomes. Contributions should highlight preprocessing, feature selection, and data augmentation methods.
This track investigates optimization strategies for IT systems leveraging big data and AI technologies. Papers should present methodologies that enhance system performance and resource utilization.