International Conference on Big Data Integration and Knowledge Management
(ICBDIKM-27)

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

This session will explore innovative methodologies for integrating diverse data sources to support effective knowledge management. Emphasis will be placed on frameworks that facilitate seamless data interoperability and accessibility.

This track examines the role of cloud computing in enabling scalable and efficient big data analytics. Contributions should highlight cloud-based architectures that enhance data processing and storage capabilities.

This session is dedicated to the exploration of optimization methods applicable to complex computational problems. Researchers are invited to discuss algorithms that improve performance in various scientific and engineering domains.

This track will address the development and application of predictive modeling techniques to enhance decision-making processes. Papers should focus on case studies that demonstrate the impact of predictive analytics in real-world scenarios.

This session will delve into the intersection of artificial intelligence and data science, showcasing applications that leverage AI techniques for data analysis. Contributions should highlight innovative use cases that drive insights from large datasets.