This track focuses on the latest methodologies and applications of predictive analytics in big data environments. Researchers are encouraged to present novel approaches that enhance prediction accuracy and efficiency.

This session will explore innovative data preprocessing methods essential for effective big data analysis. Topics may include data cleaning, normalization, and transformation techniques that improve model performance.

This track emphasizes the importance of feature selection and dimensionality reduction in machine learning. Participants will discuss algorithms and strategies that optimize model training and enhance interpretability.

This session examines frameworks such as Hadoop and Spark that facilitate large-scale data processing. Contributions should highlight performance improvements and case studies demonstrating real-world applications.

This track investigates the role of distributed computing in accelerating machine learning tasks. Researchers are invited to present solutions that leverage distributed systems for enhanced scalability and efficiency.

This session will focus on techniques for real-time analytics and streaming data processing. Presentations should address challenges and solutions in handling continuous data streams effectively.