This track focuses on the latest innovations in blockchain technology and its applications within computer science engineering. Researchers are encouraged to present their findings on scalability, interoperability, and security enhancements.

This session explores various predictive modeling techniques applicable to distributed ledger technologies. Contributions should highlight methodologies that improve accuracy and efficiency in real-time data analysis.

This track invites papers that investigate the integration of machine learning algorithms with blockchain systems. Topics may include supervised and unsupervised learning approaches for enhancing data security and transaction efficiency.

This session aims to address the challenges of anomaly detection within distributed ledger technologies. Researchers are encouraged to present novel algorithms and frameworks that enhance the identification of fraudulent activities.

This track focuses on innovative techniques for feature extraction from blockchain data. Papers should discuss methods that improve data representation and facilitate advanced analytics in distributed systems.

This session explores the role of workflow automation in optimizing processes within distributed ledger environments. Contributions should demonstrate how automation can enhance efficiency and reduce operational risks.