International Conference on Software Engineering for Blockchain Systems (ICSEBS - 27)

This track focuses on the application of predictive modeling techniques to enhance the performance and reliability of blockchain systems. Researchers are invited to explore novel approaches that leverage data-driven insights for improved decision-making in blockchain environments.

This session aims to investigate the integration of supervised and unsupervised learning methodologies within blockchain analytics. Papers should discuss innovative algorithms and their effectiveness in extracting meaningful patterns from blockchain data.

This track highlights the use of deep learning techniques to address security challenges in blockchain systems. Contributions should focus on novel architectures and methods that enhance the detection of anomalies and threats within decentralized networks.

This session invites research on advanced anomaly detection techniques specifically tailored for blockchain transactions. Papers should present methodologies that improve the identification of fraudulent activities and enhance transaction integrity.

This track explores innovative feature extraction methods that facilitate effective data analysis in blockchain systems. Contributions should demonstrate how these techniques can improve the understanding and usability of blockchain data.

This session focuses on the automation of workflows in the software engineering lifecycle of blockchain projects. Researchers are encouraged to present frameworks and tools that streamline development processes and enhance productivity.