This track explores the integration of blockchain technology in predictive modeling applications within computer science. It aims to discuss innovative methodologies that enhance the accuracy and reliability of predictive models through decentralized data management.

This session focuses on the application of supervised learning techniques to improve blockchain-based systems. Participants will present research on algorithmic advancements that optimize data processing and decision-making in blockchain environments.

This track examines the role of unsupervised learning in enhancing data security within blockchain frameworks. Discussions will center on novel approaches to anomaly detection and feature extraction that bolster the integrity of distributed ledger systems.

This session delves into the application of deep learning techniques to optimize smart contract performance and security. Researchers will share insights on how machine learning models can predict contract outcomes and mitigate risks.

This track highlights the intersection of blockchain technology and industrial IoT for effective anomaly detection. Presentations will cover case studies and frameworks that utilize blockchain to enhance system monitoring and predictive maintenance.

This session focuses on the automation of workflows using blockchain technology to streamline processes in computer science engineering. Contributions will include methodologies that leverage distributed ledgers for efficient task management and process optimization.