International Conference on Blockchain Technologies and Machine Learning
(ICBTML-27)

This track focuses on the latest advancements in blockchain technologies, emphasizing their integration with machine learning. Researchers are invited to present novel approaches that enhance the functionality and security of blockchain systems.

This session explores the application of machine learning techniques for predictive modeling within blockchain environments. Contributions should highlight methodologies that improve forecasting accuracy and decision-making processes in decentralized systems.

This track addresses the challenges of anomaly detection in distributed ledger technologies. Participants are encouraged to present innovative algorithms and frameworks that enhance the identification of fraudulent activities and security breaches.

This session showcases the integration of deep learning methodologies in blockchain applications. Papers should discuss the implications of deep learning for enhancing data analysis, transaction validation, and overall system performance.

This track investigates the intersection of smart contracts and machine learning. Submissions should focus on how machine learning can optimize smart contract execution and improve their reliability in various applications.

This session delves into risk assessment methodologies applicable to blockchain networks. Researchers are invited to present frameworks that evaluate vulnerabilities and propose mitigation strategies using machine learning.