This track focuses on the latest innovations in neural network designs and architectures. Researchers are encouraged to present their findings on novel structures that enhance performance in various applications.

This session will explore ensemble learning methods that combine multiple models to improve predictive accuracy. Contributions that demonstrate the effectiveness of these techniques in real-world scenarios are particularly welcome.

This track addresses the integration of different machine learning models to create hybrid systems. Papers discussing innovative model fusion techniques and their applications in engineering are encouraged.

This session will highlight the application of reinforcement learning in engineering domains. Researchers are invited to share case studies and methodologies that showcase the practical implementation of these techniques.

This track focuses on methods for effective feature extraction and dimensionality reduction in high-dimensional datasets. Contributions that enhance model performance through these techniques are sought.

This session will cover advanced methods for detecting anomalies in various engineering systems using machine learning. Papers that present novel algorithms or applications in this area are highly encouraged.