This track focuses on the latest methodologies and applications of predictive modeling in various engineering domains. Researchers are invited to present their findings on enhancing prediction accuracy through innovative techniques.

This session aims to explore the distinctions and applications of supervised and unsupervised learning in computer science engineering. Contributions that highlight novel algorithms or frameworks are particularly encouraged.

This track will delve into the transformative impact of deep learning technologies across engineering disciplines. Participants are invited to share case studies and research that demonstrate practical applications and outcomes.

This session addresses the challenges and solutions related to anomaly detection within complex engineering systems. Submissions that present new techniques or frameworks for effective anomaly identification are welcome.

This track focuses on innovative methods for feature extraction and representation in data-intensive engineering applications. Researchers are encouraged to discuss their approaches to improving data interpretability and model performance.

This session will explore the development and implementation of expert systems in engineering contexts. Contributions that address knowledge representation techniques and their practical implications are highly sought after.