Tentative Program

This track focuses on the latest methodologies in predictive modeling tailored for IoT applications in engineering. Researchers are invited to present innovative approaches that enhance predictive accuracy and reliability.

This session will explore cutting-edge techniques for processing sensor data in engineering contexts. Contributions should address challenges and solutions related to data quality, integration, and real-time processing.

This track aims to discuss the application of supervised and unsupervised learning techniques in various engineering domains. Papers should highlight novel algorithms and their effectiveness in solving engineering problems.

This session will delve into the application of deep learning methodologies in the context of industrial IoT. Participants are encouraged to share insights on model architectures and their impact on engineering processes.

This track will focus on the development and application of anomaly detection techniques within IoT systems. Contributions should emphasize real-world applications and the implications for system reliability and safety.

This session will explore innovative approaches to feature extraction that improve data analytics in engineering applications. Papers should discuss the impact of feature selection on model performance and interpretability.