Tentative Program

This track focuses on the development and application of predictive modeling techniques in food safety engineering. Researchers will present innovative approaches that leverage data analytics to enhance food safety protocols.

This session explores the integration of supervised and unsupervised learning methods in food processing. Participants will discuss case studies demonstrating the effectiveness of machine learning in optimizing food safety and quality.

This track highlights the use of deep learning algorithms for detecting anomalies in food production systems. Presentations will cover various methodologies and their implications for improving food safety standards.

This session delves into the importance of feature extraction in enhancing predictive models for food safety. Researchers will share insights on techniques that improve model accuracy and reliability.

This track examines the role of workflow automation in streamlining food safety processes. Discussions will focus on technologies that facilitate real-time monitoring and compliance in food production.

This session investigates the integration of Industrial Internet of Things (IIoT) in food safety engineering. Participants will explore how IIoT technologies enhance monitoring, data collection, and decision-making in food systems.