This track focuses on the latest methodologies and technologies aimed at enhancing data privacy in engineering applications. Contributions may include novel encryption methods, secure data processing techniques, and frameworks for data governance.

This session explores the application of machine learning algorithms in predictive analytics within engineering contexts. Topics may cover supervised and unsupervised learning techniques, as well as their implications for data-driven decision-making.

This track addresses the challenges and solutions related to anomaly detection in large-scale engineering datasets. Participants are encouraged to present innovative approaches that leverage deep learning and statistical methods.

This session delves into techniques for feature extraction and data transformation to improve model performance in engineering applications. Discussions will include methodologies that enhance data representation and facilitate better insights.

This track examines the security challenges associated with data processing in Internet of Things (IoT) systems. Contributions should focus on strategies for ensuring data confidentiality and integrity in industrial IoT applications.

This session highlights methodologies for risk assessment and management in the context of data security within engineering domains. Papers may address frameworks for evaluating vulnerabilities and implementing effective mitigation strategies.