Parallel and Distributed Computing (ICPDC-26)

This track focuses on the latest developments in parallel computing architectures, including hardware innovations and software frameworks. Researchers are invited to present their findings on optimizing performance and efficiency in parallel systems.

This session aims to explore various distributed computing paradigms and their real-world applications. Contributions that address challenges in scalability, fault tolerance, and resource management are particularly welcome.

This track invites papers that investigate machine learning methodologies for predictive modeling in diverse domains. Emphasis will be placed on supervised and unsupervised learning approaches and their practical implications.

This session highlights the role of deep learning in enhancing anomaly detection systems across various industries. Participants are encouraged to share novel algorithms and case studies demonstrating effective anomaly detection.

This track focuses on innovative feature extraction and data preprocessing methods that improve model performance. Contributions that address challenges in high-dimensional data and noise reduction are particularly encouraged.

This session explores the automation of workflows in parallel and distributed computing environments. Papers that discuss frameworks, tools, and methodologies for enhancing workflow efficiency are welcome.

This track addresses the integration of system monitoring techniques with predictive maintenance strategies in industrial settings. Researchers are invited to present solutions that leverage data analytics for proactive maintenance.

This session focuses on the evaluation of machine learning models and the development of robust performance metrics. Contributions that propose new evaluation frameworks or address existing challenges in model assessment are encouraged.

This track examines the intersection of industrial IoT and resource allocation strategies in distributed environments. Papers that present innovative approaches to optimize resource utilization in IoT systems are particularly welcome.

This session explores the integration of cloud computing solutions with high-performance computing frameworks. Contributions that address scalability, cost-effectiveness, and performance optimization in cloud-based HPC are encouraged.

This track focuses on optimization techniques applied to network analytics, including resource allocation and traffic management. Researchers are invited to present novel algorithms and their applications in real-time network scenarios.