Special Session on Deep Learning for Data Optimization and Enhancement in Complex Systems and Its Applications

Special Session Chairs

• A/Prof. Yunhe Wang, Hebei University of Technology, Tianjin, China

• Prof. Suqi Zhang, Tianjin University of Commerce, Tianjin, China

• A/Prof. Qiqi Liu, Westlake University, Hangzhou, China

Introduction

As modern systems become increasingly complex and data-intensive, the need for effective data optimization and enhancement strategies has never been more critical. These processes are fundamental to improving system performance, enhancing decision-making quality, and ensuring data reliability. However, traditional data processing techniques often fall short in addressing the high dimensionality, heterogeneity, and dynamic characteristics inherent in contemporary complex systems. Deep learning, with its exceptional capabilities in hierarchical feature extraction, nonlinear modeling, and adaptive learning, offers powerful tools to overcome these challenges. When integrated with advanced optimization algorithms and adaptive enhancement techniques, deep learning enables more accurate, scalable, and efficient solutions for data-centric problems in complex environments.

This special session aims to provide a platform for researchers, practitioners, and industry professionals to share and discuss recent developments, emerging trends, and real-world applications at the intersection of deep learning and data optimization or enhancement in complex systems. We welcome high-quality submissions that cover theoretical foundations, algorithmic innovations, and practical implementations, especially those that emphasize the synergistic integration of deep learning techniques with advanced data processing strategies in complex and dynamic settings.

Keywords

Federated Learning; Data-Driven Optimization; Recommendation Systems; Evolutionary Optimization; Deep Learning; Large Language Models

Topics of Interest

Topics include but are not limited to:

•Federated Data-Driven Optimization

•Large-Language Model-Assisted Optimization

•Feature Selection

•Wind Power Prediction

•Social Recommendation

•Single Cell Multi Omics Clustering

•Unmanned Aerial Vehicle Path Planning

•Federated Learning For Recommendation Systems

•Wind Turbine Fault Diagnosis

•Traffic Flow Prediction

•Vehicle Re-Identification Algorithm

•Vital Signs Detection