[visionlist] [Call for Papers]: Multi-Sensor and Synthetic Data Fusion in Advanced AI Systems: Opportunities and Ethical Challenges

[Ivanovska, Marija]

*****************************
Multi-Sensor and Synthetic Data Fusion in Advanced AI Systems: Opportunities and Ethical Challenges
Special Issue in Elsevier’s Information Fusion (IF:14.8)
Paper Submission Deadline: July 31, 2025
More information: https://www.sciencedirect.com/special-issue/316809/multi-sensor-and-synthetic-data-fusion-in-advanced-ai-systems-opportunities-and-ethical-challenges
*****************************

*** Call for Papers ***
The rapid integration of Artificial Intelligence (AI) and data fusion across various sectors are pivotal for increasing operational efficiency and ensuring public safety and security. Simultaneously, the emergence of highly realistic synthetic data is revolutionizing approaches to AI challenges, particularly those related to data scarcity and privacy.This special issue aims to explore the broad applications of information fusion, focusing on integrating data from multiple sources with synthetic data to enhance systems' learning and precision in decision-making processes. Additionally, it will address the ethical concerns inherent in using synthetic data and large-scale data integration across various domains.
Topics include but are not limited to:

  *   Multimodal AI Systems: Data fusion techniques for combining visual, verbal, and sensor data; Behavioral analysis and anomaly detection using fused data streams; Data fusion for real-time applications;
  *   Synthetic Data in AI: Novel generative models (diffusion models, GANs, VAEs) for data synthesis; Label generation and benchmarking of synthetic datasets; Synthetic realities for data collection and analysis;
  *   Fusion of Synthetic and Real Data: Combining synthetic and real data to enhance model learning; Leveraging synthetic data for increased data diversity and volume; Enhanced privacy protection through utilization of synthetic data; Synthetic realities in immersive media;
  *   Data Ethics and Privacy: Accountability and transparency in data fusion applications; Information leakage and privacy concerns in synthetic data; Privacy-preserving techniques in large-scale data integration; Data synthesis for bias mitigation and fairness;
  *   Case Studies and Applications: Case studies of integrated surveillance systems in smart cities, industrial environments, biometric applications; Case studies on implementations of synthetic data in smart environments and biometric systems; Data fusion for bias mitigation and fairness in security and surveillance applications; Fusion of high-altitude and ground-level data for comprehensive monitoring.

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20250503/f4019d92/attachment.html>