Researchers have developed a self-tuning AI framework that dynamically filters noisy graph data to boost reliability and accuracy across industries from healthcare to finance.

Researchers from Jilin University and the University of North Carolina have developed an energy-efficient, stability-boosting data-offloading method that uses advanced optimization algorithms to slash delays and power use in mobile crowdsensing, paving the way for smoother, greener smart-city services.

Researchers at Tongji University and the Shanghai AI Lab show that graph-based neural networks can uncover hidden money-laundering rings and collusion networks in financial transactions far more effectively than traditional methods, offering a clear roadmap for real-world implementation and stronger fraud defenses across banking, insurance, and regulatory systems.