Jin-xing Liang
Papers
1
Total Citations
10
H-Index
1
About
Dr. Jin-xing Liang is a leading researcher in intelligent fire detection systems, specializing in the fusion of machine learning and computer vision for public safety. His most cited work, "Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video" (2022, 10 citations), addresses critical limitations in traditional fire detectors, including low sensitivity and delayed response. Dr. Liang’s key contribution lies in developing a hybrid model that combines random forest-based feature selection with back propagation neural networks, enabling accurate, real-time fire identification from video streams. This approach significantly reduces false alarms and improves early detection—a vital advancement for safeguarding lives and property. By tackling the challenge of limited sensor effectiveness, his research bridges the gap between conventional fire safety systems and modern AI-driven surveillance. Dr. Liang’s work has been recognized for its practical impact, offering a scalable solution for smart city infrastructure and industrial safety. His ongoing efforts continue to push the boundaries of video-based hazard detection, making him a notable figure in the intersection of environmental monitoring and deep learning.
Research Focus
Key Achievements
Top Papers
- 1