Khang Nguyen Quoc
Papers
1
Total Citations
65
H-Index
1
About
Khang Nguyen Quoc is a rising researcher at the forefront of explainable artificial intelligence (XAI) and its transformative applications in smart agriculture. His work addresses a critical gap in modern deep learning: while models achieve high accuracy, their decision-making processes remain opaque. In his highly cited 2023 paper, "Explainable Deep Learning Models With Gradient-Weighted Class Activation Mapping for Smart Agriculture" (65 citations), Quoc introduced a novel framework that integrates Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize and interpret model predictions for agricultural image classification. This contribution not only enhances trust in AI-driven crop monitoring but also provides a rigorous methodology for evaluating model effectiveness in real-world farming scenarios. By bridging the gap between black-box deep learning and practical, transparent decision support, his work empowers farmers and agronomists to adopt AI tools with confidence. Quoc’s research has quickly gained traction, reflecting the urgent need for interpretable AI in precision agriculture. His achievements mark him as a key voice in the emerging field of XAI for sustainable food production, offering a blueprint for future studies that prioritize both performance and explainability.
Research Focus
Key Achievements
Top Papers
- 1