False Positive/Negative Rates
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
The studies lack comprehensive error analysis, particularly in false positive and negative rates for machine learning models across various contexts such as pavement crack detection, deforestation prediction, employee performance monitoring, and threat identification in fog-IoT systems.
Consensus across the literature
The papers collectively establish the need for more rigorous error analysis but leave open how to systematically address false positives and negatives.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- Research on Pavement Crack Identification Method Based on Improved YOLOv11 (2026) · doi
The visualization results section appears incomplete ('it can be seen from Figure 5 that the original YOLOv11 model exhibited issues of false dete'), and no discussion of false positive/negative cases or failure modes is provided.
Keywords: false visualization appears incomplete seen original yolov model exhibited issues dete discussion positive negative cases - Implementation of Machine Learning Approach for Deforestation Prediction (2026) · doi
The study does not discuss error analysis, false positive/negative cases, or specific failure scenarios of the CNN model.
Keywords: discuss error false positive negative cases specific failure scenarios model
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