Efficient colour sorting of Chios mastiha

Pavlidis, George; Mouroutsos, Spyridon G.; Sevetlidis, Vasileios

doi:10.1109/ist.2014.6958510

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Promptly spotting flaws helps avert issues related to quality, leading to cost reductions by circumventing the need for costly revisions or product recalls. As Industry 4.0 progresses, there is a growing reliance on automated systems for inspection to ensure defects are identified instantly, guaranteeing that only the finest quality products reach the consumers [62]. Nevertheless, the acquisition of extensive labeled datasets to train defect detection models that are precise poses a substantial hurdle [63].…”

Section: Weakly Supervised Learning In Defect Detectionmentioning

confidence: 99%

Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

Sevetlidis,

Pavlidis,

Mouroutsos

et al. 2024

Computers

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Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.

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Section: Weakly Supervised Learning In Defect Detectionmentioning

confidence: 99%