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• W4353068511 abstract "Water pollution poses significant risks to society and human health, necessitating the development of low-cost and environmentally friendly methods for identifying various pollutants. Artificial intelligence has the potential to be a valuable tool in this regard. Locomotor behavior is a complex integration of physiological, sensory, nervous, and muscular systems. Organisms exhibit unique “locomotor track fingerprints” under the influence of differential mechanisms of different chemicals. In this study, a process was developed to determine chemicals by identifying locomotor tracks of Daphnia magna. Using the high-throughput in vivo exposure method, a large number of images for deep learning model training were obtained, which solved the problem of lack of training data sets. The ResNet50 model was able to achieve 87.3% classification accuracy across 117 classes after hyperparameter tuning. Interpretability analysis showed that the model can accurately extract relevant features. This research proposed a low-cost and environmentally friendly method for identifying water pollutants by combining in vivo exposure with deep learning." @default.
• W4353068511 created "2023-03-23" @default.
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• W4353068511 date "2023-03-22" @default.
• W4353068511 modified "2023-10-17" @default.
• W4353068511 title "Identification of Chemicals Based on Locomotor Tracks of <i>Daphnia magna</i> Using Deep Learning" @default.
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• W4353068511 doi "https://doi.org/10.1021/acs.estlett.3c00162" @default.
• W4353068511 hasPublicationYear "2023" @default.
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