بدائل البحث:
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
relevant study » relevant studies (توسيع البحث), recent study (توسيع البحث)
complement box » complement low (توسيع البحث), complement _ (توسيع البحث), complement 5a (توسيع البحث)
box algorithm » best algorithm (توسيع البحث), _ algorithm (توسيع البحث), ii algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
relevant study » relevant studies (توسيع البحث), recent study (توسيع البحث)
complement box » complement low (توسيع البحث), complement _ (توسيع البحث), complement 5a (توسيع البحث)
box algorithm » best algorithm (توسيع البحث), _ algorithm (توسيع البحث), ii algorithm (توسيع البحث)
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System architecture of this study.
منشور في 2025"…Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. …"
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<b>A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention</b>
منشور في 2024"…Experimental results demonstrate that on a dataset comprising 12 diseases and 2 healthy states, the mean Average Precision at IoU 0.50 (mAP50) of this model achieved 97%, with a Precision of 91.5%; these figures reflect improvements of 1.1% and 1.2%, respectively, compared to the original YOLOv8 algorithm. Additionally, the inference speed increased by 14.6%, fully meeting the real-time requirements for detecting diseases in citrus fields and illustrating the effectiveness and advanced nature of the improved algorithm, thereby providing robust support for the rapid identification of diseases in the citrus cultivation process.…"
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Study flow diagram.
منشور في 2025"…We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…"
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A depiction of the labeled gaps constructed by the bottom-up phase on the input shown in Fig 1.
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Example input of the DPP: a phylogenetic tree T and an alignment A for L(T).
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(Right:) A candidate solution <i>A</i><sup> + </sup> for the example in Fig 1.
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An instance of the DPP that provides a solution of the first half of the IPP problem in Fig 8.
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