Titlebook: Artificial Intelligence in Data and Big Data Processing; Proceedings of ICABD Ngoc Hoang Thanh Dang,Yu-Dong Zhang,Bo-Hao Chen Conference pr

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书目名称Artificial Intelligence in Data and Big Data Processing影响因子(影响力)




书目名称Artificial Intelligence in Data and Big Data Processing影响因子(影响力)学科排名




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书目名称Artificial Intelligence in Data and Big Data Processing网络公开度学科排名




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书目名称Artificial Intelligence in Data and Big Data Processing被引频次学科排名




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书目名称Artificial Intelligence in Data and Big Data Processing年度引用学科排名




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书目名称Artificial Intelligence in Data and Big Data Processing读者反馈学科排名

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Determinanten der Familienmodellwahl,mance. To improve convergence and performance in object detection, many researchers have modified and proposed Intersection over Union (IoU) loss functions. In existing researches, the loss functions have some main drawbacks. Firstly, the IoU-based loss functions are inefficient enough to perform th

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https://doi.org/10.1007/978-3-531-91362-9t variable. To improve the performance, we consider resampling the dataset and ensembling the classifiers. The benchmarks are taken from the best performance among six considered classifiers. Resampling the dataset includes oversampling and undersampling. The performance of ensemble classifiers are

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Determinanten der Familienmodellwahl,It establishes a routine that informs the student of their responsibilities throughout the semester. Creating a well-constructed course schedule takes a long time and a lot of human effort when managers have to put up subjects, classes, lecturers into constrained duration. To solve these issues, we

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Entreaty

背书

https://doi.org/10.1007/978-3-531-91362-9ious research applies a semantic parser to transform a sentence into a semantic graph, while this heuristic approach can be regarded as the data augmentation technique. Following this idea, this manuscript investigates the Dependency Parsing graph via GNNs to improve the current performance of the T

最有利

Determinanten der Familienmodellwahl,s time and effort. This paper investigates several text summarization models based on neural networks, including extractive summarization, abstractive summarization, and abstractive summarization based on the re-writer approach and bottom-up approach. We perform experiments on the CTUNLPSum dataset

圣歌

MEET