Titlebook: Data Science; 9th International Co Zhiwen Yu,Qilong Han,Zeguang Lu Conference proceedings 2023 The Editor(s) (if applicable) and The Author

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John M. Bensche,Jennifer C. Ritterorks, this paper optimizes the generator and discriminator based on the SRGAN model. First, the residual dense block is used as the basic structural unit of the generator to improve the network‘s feature extraction capability. Second, enhanced lightweight coordinate attention is incorporated to help

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Overabundance: A Canonical Typologyn. To address these issues, an autoencoder and data augmentation-based anomaly detection model for high-dimensional sparse data is proposed (SEAOD). First, the model solves the problem of imbalanced data by using the weighted SMOTE algorithm and ENN algorithm to fill in the minority class samples an

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Competition in Frisian Past Participlesin large datasets, and before reducing the dimension of the dataset, we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduction. Accordingly, we propose a dimension reduction technique based on biased sampling, a new procedure that incorpo

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https://doi.org/10.1007/978-981-99-5968-6artificial intelligence; communication systems; computer hardware; computer networks; computer systems; c

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