Titlebook: Data Mining and Big Data; 6th International Co Ying Tan,Yuhui Shi,Jun Cai Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 20

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Steve Taylor,Tim Matton-Johnsonthe mining power is rather high for the attackers, machine learning is accessary to a tyrant’s crimes. Thus the attackers can maximize their rewards by optimizing their attacking strategies. In this paper, we propose a novel intelligent attacking strategy (aka. BSMRL). More specifically, attackers o

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https://doi.org/10.1057/9781137475473 multi-dividing ontology learning algorithm makes full use of the characteristics of the tree structure ontology graph, and then plays a role in the engineering field. The main contribution of this paper is to use the Rademacher vector method to perform theoretical analysis on the multi-dividing ont

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Kristine Moruzi,Michelle J. Smithth uniform dimension. The essence of the similarity calculation of the ontology concept is the calculation of the distance of the vector corresponding to the vertex in the high-dimensional space. This paper continues to consider the ontology learning algorithm of the multi-dividing setting, and prop

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Colonial Heritage, Power, and Contestationmplex morphology and wide variety of bone marrow cells, their segmentation is still a challenging task. To improve the accuracy of bone marrow cell segmentation, we propose an end-to-end U-shaped network based on the pyramid residual convolution and the attention mechanism. Specifically, the standar

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Ana Cristina Pandolfo,Jaisson Teixeira Linoherefore, the classification of WBCs in the images is a basic task. Most of the existing WBCs classification methods are based on supervised learning, which highly depends on a large number of image labels. To cope with the challenge of image annotation, in this paper, we propose an unsupervised WBC