Titlebook: Computer Science and Education. Computer Science and Technology; 18th International C Wenxing Hong,Geetha Kanaparan Conference proceedings

Decongestant

窒息

https://doi.org/10.1007/978-1-4613-3646-4cts the information traces inevitably generated during network intrusion by deep neural network and optimizes the parameters of deep neural network model by improved seagull optimization algorithm, so as to build an efficient network intrusion detection model. The traditional seagull optimization al

insipid

Abigail J. Stewart PH.D.,Patricia Salt PH.D.hm (DA) is one of the optimization algorithms, which has been used in some aspects. But it still has some shortcomings, such as slow convergence speed and search precision, and it is also prone to fall into local optimal solutions. Aiming at the shortcomings of the original dragonfly algorithm, a mi

鐵塔等

canvass

CHASE

https://doi.org/10.1057/9780230523234rk to train pseudo-labels. While clustering the learning features of the residual network, the network parameters are updated to achieve. The clustering performance of pseudo-labeled datasets is improved to solve the problem of scarcity of labeled data.

BOON

Justin Greenwood,Alfons Westgeestlems. In order to address the problem of small inter-class similarity among vehicles, previous studies utilize vehicle parsing models to extract local features. Therefore, we introduce the Squeeze-and-Excitation attention mechanism to extract important discriminative information from these local fea

微粒

Horizontal Business Associations at EU Levelching situation. Based on the deep learning method, we propose a crowd localization and counting model for students’ seating area in offline classroom. Firstly, we choose YOLOv8 to improve it, adding the SENet attention module after the backbone network reinforce the role of important channels and s

flex336

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