Research on intelligent calculation method of intelligent traffic flow index based on big data mining

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Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that

Section: Adjacent Nodesmentioning

confidence: 99%

Section: F I G U R E 6 Flow Chart Of Three-dimensional Medical Image ...mentioning

confidence: 99%

Section: Information Classification Of Overlapping Regions In 3d Medi...mentioning

confidence: 99%

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Research on 3D medical image surface reconstruction based on data mining and machine learning

Hua

Liu

Yin

et al. 2021

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“…13 Deep learning tasks are segmented into several sorts. 14 In supervised learning, target variables are set up and a model is built from the data based on a series of training samples, so as to predict the value of target variables of new data based on the model. 15 For instance, if the task is used to determine whether the image contains an object, images with and without the object (input) will be included in the supervised learning algorithm's training data, and there will be a label (output) on each image indicating if it contains the object.…”

Section: Introductionmentioning

confidence: 99%

“…In the application of cross‐business problems, the model of CNN is usually used to predict results due to the characteristics of multiple business types and large amount of real‐time data, so deep learning is also called predictive analysis 13 . Deep learning tasks are segmented into several sorts 14 . In supervised learning, target variables are set up and a model is built from the data based on a series of training samples, so as to predict the value of target variables of new data based on the model 15 .…”

Section: Introductionmentioning

confidence: 99%

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

Ding

Zhao

Zhang

et al. 2022

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A visual mechanical signal detection and loading platform with super-resolution based on deep learning is designed to improve the detection accuracy of mechanical signals. The visual mechanical signal detection and loading platform with super-resolution include three-dimensional (3D) biological force quantitative detection platform and the mechanical signal loading platform with 3D magnetic distortion and ultrahigh resolution. In the 3D biological force quantitative detection platform, four 3D force sensors are used to collect mechanical signals, and the improved fuzzy clustering fusion method is used to fuse the mechanical signals collected by 3D force sensors to improve the detection accuracy of mechanical signals. The mechanical signal loading platform of 3D magnetic distortion and ultrahigh resolution technology connects the 3D magnetic distortion instrument and microscope, collects images through high-speed scanning components and distorted magnetic field, reconstructs the collected images by deep learning method, obtains ultrahigh-resolution mechanical signal visual images, and triggers mechanical signal loading and release by synchronous interactive system. The consequences of the experiment demonstrate that the designed platform can display the super-

RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

Liu

et al. 2022

Self Cite

For the past several years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing. The U-shaped topology of Unet can well solve image segmentation tasks. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes RT-Unet, which combines the advantages of Transformer and Residual network for accurate medical segmentation. In RT-Unet, the Residual block is taken as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Meanwhile, Skip-Transformer is proposed, which takes Multi-head Self-Attention as the main algorithm framework, instead of the original Skip-Connection layer in Unet to avoid the influence of shallow features on the network's performance. Besides, we add attention module at the decoder to reduce semantic differences. According to the experiments on MoNuSeg data set and ISBI_2018cell data set, RT-Unet achieves better segmentation performance than other deep learning-based algorithms. In addition, a series of further ablation experiments were conducted on Residual network and