scholarly journals Therapeutic Effect of Cervical Cerclage on Cervical Insufficiency during Pregnancy Analyzed by Magnetic Resonance Image under Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yongjuan Liu ◽  
Yongpan Tan ◽  
Rongxia Liu ◽  
Xuekui Ye ◽  
Lina Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Control Group ◽  
Cervical Insufficiency ◽  
Cervical Cerclage ◽  
Normal Delivery ◽  
Bleeding Rate ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Experimental Group

Objective. This research was developed to investigate the effect of magnetic resonance imaging (MRI) analysis based on neural network algorithm for cervical ligation in the treatment of cervical insufficiency. Methods. 44 patients who were suspected to be pregnant with cervical insufficiency and needed cervical ligation were selected. MR imaging analysis was performed before cervical ligation. MR images were analyzed based on the back propagation neural network (BPNN) algorithm, and patients were randomly divided into experimental group and control group. Preoperative MRI analysis was performed in the experimental group, while simple transvaginal ultrasonography was used to diagnose cervical insufficiency in the control group. Then, postoperative fetal preservation time, vaginal bleeding rate, and infection rate within one week after surgery were compared between the two groups. Results. Based on experience and experimental testing, the relevant parameters were set as follows. The number of particles n = 50, the inertia weight ω = 0.9, and c1 = c2 = 2. The weight range of the output layer of the neural network was [−1, 1], the target error e = 10−5, and the maximum number of iteration steps was 3,000. Compared with the control group, the experimental group’s postoperative bleeding rate and infection probability were substantially reduced, while the normal delivery rate was substantially increased ( P < 0.05 ). Conclusion. MR image analysis based on neural network algorithm played an important role in cervical cerclage surgery. The image map showed the local anatomy clearly, increasing the success rate of the operation and improving the prognosis of the patient.

scholarly journals Deep Learning in Laparoscopic Colorectal Carcinoma Surgery under Magnetic Resonance Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shuguang Pan ◽  
Wei Tang ◽  
Tiejun Zhou ◽  
Wei Luo
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Laparoscopic Surgery ◽  
Magnetic Resonance ◽  
Colorectal Carcinoma ◽  
Control Group ◽  
Resonance Imaging ◽  
Neural Network Algorithm ◽  
Experimental Group

This study aimed to explore the application effect of magnetic resonance imaging (MRI) based on deep learning in laparoscopic surgery for colorectal carcinoma (CRC). 40 patients with CRC who were diagnosed and required laparoscopic surgery were selected in the research. The MRI scan images of all patients were processed based on the convolutional neural network algorithm. The MRI images before and after treatment were set as the control group and the experimental group, respectively. The consistency of MRI results with laparoscopic and postoperative pathological biopsy results was observed. Through the comparative analysis of the research results, in terms of consistency with the surgical plane, the assessment results of the experimental group were more consistent than those of the control group and direct observation under laparoscopy, and the difference was statistically significant ( P < 0.05 ). In terms of tumor T staging, the consistency between the experimental group and pathological biopsy results was superior to that of the control group, with considerable difference ( P < 0.05 ). In conclusion, practically speaking, the application of MR images based on convolutional neural network algorithm in laparoscopic CRC surgery was better than conventional MRI technology. However, the research was a small-scale pathological study, which was not very representative.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

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