scholarly journals Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Zhang ◽  
Qixiang Zhong ◽  
Zhenglun Yu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
High Order ◽  
Diagnosis And Treatment ◽  
Disease Monitoring ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Medical Big Data

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

scholarly journals Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 335
Author(s):  
Rong Chen ◽  
Shishi Liu ◽  
Yufei Yang ◽  
Wei Huang ◽  
Zongwei Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Sampling Design ◽  
Road Networks ◽  
Soil Sampling ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Sports Action Recognition Based on GB-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lidong Wang ◽  
Kai Qiu ◽  
Wang Li
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Action Recognition ◽  
Bp Neural Network ◽  
Big Data Analysis ◽  
Classification Model ◽  
Gradient Boosting ◽  
Network Algorithm ◽  
Neural Network Algorithm

In recent years, the application of the gradient boosting-back propagation (GB-BP) neural network algorithm in many industries has brought huge benefits, so how to combine the GB-BP neural network algorithm with sports has become a research hotspot. Based on this, this paper studies the application of the GB-BP neural network algorithm in wrestling, designs the sports athletes action recognition and classification model based on the GB-BP neural network algorithm, first analyzes the research status of wrestling action recognition, and then optimizes and improves the shortcomings of action recognition and big data analysis technology. The GB-BP neural network algorithm can realize the accurate recognition and classification of wrestlers’ training actions and carry out big data mining analysis with known action recognition, so as to achieve accurate classification. The experimental results show that the model can play a good role in wrestling and effectively improve the efficiency of wrestlers in training.

A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy

2013 ◽  
Vol 4 (4) ◽  
pp. 32-45 ◽  
Author(s):  
Qiuhong Zhao ◽  
Feng Ye ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Active Learning ◽  
Learning Strategy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Active Learning Strategy

This paper introduces the active learning strategy to the classical back-propagation neural network algorithm and proposes punishing-characterized active learning Back-Propagation (BP) Algorithm (PCAL-BP) to adapt to big data conditions. The PCAL-BP algorithm selects samples and punishments based on the absolute value of the prediction error to improve the efficiency of learning complex data. This approach involves reducing learning time and provides high precision. Numerical analysis shows that the PCAL-BP algorithm is superior to the classical BP neural network algorithm in both learning efficiency and precision. This advantage is more prominent in the case of extensive sample data. In addition, the PCAL-BP algorithm is compared with 16 types of classical classification algorithms. It performs better than 14 types of algorithms in the classification experiment used here. The experimental results also indicate that the prediction accuracy of the PCAL-BP algorithm can continue to increase with an increase in sample size.

scholarly journals Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
ChuanDong Yu ◽  
Nan Du
Keyword(s):  
Neural Network ◽  
Landscape Ecology ◽  
Wavelet Neural Network ◽  
High Order ◽  
Average Error ◽  
Ecological Planning ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Landscape Ecological Planning ◽  
Landscape Ecological

Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

scholarly journals Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Xi
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotional Expression ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Big Data Analysis ◽  
Emotion Expression ◽  
Analysis Model ◽  
Network Algorithm ◽  
Neural Network Algorithm

The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

Application of Neural Network in Network Intrusion Detection

2014 ◽  
Vol 644-650 ◽  
pp. 3334-3337
Author(s):  
Zhong Yang ◽  
Hua Du
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Satisfactory Performance ◽  
Network Algorithm ◽  
Neural Network Algorithm

In this paper, we combined the clustering algorithm and neural network algorithm, proposes a new FORBF neural network algorithm based on FCM and OLS, and apply it to the research of intrusion detection system. The simulation results show that, the algorithm has satisfactory performance.

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