scholarly journals Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Fei-Peng Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Bp Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Accuracy Rate ◽  
Financial Credit ◽  
Internet Finance ◽  
Tracking Model ◽  
Credit Data ◽  
Research On Application

The arrival of the era of big data has provided a new direction of development for internet financial credit collection. First of all, the article introduced the situation of internet finance and traditional credit industry. Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information. Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed. It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal. Finally, the rule extraction algorithm is used to simulate the test samples. The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.

scholarly journals Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianhui Wu ◽  
Lu Zhang ◽  
Sufeng Yin ◽  
Haidong Wang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Big Data ◽  
Differential Diagnosis ◽  
Decision Tree ◽  
Cell Morphology ◽  
Bp Neural Network ◽  
Data Platform ◽  
New Ideas ◽  
Medical Big Data

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

scholarly journals Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Yuanyuan Zhang ◽  
Yan Yan
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Risk Assessment ◽  
Big Data ◽  
Data Storage ◽  
Bp Neural Network ◽  
Security And Privacy ◽  
Cloud Environment ◽  
Privacy Risk ◽  
Whole Life Cycle

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

Recognition of Paper Currency Research Based on AGA-BP Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 3968-3972
Author(s):  
Xue Xiao ◽  
Qing Hong Wu ◽  
Ying Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Biological Evolution ◽  
Adaptive Genetic Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Paper Currency ◽  
Randomized Search ◽  
Banknote Recognition

The genetic algorithm is a randomized search method for a class of reference biological evolution of the law evolved, with global implicit parallelism inherent and better optimization. This paper presents an adaptive genetic algorithm to optimize the use of BP neural network method, namely the structure of weights and thresholds to optimize BP neural network to achieve the recognition of banknotes oriented. Experimental results show that after using genetic algorithms to optimize BP neural network controller can accurately and quickly achieved recognition effect on banknote recognition accuracy compared to traditional BP neural network has been greatly improved, improved network adaptive capacity and generalization ability.

Research on Closed-Loop Safety Production System of Hot Oil Pipeline Based on Big Data Mining

2019 ◽  
Author(s):  
Yu Tao ◽  
Li Chuanxian ◽  
Liu Lijun ◽  
Chen Hongjun ◽  
Guo Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Long Distance ◽  
Oil Pipeline ◽  
Big Data Mining ◽  
Actual Production

Abstract The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as large error and complex application. This research relies on actual production data and uses big data mining algorithms such as BP neural network, ARMA, seq2seq to establish oil temperature prediction model. The prediction result is less than 0.5 C, which solves the problem of accurate prediction of dynamic oil temperature during pipeline operation. Combined with pigging, the friction prediction model of standard pipeline section is established by BP neural network, and then the economic pigging period of 80 days is given; and after the friction database is established, the historical friction data are analyzed by using the Gauss formula, and 95% of the friction is set as the threshold data to effectively monitor the variation of the friction due to the long period of waxing in pipelines. The closed loop operation system of hot oil pipeline safety and optimization was formed to guide the daily process adjustment and production arrangement of pipeline with energy saving up to 92.4%. The prediction model and research results based on production big data have good adaptability and generalization, which lays a foundation for future intelligent control of pipelines.

Application of BP Neural Network Based on Petrophysical Big Data Mining

2021 ◽  
Author(s):  
Ding Yu ◽  
Yuan Shixiong ◽  
Deng Rui ◽  
Luo Chenxiang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Bp Neural Network ◽  
Reference Value ◽  
Cloud Computing Environment ◽  
Big Data Mining ◽  
Geological Conditions ◽  
Network Prediction ◽  
Distributed Cloud

Based on the big data mining method of petrophysical data, this paper studies the method and application of BP neural network to establish nonlinear interpretation model in distributed cloud computing environment. The nonlinear mapping relationship between the relative objective logging response and actual formation component is established by extracting the data mining result model, which overcomes existing deficiencies of the conventional logging interpretation procedure based on the homogeneity theory, linear hypothesis and the use of statistical experience simplifying model and parameters. The results show that network prediction model has been improved and has superior reference value for solving practical problems of interpretation under complex geological conditions.

Using BP Neural Network to Predict the Sinter Comprehensive Performance: FeO and Sinter Yield

2013 ◽  
Vol 771 ◽  
pp. 209-212
Author(s):  
Wei Chen ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Hui Juan Zhang ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Bp Neural Network ◽  
High Accuracy ◽  
Raw Material ◽  
Production Data ◽  
Prediction System ◽  
Accuracy Rate ◽  
Actual Production ◽  
Comprehensive Performance

Sinter is the main raw material for ironmaking. It is very important to control sinter chemical composition and comprehensive performance. In this paper, a predictive system for sinter chemical composition FeO and the sinter yield was established based on BP neural network, which was trained by actual production data. The MATLAB m file editor was used to write code directly in this paper.The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

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