scholarly journals Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Zhihong Li ◽  
Lanteng Wu ◽  
Hongting Tang
Keyword(s):  
Neural Network ◽  
Interest Rate ◽  
Bp Neural Network ◽  
Interval Estimation ◽  
Real World Data ◽  
Proposed Model ◽  
P2p Lending ◽  
New Perspective ◽  
P2p System ◽  
Benefits And Costs

P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers’ borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. This research provides a new perspective from borrowers in exploring the P2P lending. The case base and proposed knowledge are the two contributions for FinTech research.

scholarly journals Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

2021 ◽  
Author(s):  
Xu Wang ◽  
Hongyang Gu ◽  
Tianyang Wang ◽  
Wei Zhang ◽  
Aihua Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Structural Characteristics ◽  
Proposed Model ◽  
The Hierarchical Structure ◽  
Hierarchical Decision ◽  
Tree Methods ◽  
New Perspective ◽  
Simple Combination

AbstractThe fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

scholarly journals An Early Risk Warning of Cross-Border E-Commerce Using BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhichao Deng ◽  
Meiji Yan ◽  
Xu Xiao
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Risk Index ◽  
Principal Component ◽  
Absolute Error ◽  
Kernel Principal Component Analysis ◽  
Cross Border ◽  
Risk Warning ◽  
Proposed Model

In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

scholarly journals Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Na Jiang ◽  
Zhiwei Zhao ◽  
Pan Xu
Keyword(s):  
Neural Network ◽  
Liver Disease ◽  
Chronic Liver Disease ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Chronic Liver ◽  
Ant Colony ◽  
Assessment Model ◽  
Disease Assessment ◽  
Proposed Model

Timely prediction of the mechanism and characteristics of chronic liver disease using next-generation information technology is an effective way to improve the diagnosis rate of chronic liver disease. In this paper, we have proposed a modified backpropagation (BP) neural network with improved ant colony optimization algorithm to process multiple index attribute items describing chronic liver disease and construct a chronic liver disease assessment model. The proposed model is very effective in detecting chronic liver disease on time with acceptable level of accuracy and precision ratio. To verify these claims, the proposed scheme is checked experimentally where 125 groups of 20-dimensional medical test index data items of patients with chronic liver disease were analyzed. Moreover, 13-dimensional index items were preferentially selected as test index attribute items with high sensitivity to chronic liver disease using well-known ROC curves. The 13-dimensional index items were reduced to 5-dimensional comprehensive data items by principal component analysis. The proposed neural network-based model was trained with 115 sets of test indicator sample sets, and the remaining 10 sets of sample sets were used as test samples. Compared with the original 20-dimensional data as the neural network input, the proposed model not only reduces the complexity but also improves the prediction accuracy by 15.07%.

scholarly journals Traffic Sign Classification Using Convolutional Neural Network

2021 ◽  
pp. 01-10
Author(s):  
Pranav Kale ◽  
Mayuresh Panchpor ◽  
Saloni Dingore ◽  
Saloni Gaikwad ◽  
Prof. Dr. Laxmi Bewoor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Driving ◽  
Time Data ◽  
Traffic Sign ◽  
Real World Data ◽  
Data Set ◽  
Proposed Model ◽  
Validation Set

In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]

Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 79-85 ◽  
Author(s):  
Feng Yu ◽  
Zhi Qing Wang ◽  
Xiao Zhong Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Learning Ability ◽  
Time Frequency ◽  
Proposed Model ◽  
Real Coded Genetic Algorithm ◽  
Self Learning ◽  
Single Neural Network

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

Prediction Research of Highway Traveling Passenger Volume Based on Wavelet Neural Network

2013 ◽  
Vol 401-403 ◽  
pp. 1401-1405
Author(s):  
Ting Gui Li ◽  
Li Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Wavelet Neural Network ◽  
Proposed Model ◽  
Passenger Volume ◽  
Simulation Results ◽  
Bp Neural Network Model

Aiming at the problem that it is difficult to predict the highway traveling passenger volume (HTPV), a new prediction model of HTPV based on wavelet neural network (WNN) is proposed. A case study is given to verify the proposed model. The simulation results show that the WNN model has higher convergence speed and prediction precision than the traditional BP neural network model (TBPNNM), and has more practical values.

City Fire Forecasts and Analysis Based on Nonlinear Auto-Regressive Time-Series Model

2012 ◽  
Vol 241-244 ◽  
pp. 1550-1555 ◽  
Author(s):  
Sheng Peng Liu ◽  
Ye Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Support Vector ◽  
Future Developments ◽  
Proposed Model ◽  
Network Method ◽  
Auto Regressive ◽  
The City

The forecasting to future developments of the city fire time series is a challenging task that has been addressed by many researchers due to the importance. In this paper, a Nonlinear Auto-Regressive (NAR) prediction model is applied to forecast the city fire data based on support vector regression. The performances of the NAR prediction model in city fire forecasting are compared with the BP neural network method. The experimental results show that the proposed model performs best.

Optimization Research of the Comprehensive Coke Rate of Blast Furnace Based on the Operational Characteristic of Auxiliary Materials

2012 ◽  
Vol 260-261 ◽  
pp. 3-9 ◽  
Author(s):  
Hua Zhang ◽  
Zhao Hui Feng ◽  
Yan Hong Wang
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Influencing Factors ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Operational Characteristic ◽  
Coke Rate ◽  
New Perspective ◽  
Bp Neural Network Model

With the in-depth study on the blast furnace iron-making process and the operational characteristic of auxiliary materials in iron-making process, the comprehensive coke rate’s main influencing factors based on the operation characteristics of auxiliary materials were found. Then, a BP neural network model was used to simulate the mathematic mapping relationship between comprehensive coke rate and main influencing factors. Based on the established BP neural network model, through setting the comprehensive coke rate lowest as the goal and using the actual production data of a iron &steel company’s 6# blast furnace ,a genetic algorithm method is adopted to find the best optimal combination among the main influencing factors. The results show that after optimization calculation the comprehensive coke rate could be reduced about 35.85kg. A new perspective and a scientific method are proposed to realize the target of energy conservation and emission reduction in ironmaking process in this paper.

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