A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Supplier Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Supplier Evaluation ◽  
Test Results ◽  
Model Training

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data

2004 ◽  
Vol 57 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Ahmed El-Rabbany ◽  
Mohammed El-Diasty
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Noise Suppression ◽  
Back Propagation ◽  
Frequency Noise ◽  
Original Signal ◽  
Back Propagation Algorithm ◽  
Major Drawback ◽  
Noise Component

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

scholarly journals A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks

2015 ◽  
Vol 5 (5) ◽  
pp. 1158 ◽  
Author(s):  
M R Narasinga Rao ◽  
Deepthi Gurram ◽  
Sai Mahathi Vadde ◽  
Sathish Tallam ◽  
N. Sai Chand ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Educational Institution ◽  
Research Performance ◽  
Back Propagation ◽  
Educational Data Mining ◽  
Statistical Techniques ◽  
Back Propagation Algorithm ◽  
Prime Concern

<div class="WordSection1"><p>Assessing the performance of an educational institute is a prime concern in an educational scenario. Educational Data Mining (EDM) considers several tasks originated from an educational context. One of the tasks identified is providing feedback for supporting instructors, administrators, teachers, course authors in decision making and thereby enable them to take appropriate remedial action. In this research, we have developed a prototype Neural Network Model which is trained to predict the performance of an educational institution. A Multilayer Perceptron Neural Network (MLP) model had been developed for this proposed research. The network is trained by back propagation algorithm. Data was obtained from a well-defined questionnaire consisting of 14 questions in the domains namely Academic Schedule, International Exposure, Jobs and Internship, Quality of the college, and Life at Campus. The results of these questions have been taken as inputs and performance of the institute has been considered as the output. To, validate the results generated by the network, statistical techniques have been used for the purpose. In this proposed research performance of an educational institution has been predicted. The results generated by the Neural Network and the statistical techniques have been compared in this research and it is observed that, both the methods have generated accurate results. The results have been considered based on the Normalized System Error (NSE) values of the network. A prototype Neural Network model has been developed to assess the performance of an educational institution.</p><strong></strong></div>

scholarly journals An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram

2019 ◽  
Vol 8 (2S6) ◽  
pp. 502-507
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
The Body ◽  
Mental Wellbeing ◽  
Daily Routine ◽  
Back Propagation Algorithm ◽  
Suggested Technique

People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone's response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.

scholarly journals METHODOLOGY FOR DETERMINING THE TIMING OF THE MANUFACTURE OF NEW ORDERS IN SINGLE PRODUCTION AND REDUCING THE RISKS OF PRODUCTION ACTIVITIES

2021 ◽  
Vol 23 (4) ◽  
pp. 27-34
Author(s):  
E.A. Koleganova ◽  
◽  
V.V. Kokareva ◽  
A.I. Khaimovich ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
High Tech ◽  
Complex Method ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Single Production

The article is devoted to the development and testing of the methodology for assigning the priority of technological operations for a number of orders and assessing the risks of setting the price and deadlines for new orders, taking into account it’s complexity and priority. It is noted the main problems of high-tech single production and the risks they create. To solve the identified problems on the basis of the analysis, a complex method was chosen, consisting of a combination of the use of simulation modeling and neural network modeling. The neural network model is based on the statistics of the production times of various parts in this area. The type of neural network model selected by the Fitting app is trained by the network using the Levenberg-Marquardt error back propagation algorithm. The simulation model of the production site is built in the Tecnomatix Plant Simulation program. As a result, thanks to the developed methodology, it became possible to obtain information about a new order before it was directly introduced into production, to diversify risks before they caused damage, as well as to improve reputation. In conclusion, as an example, the addition of a new part to other parts already being produced is given, and the time of its production is calculated.

An Neural Network Model for the Fe/SiO2 Ratio in Copper Flash Smelting Slag Using Improved Back Propagation Algorithm

2012 ◽  
Vol 524-527 ◽  
pp. 1963-1966
Author(s):  
Hong Lu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Local Minimum ◽  
Back Propagation ◽  
Flash Smelting ◽  
Smelting Process ◽  
Back Propagation Algorithm ◽  
Technological Parameters ◽  
Smelting Slag

The Fe/SiO2 ratio in slag is one of the important control parameters for copper flash smelting process, but it is difficult to describe the complex relationship between the technological parameters and the Fe/SiO2 ratio in slag using accurate mathematic formulae, because the copper flash smelting process is a complicated nonlinear system. An neural network model for the Fe/SiO2 ratio in copper flash smelting slag was developed, whose net structure is 8-15-12-1, and input nodes include the oxygen volume per ton concentrate, the oxygen grade, the flux rate, the quantity of Cu, S, Fe, SiO2 and MgO in concentrate. In order to avoid local minimum terminations when the model is trained by back propagation (BP) algorithm, a new algorithm called GA-BP is presented by using genetic algorithm (GA) to determine the initial weights and threshold values. The results show that the model can avoid local minimum terminations and accelerate convergence, and has high prediction precision and good generalization performance. The model can be used to optimize the copper flash smelting process control.

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