scholarly journals Credit Card Fraud Analysis using Robust Space Invariant Artificial Neural Networks (RSIANN)

2019 ◽  
Vol 8 (2) ◽  
pp. 6413-6417
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Credit Card Fraud ◽  
Tuning Parameters ◽  
Deep Convolution Neural Network ◽  
Artificial Neural

One of the impact factor for any organizations or banks revenue and service quality is credit card fraud activities. Hence, need of efficient approach for detect early potential fraud and/or prevent them. In this paper, we considered pre-processing and used deep convolution neural network called as Space Invariant Artificial Neural Networks for classifying fraudsters. Available Credit card fraud dataset may not have sufficient information hence need pre-processing. The proposed approach has pre-processing phrase to make as robust. This approach used leverage layers and suitable tuning parameters for getting good classification accuracy. In neural network applications, choosing of tuning parameters and model selection has great role in solving the problems. We have done careful analysis and selected leverage layers and corresponding parameter values. The proposed architecture tested with all possible tuning parameters to evaluate the performance on pre-processed credit card fraud records. We found the proposed robust SIANN (RSIANN) is outperformed other state-of-art machine learning (ML) algorithms (Support vector machine (SVM), random forest (RF), Navie bayes and deep convolution neural network (DCNN) in terms of accuracy (85%). Thus, this model analyses the transaction and decide it fraud or not.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

FINDING PROBLEM SPECIFIC SHANNON INFORMATION IN HIGH DIMENSIONAL INPUT SPACE FOR ARTIFICIAL NEURAL NETWORKS

2013 ◽  
Vol 07 (02) ◽  
pp. 147-155
Author(s):  
JOSEPH R. BARR ◽  
W. KURT DOBSON
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
Sparse Data ◽  
Shannon Information ◽  
Input Space ◽  
Artificial Neural

Artificial neural networks, due to their ability to find the underlying model even in complex highly nonlinear and highly coupled problems, have found significant use as prediction engines in many domains. However, in problems where the input space is of high dimensionality, there is the unsolved problem of reducing dimensionality in some optimal way such that Shannon information important to the prediction is preserved. The important Shannon information may be a subset of total information with an unknown partition, unknown coupling and linear or nonlinear in nature. Solving this problem is an important step in classes of machine learning problems and many data mining applications. This paper describes a semi-automatic algorithm that was developed over a 5-year period while solving problems with increasing dimensionality and difficulty in (a) flow prediction for a magnetically levitated artificial heart (13 dimensions), (b) simultaneous chemical identification/concentration in gas chromatography (22 detection dimensions with wavelet compressed time series of 180,000 points), and finally in (c) financial analytics portfolio prediction in credit card and sub-prime debt problems (80 to 300 dimensions of sparse data with a portfolio value of approximately US$300,000,000.00). The algorithm develops a map of input space combinations and their importance to the prediction. This information is used directly to construct the optimal neural network topology for a given error performance. Importantly, the algorithm also produces information that shows whether the space between input nodes is linear or nonlinear; an important parameter in determining the number of training points required in the reduced dimensionality of the training set. Software was developed in the MatLAB environment using the Artificial Neural Network Toolbox, Parallel and Distributed Computing toolboxes, and runs on Windows or Linux based supercomputers. Trained neural networks can be compiled and linked to server applications and run on normal servers or clusters for transaction or web based processing. In this paper, application of the algorithm to two separate financial analytics prediction problems with large dimensionality and sparse data sets are shown. The algorithm is an important development in machine learning for an important class of problems in prediction, clustering, image analysis, and data mining. In the first example application for subprime debt portfolio analysis, performance of the neural network provided a 98.4% prediction rate, compared to 33% rate using traditional linear methods. In the second example application regarding credit card debt, performance of the algorithm provided a 95% accurate prediction (in terms of match rate), and is 10% better than other methods we have compared against, primarily logistic regression.

scholarly journals Artificial Neural Networks and the Mass Appraisal of Real Estate

2018 ◽  
Vol 14 (03) ◽  
pp. 180 ◽  
Author(s):  
Gang Zhou ◽  
Yicheng Ji ◽  
Xiding Chen ◽  
Fangfang Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Estate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
The Real ◽  
The Real Estate ◽  
Artificial Neural

<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>

Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models

2017 ◽  
Vol 28 (5) ◽  
pp. 893-903 ◽  
Author(s):  
S. Sankar Ganesh ◽  
Pachaiyappan Arulmozhivarman ◽  
Rao Tatavarti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Gradient Descent ◽  
Air Quality Index ◽  
Support Vector ◽  
Artificial Neural

Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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