Maxout Neural Network for Big Data Medical Fraud Detection

2019 ◽  
Author(s):  
Gabriel Castaneda ◽  
Paul Morris ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fraud Detection

scholarly journals Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jian Liu ◽  
Xin Gu ◽  
Chao Shang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Fraud Detection ◽  
Quantitative Detection ◽  
Financial Fraud

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

2021 ◽  
Author(s):  
Feng He ◽  
Hongjiang Liu ◽  
Chunxue Liu ◽  
Guangjing Bao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Convolutional Neural Network ◽  
New Technologies ◽  
Hazard Identification ◽  
Identification Accuracy ◽  
Radar Image ◽  
Geologic Hazard ◽  
Continuous Increase ◽  
Identification Model

Abstract To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

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