Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining

2017 ◽  
Vol 9 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Vanita Jain
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Stream ◽  
Fraud Detection ◽  
Neural Network Classification ◽  
Using Data ◽  
Stream Analytics ◽  
Perspective Analysis

scholarly journals Fraud Detection of Credit Card using Data Mining Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 4410-4413
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Network Clustering ◽  
Transaction Data ◽  
Using Data ◽  
The Look

The handling of credit card for online and systematic purchase is booming and scam associated with it. An industry of fraud detection where cumulative rise can have huge perk for banks and client. Numerous stylish techniques like data mining, genetic programming, neural network etc. are used in identify fraudulent transaction. In online transaction, Data mining acquire indispensable aspect in discovery of credit card counterfeit. This paper uses gradient boosted trees, neural network, clustering technique and genetic algorithm and hidden markov model for achieving upshot of the fraudulent transaction. These all model are emerging in identifying various credit card fraudulent detection. The indispensable aims to expose the fraudulent transaction and to corroborate test data for further use. This paper presents the look over techniques and pinpoint the top fraud cases.

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

Provide a data mining algorithm for text classification based on text content emotions using neural network

2015 ◽  
Vol 16 (SE) ◽  
pp. 283-291
Author(s):  
Ebrahim Haydari ◽  
Amir Reza Estakhrian Haghighi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Simulation Software ◽  
Data Mining Algorithm ◽  
Positive Polarity ◽  
Promising Alternative ◽  
Neural Network Classification ◽  
Text Content ◽  
Regulatory Procedure

One of the newest areas of research is in data mining and text mining is automatic discovery of knowledge from semi-structured text. an important application of data mining in the classification texts.neural networks have emerged as a powerful tool in the classification of the content of texts and a promising alternative to conventional methods of classification. in this study, by combining and improving the regulatory procedure parameters weighted binary artificial neural network model, we will provide an algorithm to classify content. This algorithm content of the texts of the Persian texts will be based on the polarity of emotion suitable performance and high accuracy. The proposed algorithm is implemented using the simulation software MATLAB and evaluated collected over 1200 comments in Farsi in the real environment. The results above show that the proposed algorithm is a neural network classification accuracy of 96% negative polarity and positive polarity sentences based on document content.

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