scholarly journals Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network

2021 ◽  
Vol 2 (1) ◽  
pp. 15-24
Author(s):  
Arnold Adimabua Ojugo ◽  
Obinna Nwankwo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Spectral Clustering ◽  
Credit Card ◽  
Credit Cards ◽  
Spectral Cluster ◽  
Cluster Solution ◽  
Large Magnitude ◽  
Model Accuracy ◽  
Deep Learning Neural Network

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.

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 Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Eun Ji Choi ◽  
Yongseok Yoo ◽  
Bo Rang Park ◽  
Young Jae Choi ◽  
Jin Woo Moon
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Thermal Environment ◽  
Classification Model ◽  
Indoor Environments ◽  
Model Accuracy ◽  
Indoor Space ◽  
Deep Learning Neural Network ◽  
Coordinate Data ◽  
Sedentary Activities

This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: home and office. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and office, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant.

scholarly journals Credit Card Fraud Detection using PSO Optimized Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 360-363
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Detection System ◽  
Confusion Matrix ◽  
Credit Cards ◽  
Fraud Detection ◽  
Timely Manner ◽  
Credit Card Fraud ◽  
Computing Performance ◽  
Selection Of

Credit card fraud is one of the most important problems that financial institutions are currently facing. Although the technology has allowed to increase the security in the credit cards with the use of PIN keys, the introduction of chips in the cards, the use of additional keys such as tokens and improvements in the regulation of its use is also a necessity for banks, to act preventively against this crime. To act preventively, it is necessary to monitor in real time the operations that are carried out and have the ability to react in a timely manner against any doubtful operation that is performed. This paper presents an implementation of automatic credit card fraud detection system using Particle Swarm Optimized Neural Network classifier on Kaggle dataset. The selection of proper attributes for reducing the training overhead and claiming higher accuracy for the fraud detection using soft computing. Performance evaluation is achieved using confusion matrix plot with accuracy, sensitivity and precision values.

Consumers‟ Acceptance of „Halal‟ Credit Card Services: An Empirical Analysis

2015 ◽  
Vol 3 (1) ◽  
pp. 51 ◽  
Author(s):  
Zaimy Johana Johan ◽  
Lennora Putit
Keyword(s):  
Perceived Control ◽  
Credit Card ◽  
Credit Cards ◽  
Product Knowledge ◽  
Key Factors ◽  
Developing Nation ◽  
Business Transactions ◽  
Factors Influencing ◽  
Financial Credit ◽  
Survey Questionnaires

Many past researches have been carried out in an attempt to continuously understand individuals‟ consumption behaviour. This study was conducted to investigate key factors influencing consumers‟ potential acceptance of halal (or permissible) financial credit card services. Specifically, it anticipated the influence of attitude, social influences and perceived control on consumers‟ behavioural intention to accept such services. In addition, factors such as religiosity and product knowledge were also postulated to affect consumers‟ attitude towards the act of using halal credit cards for any retail or business transactions. Using non-probability sampling approach, a total of 500 survey questionnaires was distributed to targeted respondents in a developing nation but only 220 usable feedbacks were received for subsequent data analysis. Regression results revealed that religiosity and product knowledge significantly influence consumers‟ attitude toward using halal credit card services.  Attitude in turn, subsequently has a significant impact on consumers‟ intention to accept halal financial credit card services. Several theoretical and managerial contributions were observed in this study.   

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