Cluster Analysis and Artificial Neural Networks: A Case Study in Credit Card Fraud Detection

2015 ◽  
Author(s):  
Emanuel Mineda Carneiro ◽  
Luiz Alberto Vieira Dias ◽  
Adilson Marques Da Cunha ◽  
Lineu Fernando Stege Mialaret
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Artificial Neural

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.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Predicting the concentration of sulfate (SO42-) in drinking water using artificial neural networks: a case study: Médéa-Algeria

2021 ◽  
Vol 217 ◽  
pp. 181-194
Author(s):  
Hichem Tahraoui ◽  
Abd-Elmouneïm Belhadj ◽  
Adhya-eddine Hamitouche ◽  
Mounir Bouhedda ◽  
Abdeltif Amrane
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Artificial Neural
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