Fraud Detection with Machine Learning - Model Comparison

2023 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Guilherme Ferreira Pelucio Salome ◽  
Jo�ão Luiz Chela ◽  
Jo�ão Carlos Pacheco Junior
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Model

Machine Learning Model Comparison for Leak Detection in Noisy Industrial Pipelines

2020 ◽  
Author(s):  
Dimitrios Kampelopoulos ◽  
George N. Papastavrou ◽  
George P. Kousiopoulos ◽  
Nikolaos Karagiorgos ◽  
Sotirios K. Goudos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Leak Detection ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis

2021 ◽  
Author(s):  
Pratyush Sharma ◽  
Souradeep Banerjee ◽  
Devyanshi Tiwari ◽  
Jagdish Chandra Patni
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann

In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.

A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

2021 ◽  
pp. 890-898
Author(s):  
Miguel Ángel Quiroz Martinez ◽  
Byron Alcívar Martínez Tayupanda ◽  
Sulay Stephanie Camatón Paguay ◽  
Luis Andy Briones Peñafiel
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Machine Learning Model ◽  
Selection Framework

scholarly journals Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matthew Behnke ◽  
Nathan Briner ◽  
Drake Cullen ◽  
Katelynn Schwerdtfeger ◽  
Jackson Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Model ◽  
Feature Engineering ◽  
Activity Detection ◽  
Malicious Activity ◽  
Machine Learning Model

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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