scholarly journals SUPERVISED MACHINE LEARNING ALGORITHMS FOR DETECTING CREDIT CARD FRAUD

2021 ◽  
pp. 131-134
Author(s):  
G.Bhargav Chowdari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Learning Methods ◽  
Credit Card Fraud

One of the most serious ethical challenges in the credit card industry is fraud. Our paper’s major goal is to identify credit card theft and offer a reasonable solution to the problem. Credit card fraud has cost customers and banks billions of dollars around the world. Fraudsters are constantly attempting to come up with new ways and tricks to commit fraud, despite the fact that there are several measures in place to prevent it. Fraud detection is extremely important in the banking and finance industries. For detection purposes, we will use an artificial neural network. As a result, in order to prevent it, we will develop a system that will not only detect fraud, but will also detect it before it occurs. In order to detect new scams, our system will learn from previous frauds. Mining algorithms were used to detect fraud, but they failed miserably. We use machine learning methods to detect fraud in credit card transactions in our paper. The research employs supervised learning methods that are applied to a kaggle dataset that is severely skewed and imbalanced. We used robust scalar to balance the set, resulting in 51 percent non-fraud cases and 49 percent fraud ones. Logistic regression, random forest, decision tree, and KNN have all been implemented, with additional learning curves displaying which algorithm performs best. Accuracy, specificity, precision, and sensitivity are the evaluation criteria, and a comparative chart is created to show the comparative analysis of various supervised learning algorithms. KEYWORDS: KNN,Neural network,Logistic regression,Random forest,Decision tree

scholarly journals Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Luana Ibiapina Cordeiro Calíope Pinheiro ◽  
Maria Lúcia Duarte Pereira ◽  
Marcial Porto Fernandez ◽  
Francisco Mardônio Vieira Filho ◽  
Wilson Jorge Correia Pinto de Abreu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Hiv Aids

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

scholarly journals Comparative analysis of machine learning algorithms in water extraction

2021 ◽  
Vol 2076 (1) ◽  
pp. 012045
Author(s):  
Aimin Li ◽  
Meng Fan ◽  
Guangduo Qin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Water Body ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector

Abstract There are many traditional methods available for water body extraction based on remote sensing images, such as normalised difference water index (NDWI), modified NDWI (MNDWI), and the multi-band spectrum method, but the accuracy of these methods is limited. In recent years, machine learning algorithms have developed rapidly and been applied widely. Using Landsat-8 images, models such as decision tree, logistic regression, a random forest, neural network, support vector method (SVM), and Xgboost were adopted in the present research within machine learning algorithms. Based on this, through cross validation and a grid search method, parameters were determined for each model.Moreover, the merits and demerits of several models in water body extraction were discussed and a comparative analysis was performed with three methods for determining thresholds in the traditional NDWI. The results show that the neural network has excellent performances and is a stable model, followed by the SVM and the logistic regression algorithm. Furthermore, the ensemble algorithms including the random forest and Xgboost were affected by sample distribution and the model of the decision tree returned the poorest performance.

scholarly journals Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021 ◽  
pp. 9-22
Author(s):  
Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Fraud Detection ◽  
Loan Default ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Default Prediction ◽  
Automated Process

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

scholarly journals Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 1477-1483
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Naive Bayes ◽  
Gradient Boosting ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Importance

With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.

scholarly journals Statistical Analysis for Selective Identifications of VOCs by Using Surface Functionalized MoS2 Based Sensor Array

2021 ◽  
Vol 5 (1) ◽  
pp. 35
Author(s):  
Uttam Narendra Thakur ◽  
Radha Bhardwaj ◽  
Arnab Hazra
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Sensor Array ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Breath

Disease diagnosis through breath analysis has attracted significant attention in recent years due to its noninvasive nature, rapid testing ability, and applicability for patients of all ages. More than 1000 volatile organic components (VOCs) exist in human breath, but only selected VOCs are associated with specific diseases. Selective identification of those disease marker VOCs using an array of multiple sensors are highly desirable in the current scenario. The use of efficient sensors and the use of suitable classification algorithms is essential for the selective and reliable detection of those disease markers in complex breath. In the current study, we fabricated a noble metal (Au, Pd and Pt) nanoparticle-functionalized MoS2 (Chalcogenides, Sigma Aldrich, St. Louis, MO, USA)-based sensor array for the selective identification of different VOCs. Four sensors, i.e., pure MoS2, Au/MoS2, Pd/MoS2, and Pt/MoS2 were tested under exposure to different VOCs, such as acetone, benzene, ethanol, xylene, 2-propenol, methanol and toluene, at 50 °C. Initially, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to discriminate those seven VOCs. As compared to the PCA, LDA was able to discriminate well between the seven VOCs. Four different machine learning algorithms such as k-nearest neighbors (kNN), decision tree, random forest, and multinomial logistic regression were used to further identify those VOCs. The classification accuracy of those seven VOCs using KNN, decision tree, random forest, and multinomial logistic regression was 97.14%, 92.43%, 84.1%, and 98.97%, respectively. These results authenticated that multinomial logistic regression performed best between the four machine learning algorithms to discriminate and differentiate the multiple VOCs that generally exist in human breath.

scholarly journals Comparison of Machine Learning With Logistic Regression for Prediction of Chronic Kidney Disease in the Thai Adult Population

2021 ◽  
Vol 44 (4) ◽  
pp. 1-12
Author(s):  
Ratchainant Thammasudjarit ◽  
Punnathorn Ingsathit ◽  
Sigit Ari Saputro ◽  
Atiporn Ingsathit ◽  
Ammarin Thakkinstian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Models ◽  
The Neural Network

Background: Chronic kidney disease (CKD) takes huge amounts of resources for treatments. Early detection of patients by risk prediction model should be useful in identifying risk patients and providing early treatments. Objective: To compare the performance of traditional logistic regression with machine learning (ML) in predicting the risk of CKD in Thai population. Methods: This study used Thai Screening and Early Evaluation of Kidney Disease (SEEK) data. Seventeen features were firstly considered in constructing prediction models using logistic regression and 4 MLs (Random Forest, Naïve Bayes, Decision Tree, and Neural Network). Data were split into train and test data with a ratio of 70:30. Performances of the model were assessed by estimating recall, C statistics, accuracy, F1, and precision. Results: Seven out of 17 features were included in the prediction models. A logistic regression model could well discriminate CKD from non-CKD patients with the C statistics of 0.79 and 0.78 in the train and test data. The Neural Network performed best among ML followed by a Random Forest, Naïve Bayes, and a Decision Tree with the corresponding C statistics of 0.82, 0.80, 0.78, and 0.77 in training data set. Performance of these corresponding models in testing data decreased about 5%, 3%, 1%, and 2% relative to the logistic model by 2%. Conclusions: Risk prediction model of CKD constructed by the logit equation may yield better discrimination and lower tendency to get overfitting relative to ML models including the Neural Network and Random Forest.  

scholarly journals Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Vandana Thakkar ◽  
Upasana Mukherjee ◽  
Shawni Dutta
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Fraud Detection ◽  
Loan Default ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Default Prediction ◽  
Automated Process

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

scholarly journals Development of a weed detection system using machine learning and neural network algorithms

2021 ◽  
Vol 6 (2 (114)) ◽  
Author(s):  
Baydaulet Urmashev ◽  
Zholdas Buribayev ◽  
Zhazira Amirgaliyeva ◽  
Aisulu Ataniyazova ◽  
Mukhtar Zhassuzak ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Detection System ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Plant Diseases ◽  
Weed Detection ◽  
K Nearest Neighbors

The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 neural network, is proposed. After analyzing the geographical areas of the country, from the images of the collected weeds, a proprietary database with more than 1000 images for each class was formed. A brief review of the researchers' scientific papers describing the methods they developed for identifying, classifying and discriminating weeds based on machine learning algorithms, convolutional neural networks and deep learning algorithms is given. As a result of the research, a weed detection system based on the YOLOv5 architecture was developed and quality estimates of the above algorithms were obtained. According to the results of the assessment, the accuracy of weed detection by the K-Nearest Neighbors, Random Forest and Decision Tree classifiers was 83.3 %, 87.5 %, and 80 %. Due to the fact that the images of weeds of each species differ in resolution and level of illumination, the results of the neural network have corresponding indicators in the intervals of 0.82–0.92 for each class. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

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