Credit Card Fraudulent Detection Using Machine Learning Algorithm

The fraudulent transactions that occur in credit cards end in huge financial crisis. Since the web transactions has grown rapidly, the results of digitalized process hold an enormous sharing of such transactions. So, the financial institutions including banks offers much value to the applications of fraud detection. The Fraudulent transactions can occur in different ways and in various categories. Our work mainly focuses on detecting the illegal transactions effectively. Those transactions are addressed by employing some machine learning models and therefore the efficient method is chosen through an evaluation using some performance metrics. This work also helps to select an optimal algorithm with reference to the machine learning algorithms. We illustrate the evaluation with suitable performance measures. We use those performance metrics to evaluate the algorithm chosen. Within the existing system the algorithms provide less efficiency and makes the training model slow. Hence within the proposed system we used Multilayer Perceptron and Random Forest to supply high efficiency. From these algorithms efficient one is chosen through evaluation.

DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

InterConf ◽

10.51582/interconf.19-20.08.2021.037 ◽

2021 ◽

pp. 393-403

Author(s):

Olexander Shmatko ◽

Volodimir Fedorchenko ◽

Dmytro Prochukhan

Keyword(s):

Machine Learning ◽

Financial Services ◽

Credit Card ◽

Banking Sector ◽

Learning Algorithm ◽

Learning Algorithms ◽

Credit Cards ◽

Internet Banking ◽

Credit Card Fraud

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

10.4018/978-1-7998-7685-4.ch002 ◽

2022 ◽

pp. 34-46

Author(s):

Amtul Waheed ◽

Jana Shafi ◽

Saritha V.

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Smart Cities ◽

Learning Algorithms ◽

High Capacity ◽

Transportation Systems ◽

Smart Transportation ◽

Data Propagation ◽

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

Assessment of Machine Learning Algorithms for Network Intrusion Detection

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d8689.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1667-1671

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Performance Metrics ◽

Detection System ◽

Network Intrusion Detection ◽

Learning Models ◽

Network Intrusion ◽

Tree Classifier ◽

A Network Intrusion Detection System (NIDS) is a framework to identify network interruptions as well as abuse by checking network traffic movement and classifying it as either typical or strange. Numerous Intrusion Detection Systems have been implemented using simulated datasets like KDD’99 intrusion dataset but none of them uses a real time dataset. The proposed work performs and assesses tests to overview distinctive machine learning models reliant on KDD’99 intrusion dataset and an ongoing created dataset. The machine learning models achieved to compute required performance metrics so as to assess the chosen classifiers. The emphasis was on the accuracy metric so as to improve the recognition pace of the interruption identification framework. The actualized calculations showed that the decision tree classifier accomplished the most noteworthy estimation of accuracy while the logistic regression classifier has accomplished the least estimation of exactness for both of the datasets utilized.

Detection of Online Fake News Using Blending Ensemble Learning

Scientific Programming ◽

10.1155/2021/3434458 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Arvin Hansrajh ◽

Timothy T. Adeliyi ◽

Jeanette Wing

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Fake News ◽

Learning Models ◽

Linear Discriminant ◽

Proposed Model ◽

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efﬁcacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

Zambia ICT Journal ◽

10.33260/zictjournal.v2i2.59 ◽

2018 ◽

Vol 2 (2) ◽

pp. 16-27

Author(s):

Mumbi Chishimba ◽

Douglas Kunda

Keyword(s):

Machine Learning ◽

Resource Allocation ◽

Optimal Allocation ◽

Learning Algorithm ◽

Optimal Algorithm ◽

Sequential Assignment ◽

Actual Process ◽

Similarity Comparison ◽

Area Of Interest

Resource allocation has always been an area of interest and the era of computing. This is especially true in areas of computing such as machine learning which provides many solutions to the problem of resource allocation. The issue addressed in this paper is the issue of optimal allocation of applicants (teachers) to positions in schools where their area of specialization will be better applied. We develop an algorithm that is able to allocate applicants to schools based on the applicant qualifications and the school’s needs. We use the principles of resource allocation and machine learning in order to create an application to allocate applicants to schools where their qualifications are most suited. Methods used include classification techniques in machine learning, regression and similarity comparison. For the identification is subjects an applicant in proficient in, various machine learning algorithms are tested to determine which machine learning algorithm will be best. The actual process of identifying which applicant qualifies for a school position is also tested against sequential assignment if applicants to schools. The results of this were that the algorithm based assignment of applicants to schools produced more accurate assignment of applicants to schools than the sequential assignment of applicants. The aim of this algorithm is to provide a solution to that automatically identifies the needs (subjects) of a school, determine which needs are to have a higher priority, identify the qualifications of the applicants and assign the applicants to the school according to the school’s needs and the applicant’s qualifications.

Software Engineering for Machine Learning in Health Informatics

10.21203/rs.2.17747/v1 ◽

2019 ◽

Author(s):

Mohammed Moreb ◽

Oguz Ata

Keyword(s):

Machine Learning ◽

Software Engineering ◽

Health Informatics ◽

Learning Algorithm ◽

Learning Algorithms ◽

Prototype System ◽

Machine Learning Algorithm ◽

Learning Models ◽

Abstract Background We propose a novel framework for health Informatics: framework and methodology of Software Engineering for machine learning in Health Informatics (SEMLHI). This framework shed light on its features, that allow users to study and analyze the requirements, determine the function of objects related to the system and determine the machine learning algorithms that will be used for the dataset.Methods Based on original data that collected from the hospital in Palestine government in the past three years, first the data validated and all outlier removed, analyzed using develop framework in order to compare ML provide patients with real-time. Our proposed module comparison with three Systems Engineering Methods Vee, agile and SEMLHI. The result used by implement prototype system, which require machine learning algorithm, after development phase, questionnaire deliver to developer to indicate the result using three methodology. SEMLHI framework, is composed into four components: software, machine learning model, machine learning algorithms, and health informatics data, Machine learning Algorithm component used five algorithms use to evaluate the accuracy for machine learning models on component.Results we compare our approach with the previously published systems in terms of performance to evaluate the accuracy for machine learning models, the results of accuracy with different algorithms applied for 750 case, linear SVG have about 0.57 value compared with KNeighbors classifier, logistic regression, multinomial NB, random forest classifier. This research investigates the interaction between SE, and ML within the context of health informatics, our proposed framework define the methodology for developers to analyzing and developing software for the health informatic model, and create a space, in which software engineering, and ML experts could work on the ML model lifecycle, on the disease level and the subtype level.Conclusions This article is an ongoing effort towards defining and translating an existing research pipeline into four integrated modules, as framework system using the dataset from healthcare to reduce cost estimation by using a new suggested methodology. The framework is available as open source software, licensed under GNU General Public License Version 3 to encourage others to contribute to the future development of the SEMLHI framework.

Machine learning approaches in predicting ambulatory same day discharge patients after total hip arthroplasty

Regional Anesthesia and Pain Medicine ◽

10.1136/rapm-2021-102715 ◽

2021 ◽

pp. rapm-2021-102715

Author(s):

Haoyan Zhong ◽

Jashvant Poeran ◽

Alex Gu ◽

Lauren A Wilson ◽

Alejandro Gonzalez Della Valle ◽

...

Keyword(s):

Machine Learning ◽

Total Hip Arthroplasty ◽

Length Of Stay ◽

Hip Arthroplasty ◽

Learning Algorithm ◽

Area Under The Curve ◽

Machine Learning Algorithm ◽

Total Hip ◽

BackgroundWith continuing financial and regulatory pressures, practice of ambulatory total hip arthroplasty is increasing. However, studies focusing on selection of optimal candidates are burdened by limitations related to traditional statistical approaches. Hereby we aimed to apply machine learning algorithm to identify characteristics associated with optimal candidates.MethodsThis retrospective cohort study included elective total hip arthroplasty (n=63 859) recorded in National Surgical Quality Improvement Program dataset from 2017 to 2018. The main outcome was length of stay. A total of 40 candidate variables were considered. We applied machine learning algorithms (multivariable logistic regression, artificial neural networks, and random forest models) to predict length of stay=0 day. Models’ accuracies and area under the curve were calculated.ResultsApplying machine learning models to compare length of stay=0 day to length of stay=1–3 days cases, we found area under the curve of 0.715, 0.762, and 0.804, accuracy of 0.65, 0.73, and 0.81 for logistic regression, artificial neural networks, and random forest model, respectively. Regarding the most important predictive features, anesthesia type, body mass index, age, ethnicity, white blood cell count, sodium level, and alkaline phosphatase were highlighted in machine learning models.ConclusionsMachine learning algorithm exhibited acceptable model quality and accuracy. Machine learning algorithms highlighted the as yet unrecognized impact of laboratory testing on future patient ambulatory pathway assignment.

Applying and comparing multiple machine learning techniques to detect fraudulent credit card transactions

Dynamics of Complex Systems - XXI century ◽

10.18127/j19997493-202102-01 ◽

2021 ◽

Author(s):

D.V. Berezkin ◽

Shi Runfang ◽

Li Tengjiao

Keyword(s):

Machine Learning ◽

Credit Card ◽

Optimal Algorithm ◽

Negative Impact ◽

Classification Problem ◽

Training Sample ◽

Machine Learning Techniques ◽

Economic Losses ◽

The Difference

This experiment compared the performance of four machine learning algorithms in detecting bank card fraud. At the same time, the strong imbalance of the classes in the training sample was taken into account, as well as the difference in transaction amounts, and the ability of different machine learning methods to recognize fraudulent behavior was assessed taking into account these features. It has been found that a method that works well with indicators for assessing a classification is not necessarily the best in terms of assessing the magnitude of economic losses. Logistic regression is a good proof of this. The results of this work show that the problem of detecting fraud with bank cards cannot be regarded as a simple classification problem. AUC data is not the most appropriate metric for fraud detection tasks. The final choice of the model depends on the needs of the bank, that is, it is necessary to take into account which of the two types of errors (FN, FP) will lead to large economic losses for the bank. If the bank believes that the loss caused by identifying fraudulent transactions as regular transactions is the main one, it should choose the algorithm with the lowest FN value, which in this experiment is Adaboost. If the bank believes that the negative impact of identifying regular transactions as fraudulent is also very important, it should choose an algorithm with relatively small FN and FP data. In this experiment, the overall performance of the random forest is better. Further, by evaluating the economic losses caused by false positives (identifying an ordinary transaction as fraudulent), a quantitative analysis of the economic losses caused by each algorithm can be used to select the optimal algorithm model.

Credit Card Fraud Detection using Machine Learning and Deployment of Model in Public Cloud as a Web Service

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3800.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 548-552

Keyword(s):

Machine Learning ◽

Web Service ◽

Credit Card ◽

Detection System ◽

Credit Cards ◽

Fraud Detection ◽

Credit Card Fraud ◽

Detection Model ◽

The World

In recent times, usage of credit cards has increased exponentially which has given way to an increase in the number of cybercrimes related to transactions using credit cards. In this paper, the aim is to reduce the fraudulent credit card transactions happening around the world. Latest technologies like machine learning algorithms, cloud computing and web service implementation has been used in this paper. The model uses Local outlier factor algorithm and Isolation forest algorithm to develop the credit card fraud detection model using unsupervised learning techniques. The model has been implemented as a Web service to make the solution integratable with other applications and clients across the world. A third party prototype application is developed and integrated to the Fraud Detection Model using Web Services. The complete Fraud Detection System is deployed on the cloud. The Fraud Detection Model shows exceptionally high accuracy when compared to other models already existing.

Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)

TH Wildau Engineering and Natural Sciences Proceedings ◽

10.52825/thwildauensp.v1i.25 ◽

2021 ◽

Vol 1 ◽

Author(s):

Agbassou Guenoupkati ◽

Adekunlé Akim Salami ◽

Mawugno Koffi Kodjo ◽

Kossi Napo

Keyword(s):

Machine Learning ◽

Time Series ◽

Linear Regression ◽

Performance Metrics ◽

Learning Algorithms ◽

Support Vector ◽

Learning Models ◽

Short Term ◽

Time series forecasting in the energy sector is important to power utilities for decision making to ensure the sustainability and quality of electricity supply, and the stability of the power grid. Unfortunately, the presence of certain exogenous factors such as weather conditions, electricity price complicate the task using linear regression models that are becoming unsuitable. The search for a robust predictor would be an invaluable asset for electricity companies. To overcome this difficulty, Artificial Intelligence differs from these prediction methods through the Machine Learning algorithms which have been performing over the last decades in predicting time series on several levels. This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the electricity production of Benin Electricity Community. In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. Overall, the results show that the Machine Learning models outperform the linear regression methods. Consequently, Machine Learning methods offer a perspective for short-term electric power generation forecasting of Benin Electricity Community sources.