Bank note authentication using decision tree rules and machine learning techniques

Kumar*, C.Sathish; Dudyala, Anil Kumar

doi:10.1109/icacea.2015.7164721

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…For Wu et al [14], a framework based on self-training semi-supervised classification (SSC) that integrates the algorithms of SVM, k-NN, and CART produces 94.50%, 85.70%, and 67% accuracy respectively. The MLP and probabilistic neural network (PNN) accounted for the best accuracy rates at 98.83% and 98.60% for the work by Kumar and Dudyal [16]. Also, a GEP-Ensemble method gave banknote detection accuracy of 96.82% [19].…”

Section: Simulation Resultsmentioning

confidence: 98%

“…For fair performance comparison, the results that are reported by Wu et al [14], Kumar and Dudyal [16], and Jȩdrzejowicz and Jȩdrzejowicz [19] are selected. In their works, the same banknote authentication dataset has been used with 10-fold cross-validation as employed in our work.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Classification of the feature vectors is performed by the feed-forward neural network, and accuracy of 98.6% was generated in comparison to pattern recognition feed-forward neural network (PRFNN), cascade forward neural network (CNN), and AdaBoost. Kumar and Dudyala [16] carried out an extensive experimental investigation on banknote authentication using several machine learning algorithms. These learning algorithms include a probabilistic neural network (PNN), MLP, RBF, decision tree, and naïve Bayes.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.

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Section: Simulation Resultsmentioning

confidence: 98%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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“…Among those techniques were Decision Tree (DT), deep learning, Artificial Neural Networks (ANN), which were used to detect frauds in internet banking transactions. Another application focused on the counterfeit of Banknotes using DT, ANN and Support Vector Machines (SVM) techniques [4,5]. The rules induced by the DT models were accurate enough to help in distinguishing between original and counterfeit Banknotes.…”

Section: Introductionmentioning

confidence: 99%

Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

Meshref

2020

International Journal of Circuits, Systems and Signal Processing

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The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.

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“…It has the major role in financial activities of every country [1]. The study in [1] evaluates different machine learning algorithms and concludes that Decision-Tree and MLP technique is best to classify a bank note. In [2], the features of the banknote are extracted using Fast Wavelet Transforms.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Banknote Authentication System using Machine Learning Techniques

Shahani¹,

Jagiasi²,

Priya³

2018

IJCA

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Banknotes are one of the most important assets of a country. Some miscreants introduce fake notes which bear a resemblance to original note to create discrepancies of the money in the financial market. It is difficult for humans to tell true and fake banknotes apart especially because they have a lot of similar features. Fake notes are created with precision, hence there is need for an efficient algorithm which accurately predicts whether a banknote is genuine or not. This paper proposes machine learning techniques to evaluate authentication of banknotes. Supervised learning algorithms such as Back propagation Neural Network (BPN) and Support Vector Machine (SVM) are used for differentiating genuine banknotes from fake ones. The study also shows the comparison of these algorithms in classification of banknotes.

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Bank note authentication using decision tree rules and machine learning techniques

Cited by 20 publications

References 12 publications

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

Analysis of Banknote Authentication System using Machine Learning Techniques

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