Browsing by Author "Rajora, Shantanu"

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A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance
(2018-01-01)
Rajora, Shantanu
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Li, Dong-Lin
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Jha, Chandan
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Bharill, Neha
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Patel, Om Prakash
This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.
Deep Sparse Representation Classifier for facial recognition and detection system
(2019-07-01)
Cheng, Eric-Juwei
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Chou, Kuang-Pen
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Rajora, Shantanu
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Jin, Bo-Hao
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Tanveer, M.
This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets. (C) 2019 Elsevier B.V. All rights reserved.