Cite this DOI

D. Sonje, P. A., & D. M. Sonje, P. D. (2021). BANK NOTE AUTHENTICATION AND CLASSIFICATION USING ADVANCED MACHINE ALGORITHMS. *Journal of Science & Technology*, *06*(01), 285–290. https://doi.org/10.46243/jst.2021.v6.i04.pp285-290

@article{dsonje2021bank,
  author    = {D. Sonje, Prof. Alpana and D. M. Sonje, Prof. Dr.},
  title     = {{BANK NOTE AUTHENTICATION AND CLASSIFICATION USING ADVANCED MACHINE ALGORITHMS}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  volume    = {06},
  number    = {01},
  pages     = {285--290},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i04.pp285-290},
  url       = {https://doi.org/10.46243/jst.2021.v6.i04.pp285-290},
  language  = {en},
  abstract  = {The currency coins or banknotes used by any country to perform economic activities in the global market should be always genuine. However, some of the miscreants in the society provoke forged notes into the bazaar which bears a resemblance to exactly the genuine note. It is stiff with human naked eye to notify the difference in between these two because they have a lot of analogous facial appearance. Hence, there is a call for of competent validation system which informs accurately whether the note in the transaction is authentic or not. This paper proposes machine learning based methodology to classify the fake and genuine notes. Accordingly, exhaustive testing has been performed using Multi Layer Perceptron Neural Network (MLPNN), Naïve Bayes and Random Forest (RF) Algorithms on the standard data set. The testing results show that RF and MLPNN algorithms gives comparable results in terms of accuracy and other performance measures as compared to any other algorithms. The complete training and testing of the dataset is performed in WEKA software.}
}

TY  - JOUR
TI  - BANK NOTE AUTHENTICATION AND CLASSIFICATION USING ADVANCED MACHINE ALGORITHMS
AU  - D. Sonje, Prof. Alpana
AU  - D. M. Sonje, Prof. Dr.
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021///
VL  - 06
IS  - 01
SP  - 285
EP  - 290
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - The currency coins or banknotes used by any country to perform economic activities in the global market should be always genuine. However, some of the miscreants in the society provoke forged notes into the bazaar which bears a resemblance to exactly the genuine note. It is stiff with human naked eye to notify the difference in between these two because they have a lot of analogous facial appearance. Hence, there is a call for of competent validation system which informs accurately whether the note in the transaction is authentic or not. This paper proposes machine learning based methodology to classify the fake and genuine notes. Accordingly, exhaustive testing has been performed using Multi Layer Perceptron Neural Network (MLPNN), Naïve Bayes and Random Forest (RF) Algorithms on the standard data set. The testing results show that RF and MLPNN algorithms gives comparable results in terms of accuracy and other performance measures as compared to any other algorithms. The complete training and testing of the dataset is performed in WEKA software.
DO  - 10.46243/jst.2021.v6.i04.pp285-290
UR  - https://doi.org/10.46243/jst.2021.v6.i04.pp285-290
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i04.pp285-290",
    "title": "BANK NOTE AUTHENTICATION AND CLASSIFICATION USING ADVANCED MACHINE ALGORITHMS",
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    "volume": "06",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "The currency coins or banknotes used by any country to perform economic activities in the global market should be always genuine. However, some of the miscreants in the society provoke forged notes into the bazaar which bears a resemblance to exactly the genuine note. It is stiff with human naked eye to notify the difference in between these two because they have a lot of analogous facial appearance. Hence, there is a call for of competent validation system which informs accurately whether the note in the transaction is authentic or not. This paper proposes machine learning based methodology to classify the fake and genuine notes. Accordingly, exhaustive testing has been performed using Multi Layer Perceptron Neural Network (MLPNN), Naïve Bayes and Random Forest (RF) Algorithms on the standard data set. The testing results show that RF and MLPNN algorithms gives comparable results in terms of accuracy and other performance measures as compared to any other algorithms. The complete training and testing of the dataset is performed in WEKA software.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i04.pp285-290 gives all four in one JSON answer.