DETECTION OF FAKE BANKNOTES WITH ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

被引：0

作者：

Celik, Enes ^{[1
]}

Kondiloglu, Adil ^{[2
]}

机构：

[1] Kirklareli Univ, Bilgisayar Programciligi, Kirklareli Merkez, Kirklareli, Turkey

[2] Turk Hava Kurumu Univ, Bilgisayar Muhendisligi, Ankara, Turkey

来源：

2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The document and banknote counterfeiting remove us as a usurpation of the rights of individuals and institutions. By the advancement of technology special paper, the ink usage in the original banknote, placing watermarks, micro text are challenged to fraud that also does not prevent the counterfeiting of widespread. The counterfeit banknotes detection and minimizing damage is one of the important elements. In this, decided and expert systems can be improved to estimate the counterfeit banknotes to using dates of the moneys. The data of banknotes are sorted before to this point, after comparative results are discussed of the testing. In this study, it is classified by the classification algorithms to using digitized available data of real and counterfeit banknotes images. Artificial Neural Networks and Support Vector Machines are used in the classification. Correctly identified is detected to rate of 74.6% in the test results which is tested by Artificial Neural Networks, correctly identified is detected to rate of 93.8% in the results of tests which is tested by Support Vector Machines.

引用

页码：1317 / 1320

页数：4

共 50 条

[1] Plant Disease Identification and Detection Using Support Vector Machines and Artificial Neural Networks
Iniyan, S.
Jebakumar, R.
Mangalraj, P.
Mohit, Mayank
Nanda, Aroop
[J]. ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, 2020, 1056 : 15 - 27
[2] Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection
Samanta, B
Al-Balushi, KR
Al-Araimi, SA
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2003, 16 (7-8) : 657 - 665
[3] Gear fault detection using artificial neural networks and support vector machines with genetic algorithms
Samanta, B
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (03) : 625 - 644
[4] Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms
Jack, LB
Nandi, AK
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2002, 16 (2-3) : 373 - 390
[5] Intrusion detection using neural networks and support vector machines
Mukkamala, S
Janoski, G
Sung, A
[J]. PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 1702 - 1707
[6] The Diagnosis of Hepatitis diseases by Support Vector Machines and Artificial Neural Networks
Rouhani, Modjtaba
Haghighi, Mehdi Motavalli
[J]. IACSIT-SC 2009: INTERNATIONAL ASSOCIATION OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY - SPRING CONFERENCE, 2009, : 456 - 458
[7] Wind direction forecasting with artificial neural networks and support vector machines
Tagliaferri, F.
Viola, I. M.
Flay, R. G. J.
[J]. OCEAN ENGINEERING, 2015, 97 : 65 - 73
[8] Neural Networks and Support Vector Machines
Sekhar, C. Chandra
[J]. 2011 THIRD INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC), 2011,
[9] Freight Car Roller Bearing Fault Detection Using Artificial Neural Networks and Support Vector Machines
Maraini, Daniel
Nataraj, C.
[J]. VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY, 2015, 23 : 663 - +
[10] Support Vector Machines and RBF neural networks for fault detection and diagnosis
Ribeiro, B
[J]. 8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 873 - 879

← 1 2 3 4 5 →