9786200095398 - Ethiopian Banknote Denomination Classification & Fake Detection System: An optimal feature extraction and classification technique de Alene, Asfaw (8 resultados)

Condición: New.

Condición: New.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Forgery is one of the critical problems affecting cash transactions. Forged banknotes are becoming serious threats hampering the smooth transactions in Ethiopia. Hence, the availability of such fake notes in the market needs the automation of the money transaction system. The banking industries are unable to fully utilize self-serving devices including ATMs intensively. Nevertheless, banks have not yet utilized a reliable recognition system to identify forged banknotes. This calls for the development of a better authenticity verification system. In this study, we have examined the color momentum, SIFT, GLCM, combination of SIFT, color, and GLCM, and convolutional neural network as a feature extraction technique and support vector machine, K- nearest neighbor classifier, and feed-forward artificial neural network as a classifier to design Ethiopian banknote recognition system. In order to minimize the effect of noisy data, we have employed an intensive image preprocessing tasks, like image histogram equalization and adaptive median filter-based image denoising. 124 pp. Englisch.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Forgery is one of the critical problems affecting cash transactions. Forged banknotes are becoming serious threats hampering the smooth transactions in Ethiopia. Hence, the availability of such fake notes in the market needs the automation of the money transaction system. The banking industries are unable to fully utilize self-serving devices including ATMs intensively. Nevertheless, banks have not yet utilized a reliable recognition system to identify forged banknotes. This calls for the development of a better authenticity verification system. In this study, we have examined the color momentum, SIFT, GLCM, combination of SIFT, color, and GLCM, and convolutional neural network as a feature extraction technique and support vector machine, K- nearest neighbor classifier, and feed-forward artificial neural network as a classifier to design Ethiopian banknote recognition system. In order to minimize the effect of noisy data, we have employed an intensive image preprocessing tasks, like image histogram equalization and adaptive median filter-based image denoising.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Forgery is one of the critical problems affecting cash transactions. Forged banknotes are becoming serious threats hampering the smooth transactions in Ethiopia. Hence, the availability of such fake notes in the market needs the automation of the money transaction system. The banking industries are unable to fully utilize self-serving devices including ATMs intensively. Nevertheless, banks have not yet utilized a reliable recognition system to identify forged banknotes. This calls for the development of a better authenticity verification system. In this study, we have examined the color momentum, SIFT, GLCM, combination of SIFT, color, and GLCM, and convolutional neural network as a feature extraction technique and support vector machine, K- nearest neighbor classifier, and feed-forward artificial neural network as a classifier to design Ethiopian banknote recognition system. In order to minimize the effect of noisy data, we have employed an intensive image preprocessing tasks, like image histogram equalization and adaptive median filter-based image denoising.

Taschenbuch. Condición: Neu. Ethiopian Banknote Denomination Classification & Fake Detection System | An optimal feature extraction and classification technique | Asfaw Alene | Taschenbuch | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200095398 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.