Omar ezz (8 resultados)
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 64,20
Envío por EUR 3,45Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
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Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 36,35
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Gender Detection | Classification Face male/female using multi databases | Ezz Omar (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204182810 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de…| Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 39,90
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Today's machine learning is widely used in diverse areas. For example, fraudulent systems, recommended systems, exploited prediction, and many other applications. One of these applications, is being exploited in this search. This pa…per presents an approach to detecting a person's gender through the front face image by using extraction features and classification techniques. Gender prediction can be a very useful method in HCI (Human Computer Interaction) systems. As a very powerful method of extracting data, the classification is used here to collect class data and to classify the gender as either male or female. To extract data features, Local Binary Pattern (LBP) is used, whereas the Random Forest (RF) algorithm of classification is used to gauge the maximum accuracy. Various database models were used in this search: ORL database, FEI database, Jaffe database, and CUHK database where JAFFE database gave a very high level of accuracy which is 99.89% in contrast CUHK database which gave a lower level of accuracy 76.18% with relative stability. Details of the prediction model and results model are reported in this paper. 80 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 63,26
Envío por EUR 7,60Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 65,22
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
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Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 34,25
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Omar EzzMaster s degree in Computer Science with a specialization in Artificial Intelligence.Today s machine learning is widely used in diverse areas. For example, fraudulent systems, recommended system…s, exploited prediction, .
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 39,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Today's machine learning is widely used in diverse areas. For example, fraudulent systems, recommended systems, exploited prediction, and many other applications. One of these applications, is being exploited in this search. This paper…presents an approach to detecting a person's gender through the front face image by using extraction features and classification techniques. Gender prediction can be a very useful method in HCI (Human Computer Interaction) systems. As a very powerful method of extracting data, the classification is used here to collect class data and to classify the gender as either male or female. To extract data features, Local Binary Pattern (LBP) is used, whereas the Random Forest (RF) algorithm of classification is used to gauge the maximum accuracy. Various database models were used in this search: ORL database, FEI database, Jaffe database, and CUHK database where JAFFE database gave a very high level of accuracy which is 99.89% in contrast CUHK database which gave a lower level of accuracy 76.18% with relative stability. Details of the prediction model and results model are reported in this paper.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 40,89
Envío por EUR 60,69Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Today's machine learning is widely used in diverse areas. For example, fraudulent systems, recommended systems, exploited prediction, and many other applications. One of these applications, is being exploited in this search. This paper p…resents an approach to detecting a person's gender through the front face image by using extraction features and classification techniques. Gender prediction can be a very useful method in HCI (Human Computer Interaction) systems. As a very powerful method of extracting data, the classification is used here to collect class data and to classify the gender as either male or female. To extract data features, Local Binary Pattern (LBP) is used, whereas the Random Forest (RF) algorithm of classification is used to gauge the maximum accuracy. Various database models were used in this search: ORL database, FEI database, Jaffe database, and CUHK database where JAFFE database gave a very high level of accuracy which is 99.89% in contrast CUHK database which gave a lower level of accuracy 76.18% with relative stability. Details of the prediction model and results model are reported in this paper.
