Omar ezz (8 résultats)
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Vendeur : Books Puddle, New York, NY, Etats-UnisBooks Puddle
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EUR 64,20
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Etat : New.
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Vendeur : preigu, Osnabrück, Allemagnepreigu
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EUR 36,35
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 5 disponible(s)
Taschenbuch. Etat : 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 | An…bieter: preigu.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
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EUR 39,90
EUR 23,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : 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 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. 80 pp. Englisch.
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Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
Contacter le vendeurVendeur avec une évaluation de 4 étoilesEtat: Neuf
EUR 63,26
EUR 7,60 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 4 disponible(s)
Etat : New. Print on Demand.
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Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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EUR 65,22
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Etat : New. PRINT ON DEMAND.
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Vendeur : moluna, Greven, Allemagnemoluna
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EUR 34,25
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Etat : 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 systems, e…xploited prediction, .
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
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EUR 39,90
EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : 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 pres…ents 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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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 40,89
EUR 60,69 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : 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 prese…nts 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.
