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Optimized Convolution Neural Network (OCNN) for VBSLR: Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization & Regularization - Couverture souple
Synopsis
Human-computer-interactions (HCI) are very helpful for current technology world. Later the trendy data policies restrict the instinctive nature & velocity of HCI use, the sign language acknowledgement framework has multiplied many significances. Completely one of a kind sign language is frequently accustomed to specific intentions and intonations or for dominant units like domestic robots. In Previous work, ICNN was once in contrast to a baseline CNN and in preceding work OCNN gain 99.96% recognition rate. The foremost centre of attention of this evaluation is to enhance the loss and execution time of ICNN model by using optimizing the preference for alternate OCNN. New model is improving configuration by applying optimizing function in bottom layer. Due to the fact it is previously acknowledged, one optimizer will never give higher accuracy for all situation. The desire for the optimizer to be created through thinking about the variability of facts and consequently the nonlinearity degree of the connection designs that happen inside the facts. As a result of the theoretical calculation isn't always sufficient to work out the easiest optimization function.
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Optimized Convolution Neural Network (OCNN) for VBSLR
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6203927937
ISBN 13 : 9786203927931
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Swarnkar Suman KumarDr Suman Kumar Swanrkar received a M.Tech. degree in 2015 from the Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He received Ph.D. (CSE) degree in 2021 from Kalinga University, Nayaraipur, India. He has. N° de réf. du vendeur 490936273
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Optimized Convolution Neural Network (OCNN) for VBSLR
Edité par
LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10 : 6203927937
ISBN 13 : 9786203927931
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Human-computer-interactions (HCI) are very helpful for current technology world. Later the trendy data policies restrict the instinctive nature & velocity of HCI use, the sign language acknowledgement framework has multiplied many significances. Completely one of a kind sign language is frequently accustomed to specific intentions and intonations or for dominant units like domestic robots. In Previous work, ICNN was once in contrast to a baseline CNN and in preceding work OCNN gain 99.96% recognition rate. The foremost centre of attention of this evaluation is to enhance the loss and execution time of ICNN model by using optimizing the preference for alternate OCNN. New model is improving configuration by applying optimizing function in bottom layer. Due to the fact it is previously acknowledged, one optimizer will never give higher accuracy for all situation. The desire for the optimizer to be created through thinking about the variability of facts and consequently the nonlinearity degree of the connection designs that happen inside the facts. As a result of the theoretical calculation isn't always sufficient to work out the easiest optimization function. 120 pp. Englisch. N° de réf. du vendeur 9786203927931
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Optimized Convolution Neural Network (OCNN) for VBSLR : Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization & Regularization
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6203927937
ISBN 13 : 9786203927931
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Human-computer-interactions (HCI) are very helpful for current technology world. Later the trendy data policies restrict the instinctive nature & velocity of HCI use, the sign language acknowledgement framework has multiplied many significances. Completely one of a kind sign language is frequently accustomed to specific intentions and intonations or for dominant units like domestic robots. In Previous work, ICNN was once in contrast to a baseline CNN and in preceding work OCNN gain 99.96% recognition rate. The foremost centre of attention of this evaluation is to enhance the loss and execution time of ICNN model by using optimizing the preference for alternate OCNN. New model is improving configuration by applying optimizing function in bottom layer. Due to the fact it is previously acknowledged, one optimizer will never give higher accuracy for all situation. The desire for the optimizer to be created through thinking about the variability of facts and consequently the nonlinearity degree of the connection designs that happen inside the facts. As a result of the theoretical calculation isn't always sufficient to work out the easiest optimization function. N° de réf. du vendeur 9786203927931
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Optimized Convolution Neural Network (OCNN) for VBSLR
Edité par
LAP LAMBERT Academic Publishing Jun 2021, 2021
ISBN 10 : 6203927937
ISBN 13 : 9786203927931
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Human-computer-interactions (HCI) are very helpful for current technology world. Later the trendy data policies restrict the instinctive nature & velocity of HCI use, the sign language acknowledgement framework has multiplied many significances. Completely one of a kind sign language is frequently accustomed to specific intentions and intonations or for dominant units like domestic robots. In Previous work, ICNN was once in contrast to a baseline CNN and in preceding work OCNN gain 99.96% recognition rate. The foremost centre of attention of this evaluation is to enhance the loss and execution time of ICNN model by using optimizing the preference for alternate OCNN. New model is improving configuration by applying optimizing function in bottom layer. Due to the fact it is previously acknowledged, one optimizer will never give higher accuracy for all situation. The desire for the optimizer to be created through thinking about the variability of facts and consequently the nonlinearity degree of the connection designs that happen inside the facts. As a result of the theoretical calculation isn't always sufficient to work out the easiest optimization function.Books on Demand GmbH, Überseering 33, 22297 Hamburg 120 pp. Englisch. N° de réf. du vendeur 9786203927931
Quantité disponible : 2 disponible(s)