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Artificial Intelligence in Predication of Court Case's time Span - Multi-layer Feed Forward Perceptron Applications in Court Case's Time Span Predictions in Ethiopia - Cherinet, Eskinder Mesfin
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Cherinet, Eskinder Mesfin:
Artificial Intelligence in Predication of Court Case's time Span - Multi-layer Feed Forward Perceptron Applications in Court Case's Time Span Predictions in Ethiopia - edizione con copertina flessibile

2011, ISBN: 9783847302735

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 92, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung, Internationaler Versand

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Artificial Intelligence in Predication of Court Case's time Span - Multi-layer Feed Forward Perceptron Applications in Court Case's Time Span Predictions in Ethiopia - Cherinet, Eskinder Mesfin
libro esaurito
(*)
Cherinet, Eskinder Mesfin:
Artificial Intelligence in Predication of Court Case's time Span - Multi-layer Feed Forward Perceptron Applications in Court Case's Time Span Predictions in Ethiopia - edizione con copertina flessibile

2011, ISBN: 9783847302735

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable., DE, Neuware, gewerbliches Angebot, 92, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung, Spedizioni internazionali

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Artificial Intelligence in Predication of Court Case's time Span - Eskinder Mesfin Cherinet
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Eskinder Mesfin Cherinet:
Artificial Intelligence in Predication of Court Case's time Span - edizione con copertina flessibile

ISBN: 9783847302735

[ED: Taschenbuch], [PU: LAP Lambert Academic Publishing], Neuware - A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable., DE, [SC: 23.00], Neuware, gewerbliches Angebot, 220x150x6 mm, 92, [GW: 153g], PayPal, offene Rechnung, Banküberweisung, sofortueberweisung.de, Spedizioni internazionali

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Artificial Intelligence in Predication of Court Case´s time Span - Eskinder Mesfin Cherinet
libro esaurito
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Eskinder Mesfin Cherinet:
Artificial Intelligence in Predication of Court Case´s time Span - nuovo libro

2011, ISBN: 9783847302735

ID: 953144169

A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable. Multi-layer Feed Forward Perceptron Applications in Court Case´s Time Span Predictions in Ethiopia Buch (fremdspr.) Taschenbuch 01.12.2011 Bücher>Fremdsprachige Bücher>Englische Bücher, LAP LAMBERT Academic Publishing, .201

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Artificial Intelligence in Predication of Court Case's time Span - Cherinet, Eskinder Mesfin
libro esaurito
(*)
Cherinet, Eskinder Mesfin:
Artificial Intelligence in Predication of Court Case's time Span - edizione con copertina flessibile

ISBN: 3847302736

ID: 22519823029

[EAN: 9783847302735], Neubuch, Publisher/Verlag: LAP Lambert Academic Publishing | Multi-layer Feed Forward Perceptron Applications in Court Case's Time Span Predictions in Ethiopia | A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable. | Format: Paperback | Language/Sprache: english | 92 pp

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Dettagli del libro
Artificial Intelligence in Predication of Court Case's time Span

A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable.

Informazioni dettagliate del libro - Artificial Intelligence in Predication of Court Case's time Span


EAN (ISBN-13): 9783847302735
ISBN (ISBN-10): 3847302736
Copertina rigida
Copertina flessibile
Anno di pubblicazione: 2011
Editore: LAP LAMBERT Academic Publishing

Libro nella banca dati dal 02.05.2007 02:24:26
libro trovato per l'ultima volta il23.09.2018 17:14:12
ISBN/EAN: 9783847302735

ISBN - Stili di scrittura alternativi:
3-8473-0273-6, 978-3-8473-0273-5


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