Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Decision Patterns on Software Metrices for Single& Multiple Projects | Nageswara Rao Moparthi (u. a.) | Taschenbuch | 200 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330049550 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Feb 2017, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this research book found the Methods for detecting decision patterns on software metrics for single and associated multiple projects while implantation of SDLC Phases for reinforced taking optimal way for decision making which helps a lot for client in terms of business process improvement.In our first model, a preprocessed based hybrid Bayesian network was implemented to handle large number of metrics for multi-defect decision patterns and other a new ensemble defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns and also establishing a new privacy preserving based defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns. 200 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Moparthi Nageswara RaoDr.M.Nageswara Rao is Distinguished Professor, Inventor, Author and Business leader Born in India. He Received his M.Tech(CSE) & PhD(Computer Science & Technology) from ANU University and SKU University. Dr.M. .
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Feb 2017, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this research book found the Methods for detecting decision patterns on software metrics for single and associated multiple projects while implantation of SDLC Phases for reinforced taking optimal way for decision making which helps a lot for client in terms of business process improvement.In our first model, a preprocessed based hybrid Bayesian network was implemented to handle large number of metrics for multi-defect decision patterns and other a new ensemble defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns and also establishing a new privacy preserving based defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 200 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330049553 ISBN 13: 9783330049550
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 65,68
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this research book found the Methods for detecting decision patterns on software metrics for single and associated multiple projects while implantation of SDLC Phases for reinforced taking optimal way for decision making which helps a lot for client in terms of business process improvement.In our first model, a preprocessed based hybrid Bayesian network was implemented to handle large number of metrics for multi-defect decision patterns and other a new ensemble defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns and also establishing a new privacy preserving based defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns.