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Hybrid Classification Model Reverse by Bhambri Pankaj (5 results)
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Hybrid Classification Model For The Reverse Code Generation: Software Engineering
Language: English
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202683430 ISBN 13: 9786202683432
Condition: New.
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Hybrid Classification Model For The Reverse Code Generation | Software Engineering
Language: English
Published by LAP Lambert Academic Publishing, 2020
ISBN 10: 6202683430 ISBN 13: 9786202683432
Taschenbuch. Condition: Neu. Hybrid Classification Model For The Reverse Code Generation | Software Engineering | Pankaj Bhambri (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9786202683432 | 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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Hybrid Classification Model For The Reverse Code Generation: Software Engineering
Language: English
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202683430 ISBN 13: 9786202683432
Condition: New. PRINT ON DEMAND.
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Hybrid Classification Model For The Reverse Code Generation
Language: English
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202683430 ISBN 13: 9786202683432
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bhambri PankajDr. Pankaj Bhambri is working as an Assistant Professor in the Information Technology Department of Guru Nanak Dev Engineering College, Ludhiana, Punjab. He has more than 15 years of teaching and research experience. Hi.
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Hybrid Classification Model For The Reverse Code Generation : Software Engineering
Language: English
Published by LAP Lambert Academic Publishing, 2020
ISBN 10: 6202683430 ISBN 13: 9786202683432
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The abstract-present is the model of software engineering which is used to generate the source code from the sequence model. The code that is generated for the one phase will be given as input to generate code for the second phase. To generate reliable code, the improvement will be proposed in the abstract-present model. To do so, the SVM classifier will be used to classify required and non-required code to generate the next phase of code. The proposed model for the software defect prediction is the hybrid type of model. The proposed model is implemented in python and results are analyzed in terms of accuracy and execution time. It is analyzed that the hybrid model performs well as compared to the existing model for software defect prediction.
