The book organized as follows: Chapter 1 describes the Software development process, need for effort estimation, and different approaches in algorithmic effort estimation techniques. chapter 2 presents literature survey of existing approaches available in the effort estimation, and provide a detailed report on the non-algorithmic combination of approaches to improve the estimation accuracy. Chapter 3 proposes a new effort estimation method based on fuzzy logic with function point size. The triangular fuzzy membership function is used with size parameter in function point. This model is compared with COCOMO model for its accuracy. Chapter 4 proposes Adaptive Neuro-fuzzy scheme proposed to integrate the concept of Artificial Neural network and fuzzy logic. Chapter 5 proposes particle swarm optimization and K means hybrid algorithm for clustering the data, and effort is estimated with neural network and analogy based estimation. Chapter 6 is a comparison of all the proposed models in four experiments. The metrics used are MRE, MAE, MBRE and MIBRE. The results show that both neuro-fuzzy and clustering algorithms are the best in estimating the software effort.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
EUR 23,00 per la spedizione da Germania a U.S.A.
Destinazione, tempi e costiDa: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book organized as follows: Chapter 1 describes the Software development process, need for effort estimation, and different approaches in algorithmic effort estimation techniques. chapter 2 presents literature survey of existing approaches available in the effort estimation, and provide a detailed report on the non-algorithmic combination of approaches to improve the estimation accuracy. Chapter 3 proposes a new effort estimation method based on fuzzy logic with function point size. The triangular fuzzy membership function is used with size parameter in function point. This model is compared with COCOMO model for its accuracy. Chapter 4 proposes Adaptive Neuro-fuzzy scheme proposed to integrate the concept of Artificial Neural network and fuzzy logic. Chapter 5 proposes particle swarm optimization and K means hybrid algorithm for clustering the data, and effort is estimated with neural network and analogy based estimation. Chapter 6 is a comparison of all the proposed models in four experiments. The metrics used are MRE, MAE, MBRE and MIBRE. The results show that both neuro-fuzzy and clustering algorithms are the best in estimating the software effort. 144 pp. Englisch. Codice articolo 9786139887828
Quantità: 2 disponibili
Da: moluna, Greven, Germania
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shivakumar NagarajanAn Assistant Professor at Thiagarajar College of Engineering and Ph. D in software Engineering, with research interest in Machine Learning, Deep Learning and Virtual Reality.The book organized as follows: Ch. Codice articolo 385875940
Quantità: Più di 20 disponibili
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book organized as follows: Chapter 1 describes the Software development process, need for effort estimation, and different approaches in algorithmic effort estimation techniques. chapter 2 presents literature survey of existing approaches available in the effort estimation, and provide a detailed report on the non-algorithmic combination of approaches to improve the estimation accuracy. Chapter 3 proposes a new effort estimation method based on fuzzy logic with function point size. The triangular fuzzy membership function is used with size parameter in function point. This model is compared with COCOMO model for its accuracy. Chapter 4 proposes Adaptive Neuro-fuzzy scheme proposed to integrate the concept of Artificial Neural network and fuzzy logic. Chapter 5 proposes particle swarm optimization and K means hybrid algorithm for clustering the data, and effort is estimated with neural network and analogy based estimation. Chapter 6 is a comparison of all the proposed models in four experiments. The metrics used are MRE, MAE, MBRE and MIBRE. The results show that both neuro-fuzzy and clustering algorithms are the best in estimating the software effort.Books on Demand GmbH, Überseering 33, 22297 Hamburg 144 pp. Englisch. Codice articolo 9786139887828
Quantità: 1 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book organized as follows: Chapter 1 describes the Software development process, need for effort estimation, and different approaches in algorithmic effort estimation techniques. chapter 2 presents literature survey of existing approaches available in the effort estimation, and provide a detailed report on the non-algorithmic combination of approaches to improve the estimation accuracy. Chapter 3 proposes a new effort estimation method based on fuzzy logic with function point size. The triangular fuzzy membership function is used with size parameter in function point. This model is compared with COCOMO model for its accuracy. Chapter 4 proposes Adaptive Neuro-fuzzy scheme proposed to integrate the concept of Artificial Neural network and fuzzy logic. Chapter 5 proposes particle swarm optimization and K means hybrid algorithm for clustering the data, and effort is estimated with neural network and analogy based estimation. Chapter 6 is a comparison of all the proposed models in four experiments. The metrics used are MRE, MAE, MBRE and MIBRE. The results show that both neuro-fuzzy and clustering algorithms are the best in estimating the software effort. Codice articolo 9786139887828
Quantità: 1 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Paperback. Condizione: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock. Codice articolo zk6139887828
Quantità: 1 disponibili