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Indigo A Neuro Genetic Hybrid Approach for Enhancing Software Development Effort Estimation | Erin Mills Town Centre
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"A Neuro Genetic Hybrid Approach for Enhancing Software Development Effort Estimation" is an essential reference guide for software developers who are looking to improve their estimation accuracy. In this book, Poonam Rijwani presents a novel approach that combines both artificial neural networks and genetic algorithms to enhance the software development effort estimation process.
The book covers a range of topics, including an introduction to software development effort estimation, the fundamentals of artificial neural networks and genetic algorithms, the design and implementation of the hybrid approach, and the evaluation of the approach using real-world data sets.
The author provides step-by-step guidance on how to implement the hybrid approach, including how to select appropriate neural network architecture and genetic algorithm parameters. Additionally, the book discusses how the approach can be adapted to handle different types of software projects and different environmental conditions.
Overall, "A Neuro Genetic Hybrid Approach for Enhancing Software Development Effort Estimation" is a valuable resource for software developers, project managers, and anyone involved in the software development process. The hybrid approach presented in this book can help to improve estimation accuracy, reduce project risks, and ultimately lead to more successful software development projects.
"A Neuro Genetic Hybrid Approach for Enhancing Software Development Effort Estimation" is an essential reference guide for software developers who are looking to improve their estimation accuracy. In this book, Poonam Rijwani presents a novel approach that combines both artificial neural networks and genetic algorithms to enhance the software development effort estimation process.
The book covers a range of topics, including an introduction to software development effort estimation, the fundamentals of artificial neural networks and genetic algorithms, the design and implementation of the hybrid approach, and the evaluation of the approach using real-world data sets.
The author provides step-by-step guidance on how to implement the hybrid approach, including how to select appropriate neural network architecture and genetic algorithm parameters. Additionally, the book discusses how the approach can be adapted to handle different types of software projects and different environmental conditions.
Overall, "A Neuro Genetic Hybrid Approach for Enhancing Software Development Effort Estimation" is a valuable resource for software developers, project managers, and anyone involved in the software development process. The hybrid approach presented in this book can help to improve estimation accuracy, reduce project risks, and ultimately lead to more successful software development projects.
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