Название: An Introduction to the Management of Complex Software Projects
Автор: Veljko Milutinović, Filip Đorđević, Miloš Kotlar, Jakob Salom
Издательство: Cambridge Scholars Publishing
Год: 2024
Страниц: 246
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book explores the challenges of managing software projects, such as changing requirements, uncertain technologies, and evolving user needs, provides strategies for addressing these and other emerging issues, and contains a number of eye-opening perspectives from experts in different fields. Instead of relying solely on traditional project management techniques, the book presents a holistic, adaptive, and flexible framework that takes into account the unique challenges of each particular case of software development. It recognizes that software development is a complex and creative process that involves people with diverse skills and personalities, and provides insights into how to motivate and manage teams, how to communicate effectively, how to automate processes, and how to deal with conflict and uncertainty, from computer engineering and mathematical logic, all the way to advanced geophysics and earthquake engineering. It provides a wealth of practical advice and guidance, as well as insights into the latest schools of thought related to software project management.

This section is based on the practical implementation of the software solutions carried out in a Python environment with Keras and TensorFlow, as well as in the Matlab framework. The presented programming codes, based mainly on Artificial Neural Networks (ANNs) were formed for regression issues intended for predictive analyses, but the fundamentals can also be applied to classification problems with appropriate model adaptation. In addition, per the author’s selection, the basics for the application of some mentioned alternative techniques that are widely used in the construction industry such as Deep-Learning, Random Forest or Support Vector Machines are given, with detailed step-by-step instructions for building a full model with all phases of data processing and extraction of the results, applicable to a wide range of engineering tasks.

Based on the experience of applying Machine Learning techniques in the construction industry, the authors singled out ANN models that enable a mathematical relationship between all model attributes, their practically oriented engineering application and also interaction with the already mentioned associated alternative methods.




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