Recent Advances in Artificial Intelligence in Cost Estimation in Project Management / by Nevena Rankovic, Dragica Ranković, Mirjana Ivanovic, Ljubomir Lazić

Von:

Rankovic, Nevena [VerfasserIn]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Artificial Intelligence-Enhanced Software and Systems Engineering ; 6Verlag: Cham : Springer Nature Switzerland, 2024Verlag: Cham : Imprint: Springer, 2024Auflage: 1st ed. 2024Beschreibung: 1 Online-Ressource(XI, 417 p. 138 illus., 137 illus. in color.)ISBN:

9783031765728

Schlagwörter:

006.3 23

DOI: DOI: 10.1007/978-3-031-76572-8Online-Ressourcen:

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Zusammenfassung: Introduction -- Top AI Techniques for Every Phase of Software Project Management -- Use of AI Methods in Software Project Scheduling -- AI in Software Effort Estimation -- AI in Risk Management -- AI in Project Resource Management -- AI software project management tools -- Conclusion -- Optimizing Effort and Cost Estimation: Model Implementation using Artificial Neural Networks and Taguchi’s Orthogonal Vector Plans.Zusammenfassung: This book focuses on the practical application of AI tools and techniques in software project management, offering detailed theoretical explanations and practical examples of over 40 state-of-the-art machine learning and deep learning algorithms applied across each project phase, as well as in risk and resource management. Helping the business world estimate projects more accurately while saving costs and resources is crucial in today’s rapidly changing, fast-paced technological landscape. Moreover, it presents specific aspects of combined approaches through ensemble models, incorporating Taguchi’s optimization method to further improve estimation accuracy, advancing this area of software project management. A valuable resource for students and professionals to deepen their knowledge and skills, it also serves as a great manual for companies adopting smarter strategies to manage and develop projects more efficiently and effectively.PPN: PPN: 1911776363Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT

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