Name: RULE-BASED EXPERT SYSTEM FOR SOFTWARE VERSION CLASSIFICATION: Design, Knowledge Representation, Inference Engine, and Practical Apps for Intelligent Software Version Management (en Inglés)
SKU: 69197132
Price: 67220.00 CLP
Availability: InStock
Author: Matumueni, Humberto Cuteso
ISBN: 9786630301458

portada RULE-BASED EXPERT SYSTEM FOR SOFTWARE VERSION CLASSIFICATION: Design, Knowledge Representation, Inference Engine, and Practical Apps for Intelligent Software Version Management (en Inglés)

RULE-BASED EXPERT SYSTEM FOR SOFTWARE VERSION CLASSIFICATION: Design, Knowledge Representation, Inference Engine, and Practical Apps for Intelligent Software Version Management (en Inglés)

Formato

Libro Físico

Editorial

GlobeEdit

Autor

Matumueni, Humberto Cuteso

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786630301458

RULE-BASED EXPERT SYSTEM FOR SOFTWARE VERSION CLASSIFICATION: Design, Knowledge Representation, Inference Engine, and Practical Apps for Intelligent Software Version Management (en Inglés)

Matumueni, Humberto Cuteso (Autor) · GlobeEdit · Tapa Blanda

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Reseña del libro "RULE-BASED EXPERT SYSTEM FOR SOFTWARE VERSION CLASSIFICATION: Design, Knowledge Representation, Inference Engine, and Practical Apps for Intelligent Software Version Management (en Inglés)"

This book presents the design and implementation of a Rule-Based Expert System for software version classification using decision rules. It explains the principles of expert systems, knowledge representation, rule-based reasoning, and inference mechanisms applied to software version management. Readers will learn how to acquire expert knowledge, construct a knowledge base, develop decision rules, and implement an inference engine capable of identifying and classifying software versions accurately and consistently. The book also discusses practical applications, system architecture, performance evaluation, and future research directions. Intended for students, researchers, software engineers, and IT professionals, this work combines theoretical foundations with practical examples to demonstrate how artificial intelligence techniques can improve software version identification, maintenance, and decision-making processes in modern computing environments.