Name: Artificial Intelligence for Project Management (en Inglés)
SKU: 57307474
Price: 272.50 PEN
Availability: InStock
Author: H. Elmousalami, Haytham
ISBN: 9786139884827

portada Artificial Intelligence for Project Management (en Inglés)

Artificial Intelligence for Project Management (en Inglés)

Formato

Libro Físico

Autor

Haytham H. Elmousalami

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

188

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x1.1 cm

Peso

0.28 kg.

ISBN13

9786139884827

Categorías

Gestión y técnicas de gestión

Artificial Intelligence for Project Management (en Inglés)

Haytham H. Elmousalami (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 21 a 26 días háb.

S/ 605,55S/ 272,50

-55%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

S/ 272,50

Llega entre el 10 Nov y el 18 Nov a CERCADO DE LIMA, Lima. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Artificial Intelligence for Project Management (en Inglés)"

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save freshwater. FCIPs are applied as a case study for Artificial Intelligence (AI) applications to projecting cost modeling. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on AI and machine learning methods such as quadratic regression, artificial neural networks (ANNs), fuzzy model and case-based reasoning, genetic algorithm (GA) and hybrid fuzzy systems. Sensitivity analysis is conducted to determine the contribution of selected key parameters. Finally, a simple friendly project data-input screen is created to facilitate usage and manipulation of the developed model.