Computational Management
Applications of Computational Intelligence in Business Management
Published on 30. May 2021
Book
Hardback
XVIII, 682 pages
978-3-030-72928-8 (ISBN)
Description
This book offers a timely review of cutting-edge applications of computational intelligence to business management and financial analysis. It covers a wide range of intelligent and optimization techniques, reporting in detail on their application to real-world problems relating to portfolio management and demand forecasting, decision making, knowledge acquisition, and supply chain scheduling and management.
More details
Series
Edition
2021 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Illustrations
54 s/w Abbildungen, 114 farbige Abbildungen
XVIII, 682 p. 168 illus., 114 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 43 mm
Weight
1209 gr
ISBN-13
978-3-030-72928-8 (9783030729288)
DOI
10.1007/978-3-030-72929-5
Schweitzer Classification
Other editions
Additional editions
Srikanta Patnaik | Kayhan Tajeddini | Vipul Jain
Computational Management
Applications of Computational Intelligence in Business Management
Book
05/2022
Springer
€192.59
Shipment within 7-9 days
Srikanta Patnaik | Kayhan Tajeddini | Vipul Jain
Computational Management
Applications of Computational Intelligence in Business Management
E-Book
05/2021
1st Edition
Springer
€181.89
Available for download
Persons
Content
Computational Management: An Overview.- Mathematical and computational approaches for stochastic control of river environment and ecology: from fisheries viewpoint.- Models and Tools of Knowledge Acquisition.- Profits Lie in the Eyes of the Beholder: Entropy-based Volatility Indicators and Portfolio Rotation Strategies.- Asymmetric spill overs between risk and return series: case of CESEE markets.- Millennial Customers and Hangout Joints: An Empirical Study Using the Kano Quantitative Model.- The Effects of Oil Shocks on Macroeconomic Uncertainty: Evidence from a Large Panel Dataset of US States.