INDUCING DYNAMIC RULES OF NATIONS' COMPETITIVENESS FROM 2001–2005 MCI-WCY

A nation's competitiveness has become more and more important in forming government strategy and business decision making. This study proposes an optimization model, instead of regression model or neural network model, to induce rules for dynamic nations' competitiveness based on the Major Competitiveness Indicators of the World Competitiveness Yearbook. Fourteen attributes are used to form the dynamic rules expressed in "IF…THEN…" forms. According to the induced rules, the strategic implications are suggested for various groups of nations to improve or to sustain their competitiveness.

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The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.33.74 ◽

2010 ◽

Vol 33 ◽

pp. 74-78

Author(s):

B. Zhao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Network Model ◽

Process Parameters ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Fiber Diameter ◽

Statistical Regression ◽

Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

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Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model

Advances in Neuro-Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-02490-0_105 ◽

2009 ◽

pp. 859-866

Author(s):

Hiroaki Wagatsuma ◽

Yoko Yamaguchi

Keyword(s):

Neural Network ◽

Decision Making ◽

Network Model ◽

Neural Network Model ◽

Cortical Activity

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Recognition of Fatigue Failure in Wave Energy Converter Using Statistical Control Chart, Multi-criteria Decision Making Tools and Polynomial Neural Network Model

Water and Energy Management in India ◽

10.1007/978-3-030-66683-5_13 ◽

2021 ◽

pp. 259-270

Author(s):

Satyabrata Saha

Keyword(s):

Neural Network ◽

Decision Making ◽

Control Chart ◽

Network Model ◽

Neural Network Model ◽

Wave Energy ◽

Wave Energy Converter ◽

Multi Criteria Decision Making ◽

Statistical Control ◽

Polynomial Neural Network

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Fuzzy RBF Neural Network Model for Multiple Attribute Decision Making

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/11893295_115 ◽

2006 ◽

pp. 1046-1054

Author(s):

Feng Kong ◽

Hongyan Liu

Keyword(s):

Neural Network ◽

Decision Making ◽

Network Model ◽

Neural Network Model ◽

Rbf Neural Network ◽

Multiple Attribute Decision Making ◽

Multiple Attribute

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Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2018.1451399 ◽

2018 ◽

Vol 9 (1) ◽

pp. 501-523 ◽

Cited By ~ 4

Author(s):

Peng Xie ◽

Haijia Wen ◽

Chaochao Ma ◽

Laurie G. Baise ◽

Jialan Zhang

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Regression Model ◽

Network Model ◽

Neural Network Model ◽

Logistic Regression Model ◽

Susceptibility Mapping ◽

Mountainous Region ◽

Landslides Susceptibility

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A Neural Network Model of Motivated Decision-Making in Everyday Social Behavior

Social-Behavioral Modeling for Complex Systems ◽

10.1002/9781119485001.ch7 ◽

2019 ◽

pp. 145-162

Author(s):

Stephen J. Read ◽

Lynn C. Miller

Keyword(s):

Neural Network ◽

Decision Making ◽

Social Behavior ◽

Network Model ◽

Neural Network Model

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Labelled decision-making method based on neural network model and pruning algorithm

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2018.10015374 ◽

2018 ◽

Vol 17 (4) ◽

pp. 497

Author(s):

Jian hai Du

Keyword(s):

Neural Network ◽

Decision Making ◽

Network Model ◽

Neural Network Model ◽

Pruning Algorithm

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Predicting determinants of Internet banking adoption

Management Research Review ◽

10.1108/mrr-06-2014-0139 ◽

2015 ◽

Vol 38 (7) ◽

pp. 750-766 ◽

Cited By ~ 21

Author(s):

Sujeet Kumar Sharma ◽

Srikrishna Madhumohan Govindaluri ◽

Shahid M. Al Balushi

Keyword(s):

Neural Network ◽

Regression Model ◽

Network Model ◽

Neural Network Model ◽

Internet Banking ◽

Demographic Variables ◽

Ease Of Use ◽

Perceived Ease Of Use ◽

Content Type ◽

Main Determinants

Purpose – The purpose of this paper is to explore the main determinants of Internet banking users on the basis of literature of technology acceptance model (TAM). Understanding and predicting main determinants of Internet banking is an important issue for banking industry and users. Design/methodology/approach – Service quality and trust were incorporated in the TAM together with demographic variables. The data were collected using Google Docs from 110 Omani Internet banking users. A two-staged regression-neural network model was applied to understand and predict Internet banking adoption. Findings – The results obtained from multiple linear regression model were compared with the results from neural network model to predict Internet banking adoption and the performance of latter model was found to superior. The neural network model was able to capture relative importance of all independent variables, service quality, trust, perceived usefulness, perceived ease of use, attitude and demographic variables, whereas perceived ease of use and demographic variables were not significant predictors of Internet banking adoption as per the regression model. Practical implications – This study provides useful insights with regard to development of Internet banking systems to banking professionals and information systems researchers in Oman and similar emerging economies. Originality/value – This study is probably the first attempt to model Internet banking adoption in Gulf Cooperation Council using a predictive rather than explanatory focus. The majority of studies in Internet banking adoption in Oman and elsewhere usually utilize modeling methods suited for explanatory purposes.

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