Evaluation Model of Tourism Congestion Based on Space Syntax and Neural Network Method: A Case Study of Kulangsu Xiamen

2019 ◽  
Author(s):  
Xuchao Wu
Keyword(s):  
Neural Network ◽  
Evaluation Model ◽  
Space Syntax ◽  
Neural Network Method ◽  
Network Method

An assessment tool for the evaluation of circular economy implementation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karel Diéguez-Santana ◽  
Giselle Rodríguez Rudi ◽  
Ana Julia Acevedo Urquiaga ◽  
Emanuel Muñoz ◽  
Neyfe Sablón-Cossio
Keyword(s):  
Neural Network ◽  
Circular Economy ◽  
Assessment Tool ◽  
Business Practice ◽  
Business Practices ◽  
Content Type ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method

PurposeIn this paper, the authors adopt the theory of the circular economy to study the transitions that take place in three case studies in Mexico and Ecuador. The work is aimed to systematize a circular economy assessment tool that fosters opportunities for improvement in business practices.Design/methodology/approachThe methodology is based on a descriptive quantitative analysis, where a checklist is made with 91 items and nine study variables. This is from the study of the bibliography and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable according to the sensitivity by the Lek’s profile method.FindingsIt is based on a descriptive quantitative analysis, where a checklist with 91 items and nine study variables is made, defined from a bibliographic study and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable based on sensitivity.Research limitations/implicationsThe application of the tool requires prior knowledge of the circular economy approach, which is why specialized personnel are needed for its application. This makes research more expensive in time and human resources.Practical implicationsThe practical and methodological contribution of this work lies in the feasibility of the tool that favors the definition of improvement actions for the implementation contribution to the circular economy in business practices.Social implicationsThe social contribution is framed in the gradual transition to circular economy approaches in underdeveloped countries.Originality/valueThe use of the neural network method to predict the level of circular economy in a case study allows making decisions in a predictive way. This encourages the development of the circular economy according to the context needs.

scholarly journals Modern neural network method evaluation model security settings resources information systems

2014 ◽  
Vol 16 (3) ◽  
Author(s):  
Олександр Григорович Корченко ◽  
Ігор Анатолійович Терейковський ◽  
Андрій Васильович Дзюбаненко
Keyword(s):  
Neural Network ◽  
Information Systems ◽  
Evaluation Model ◽  
Method Evaluation ◽  
Neural Network Method ◽  
Network Method

scholarly journals Autonomous Capability Evaluation of Ground-Attack UAV Based on Cloud Model and Combined Weight Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Feng ◽  
Shuguang Liu ◽  
Wujie Xie
Keyword(s):  
Neural Network ◽  
Cloud Model ◽  
Evaluation Model ◽  
Multicriteria Decision Making ◽  
Hopfield Neural Network ◽  
Neural Network Method ◽  
Index Model ◽  
Action Capability ◽  
Network Method ◽  
Capability Evaluation

The evaluation for autonomous capability of ground-attack unmanned aerial vehicle (UAV) comes from the demand of reality, which determines the operational use of airborne equipment authority. It essentially entails a multicriteria decision-making process accounting for evaluation model and uncertainties. Firstly, as for the construction of evaluation model, the index model is proposed from four aspects of observation capability, decision capability, action capability, and security capability, namely, ODAS, which analogizes cognitive behavior mechanism of human based on airborne equipment; then, to solve uncertainties of randomness and fuzziness in the process of autonomous capability evaluation, a cloud model approach is proposed, which expresses uncertainties by the certainty degree distribution. Finally, the cloud model-based approach is tested by evaluating typical UAVs and comparing with Hopfield neural network method. The results show that the evaluation of the autonomous capability based on the cloud model is accurate and more representative than the Hopfield neural network method.

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