Prediction of multiproject resource conflict risk via an artificial neural network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Libiao Bai ◽  
Zhiguo Wang ◽  
Hailing Wang ◽  
Ning Huang ◽  
Huijing Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Index System ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Ann Model ◽  
Content Type ◽  
Resource Conflict ◽  
Resource Conflicts ◽  
Conflict Risk

PurposeInadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk.Design/methodology/approachA fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk.FindingsAccurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately.Originality/valueThe authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables.

scholarly journals Application of an Artificial Neural Network to Evaluate the Integration Effect of Quality Education and Skill Education

2021 ◽  
Vol 16 (07) ◽  
pp. 218
Author(s):  
Cai Bai ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Index System ◽  
Quality Education ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Ann Model ◽  
Current State ◽  
Integration Effect ◽  
Artificial Neural

Quality education is the basis, driver, and inspirer of skill education. These two education models can complement and interact with each other. However, few scholars have discussed the current state and future trend of the integration between the two models, not to mention quantifying the integration effect. This paper applied the artificial neural network (ANN) to evaluate the integration effect of quality education and skill education, owing to the advantages of the ANN in processing nonlinear information adaptively. First, the subjects and motivation mechanismss of the integration between the two models were analyzed. Then, an evaluation index system was established for the integration effect. After that, an ANN model was created for the compatibility of evaluation indexes and used to predict the integration effect. Experimental results verified the reasonability of the proposed evaluation index system, and the effectiveness of the proposed model. Finally, the current state of the integration was analyzed based on the prediction results.

Study on BP Artificial Neural Network of Old Community Renovation's Livable

2013 ◽  
Vol 807-809 ◽  
pp. 1804-1808
Author(s):  
Meng Ling Li ◽  
Xing Meng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Index System ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Bp Artificial Neural Network ◽  
Ancient Times ◽  
Definition Of ◽  
Artificial Network ◽  
The Ideal

“Living and working in peace and contentment” is the ideal goals of people since the ancient times, while old community’s renovation is an important aspect of it. This paper firstly analyzes the definition of livable community and the necessity of old community’s renovation，establishing the livable evaluation index system about old community’s renovation, and try to use the BP artificial network to analysis the livability.

scholarly journals Construction Quality Evaluation of Teacher Development Schools Based on Improved Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 205
Author(s):  
Xiaoyu Hua
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Teacher Development ◽  
Index System ◽  
Quality Evaluation ◽  
Job Training ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Construction Quality

There are many problems with the convention teacher education model, such as the short internship time during pre-job training, and the limited experience, pertinence, and effectiveness of on-the-job training. Fortunately, the teacher development school mechanism provides a viable solution to these problems. Therefore, the construction quality of such schools is of great significance to the teaching level and professional development of school teachers, as well as the overall development of students. As a result, this paper proposes a method to evaluate the construction quality of teacher development schools based on an improved artificial neural network. Firstly, an evaluation index system was established for the construction quality of teacher development schools, which consists of 5 core evaluation indices, and the periodical scoring criteria were detailed. Then, the feasibility of the proposed evaluation index system was verified through reliability, validity, and difference analyses. Finally, a combined neural network was constructed to evaluate the construction quality of teacher development schools. The experimental results show that our model can effectively predict the construction quality of teacher development schools, providing a reference for project quality evaluation in other fields.

scholarly journals User Experience Evaluation of Cross-Channel Consumption: Based on Grounded Theory and Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hua Yang ◽  
Huiying Wei ◽  
Xiang He ◽  
Yue Yan ◽  
Xiaoju Liu
Keyword(s):  
Neural Network ◽  
Grounded Theory ◽  
User Experience ◽  
Index System ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Theoretical Research ◽  
Evaluation Index ◽  
Channel Switching ◽  
Depth Analysis

With the rapid development of e-commerce technology, cross-channel consumption has become the mainstream mode of contemporary consumers. However, there are several problems of cross-channel consumption such as inconsistency of online and offline channel information and service, disfluency of channel switching which have brought adverse effects on user experience. The question arises here as to what factors influence user experience and how to build a scientific and effective evaluation index system. Different from previous studies based on sellers, this paper used grounded theory to analyze and summarize the evaluation index system of user experience under cross-channel consumption from the perspective of consumers. We summarized and refined four first level indexes which are “online platform attribute, offline entity attribute, channel switching attribute, and individual demand” and 13 second level indexes which are “platform operation, platform information, platform service, platform promotion, product quality, service quality, environment quality, channel consistency, channel switching cost, channel switching fluency, psychological expectation, personal interests and individual needs.” Then, we used BP neural network to build the evaluation model and trained and simulated the performance of the sample. The results show that the evaluation model has a good generalization ability and can effectively evaluate user experience under cross-channel consumption. Finally, implications and limitations are also discussed. This study helps to enrich the theoretical research on user experience and consumer behavior. It also provides targeted basis for in-depth analysis of cross-channel consumption behavior, establishment of user experience evaluation index system, and improving user experience and multichannel management of physical stores.

scholarly journals Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 526
Author(s):  
Yi Lei ◽  
Xiaodong Qiu
Keyword(s):  
Neural Network ◽  
Index System ◽  
Evaluation System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Entropy Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Investment Climate ◽  
Cross Border

China’s cross-border e-commerce will usher in a new golden age of development. Based on seven countries which include the Russian Federation, Mongolia, Ukraine, Kazakhstan, Tajikistan, Kyrgyzstan and Belarus along the “Belt and Road”, an evaluation system for cross-border e-commerce investment climate indicators is established in this study. This research applied the entropy method twice to evaluate the investment climate of seven countries based on 5 years panel data comprehensively and these countries are then classified into politics-oriented and industry-oriented countries, and then the weight of indicators for each category is analyzed. In addition, cross-border e-commerce investors are proposed to prioritize industry-oriented countries. Back propagation neural network algorithm is used to map the existing data and optimize the evaluation index system in combination with the genetic algorithm. This research denotes the effort to find out the index evaluation combination corresponding to the best overall score, make the established evaluation index system applicable to other countries, and provide reference for cross-border e-commerce investors when evaluating the investment climate in each country. This study provides the important practical implications in the sustainable development of China’s cross-border e-commerce environment.

Evaluation of auto parts remanufacturing by grey cluster model

2016 ◽  
Vol 6 (3) ◽  
pp. 296-308 ◽  
Author(s):  
Jianghui Xin
Keyword(s):  
Index System ◽  
Cluster Model ◽  
Coefficient Matrix ◽  
Clustering Coefficient ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Content Type ◽  
Fixed Weight ◽  
Grey Clustering ◽  
Auto Parts

Purpose With the improvement of economic level, car ownership is growing, and the number of scrapped automobiles is increasing. Therefore, evaluation research for auto parts remanufacturing is particularly important. The purpose of this paper is to construct the evaluation index system of auto parts remanufacturing and research the grey clustering theory. The grey fixed weight clustering evaluation is used to evaluate automobile engine remanufacturability. Design/methodology/approach According to the policies and regulations of China about remanufacturing, economic, technical, resources, energy and the environment, four indexes are selected to set up the evaluation standard of auto parts remanufacturing scheme. Grey fixed weight clustering method is used to evaluate remanufacturability of the auto parts. Firstly, number index and grey determine the whitenization weight function, then based on the clustering weight of each index, the clustering coefficient matrix is calculated. Finally, the class that certain object belongs to, according to the clustering coefficient matrix is determined. Findings Results show that constructed indexes of auto parts remanufacturing scheme can be used for effective evaluation. And the proposed fixed weight grey cluster model can aggregate all indexes information well. Therefore, the proposed indexes and model in this paper are effective and can be used for auto parts remanufacturing. Practical implications According to the requirements of the current situation in China, this paper puts forward a method based on grey clustering decision, to evaluate different auto parts remanufacturing schemes, for manufacturing enterprises to provide theoretical basis for remanufacturing production, in order to realize the reasonable configuration of resources. Originality/value This paper firstly establishes the evaluation index system of auto parts remanufacturing, the grey clustering theory is introduced into the evaluation of remanufacturing. The fixed-weight grey cluster model is proposed to aggregate indexes’ information.

Study on the Evaluation of Scientific Research Achievements Based on the BP Neural Network

2012 ◽  
Vol 170-173 ◽  
pp. 3436-3439
Author(s):  
Xiao Hui Hou ◽  
Lei Huang ◽  
Xue Fei Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Method ◽  
Scientific Research ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Evaluation Result ◽  
Set Up ◽  
Solution Efficiency

The scientific research achievements are evaluated based on the BP neural network method which is developed in this paper. According to the analysis and consult with the well-known experts, set up the evaluation index system of scientific research achievements, and based on it, the BP neural network model which is used to evaluate the scientific research achievements is established. Through an actual example, in order to improve the solution efficiency, use the Matlab software to solve the model and get the evaluation result of the scientific research achievements in the example. The evaluation result has high accuracy and could meet the basic actual needs. The evaluation method which is set up in this paper will benefit to our country's evaluation index system of the scientific research achievements and will promote the development of evaluation methods of the scientific research achievements.

Application of artificial neural network to loan recovery prediction

2016 ◽  
Vol 9 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Amos Olaolu Adewusi ◽  
Tunbosun Biodun Oyedokun ◽  
Mustapha Oyewole Bello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Type I ◽  
Ann Model ◽  
Content Type ◽  
Credit Evaluation ◽  
Artificial Neural

Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

Engineering Project Risk Evaluation System Construction and Research Based on BP Model

2013 ◽  
Vol 368-370 ◽  
pp. 2050-2053
Author(s):  
Jian Wei Zhang ◽  
Dong Lu Ye ◽  
Guan Chan Ye ◽  
Jing Zhi Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Risk Evaluation ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Engineering Project ◽  
Project Risk ◽  
Engineering Construction ◽  
Advantages And Disadvantages

According to the status of the current engineering construction field in our country, in order to adapt to the requirements of engineering construction project risk evaluation, this paper discuss establishing a reasonable risk evalluation index system and a model of effective risk evaluation. By analyzing the advantages and disadvantages of the general model of risk evaluation, determine the risk evaluation model combined with BP neural network with AHP; secondly,establish a risk evaluation index system; once again,illustrate the method which represents the correlation between evaluation index system and the degree of risk; finally, establish a reasonable BP neural network model.Key Word:Evaluation index; Risk Evaluation ;AHP;BP Neural Network; Model Construction

scholarly journals Evaluation of regional low-carbon circular economy development: a case study in Sichuan province, China

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meng Ye ◽  
Fumin Deng ◽  
Li Yang ◽  
Xuedong Liang
Keyword(s):  
Economic Development ◽  
Index System ◽  
Evaluation Method ◽  
Sichuan Province ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Low Carbon ◽  
Entropy Weight ◽  
Content Type ◽  
Development Level

Purpose This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results. Design/methodology/approach This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018. Findings It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments. Research limitations/implications This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers. Originality/value This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

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