Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ebrahimkhani hamed; KAZEMI MOSTAFA; POOYA ALIREZA; Fakoor Saghih Amir Mohammad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: MODIRIAT-E-FARDA Year:2019 | Volume:18 | Issue:60 Page(s): 205-220

Download Full-Text

Persian Verion

View:

294

Download:

Cites:

Information Journal Paper

Title

Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm

Author(s)

ebrahimkhani hamed | KAZEMI MOSTAFA | POOYA ALIREZA | Fakoor Saghih Amir Mohammad | Issue Writer Certificate

Keywords

Firm GrowthQ2

Artificial Neural NetworkQ2

Meta-heuristic algorithmsQ1

Abstract

The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting businesses, entrepreneurs and companies to pay. Accordingly, the purpose of this study was to predict the growth of small and medium enterprises with the combination of neural network and Meta-heuristic algorithms. The purpose of this research was applied and based on the method of doing descriptive-modeling work. Statistical population of this research was all small and medium enterprises of Zanjan province. Statistical sample size According to the growth of companies, 158 companies has been designated. In order to collect data in this study, interviews, questionnaires and documents of companies have been used. Validity and reliability of the questionnaire were verified and and using Cronbach's alpha coefficient. In order to analyze the research data using confirmatory factor analysis methods, the neural network of multilayer perceptron, neural network combined with genetic algorithm and neural network combined with particle swarm algorithm have been used. The results show that all three methods are able to predict the growth of the company. Among these three methods, the best predictive method for growth of the company is the neural network combined with the particle swarm algorithm with the least error rate compared to the other two methods.

Cites

No record.

References

No record.

Cite

APA: Copy

ebrahimkhani, hamed, KAZEMI, MOSTAFA, POOYA, ALIREZA, & Fakoor Saghih, Amir Mohammad. (2019). Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm. MODIRIAT-E-FARDA, 18(60 ), 205-220. SID. https://sid.ir/paper/401753/en

Vancouver: Copy

ebrahimkhani hamed, KAZEMI MOSTAFA, POOYA ALIREZA, Fakoor Saghih Amir Mohammad. Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm. MODIRIAT-E-FARDA[Internet]. 2019;18(60 ):205-220. Available from: https://sid.ir/paper/401753/en

IEEE: Copy

hamed ebrahimkhani, MOSTAFA KAZEMI, ALIREZA POOYA, and Amir Mohammad Fakoor Saghih, “Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm,” MODIRIAT-E-FARDA, vol. 18, no. 60 , pp. 205–220, 2019, [Online]. Available: https://sid.ir/paper/401753/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops