Gaur, Loveleen and Singh, Gurmeet and Solanki, Arun and Jhanjhi, Noor Z. and Bhatia, Ujwal and Sharma, Shavneet and Verma, Sahil and Kavita, - and Petrović, Nataša and Muhammad, Fazal I. and Kim, Wonjoon (2021) Disposition of youth in predicting sustainable development goals using the Neuro - fuzzy and random forest algorithms. Human-Centric Computing and Information Sciences, 11 . NA. ISSN 2192-1962
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Abstract
This paper evaluates the inclination of Asian youth regarding the achievement of Sustainable Development
Goals (SDGs). As the young population of a country holds the key to its future development, the authors of this study aim to provide evidence of the successful application of machine learning techniques to highlight
their opinions about a sustainable future. This study’s timing is critical due to rapid developments in technology which are highlighting gaps between policy and the actual aspirations of citizens. Several studies
indicate the superior predictive capabilities of neuro-fuzzy techniques. At the same time, Random Forest is
gaining popularity as an advanced prediction and classification tool. This study aims to build on the previous research and compare the predictive accuracy of the adaptive neuro-fuzzy inference system (ANFIS) and
Random Forest models for three categories of SGDs. The study also aims to explore possible differences of
opinion regarding the importance of these categories among Asian and Serbian youth. The data used in this study were collected from 425 youth respondents in India. The results of data analysis show that ANFIS is better at predicting SDGs than the Random Forest model. The SDG preference among Asian and Serbian youth was found to be highest for the environmental pillar, followed by the social and economic pillars. This paper makes both a theoretical and a practical contribution to deepening understanding of the predictive power of the two models and to devising policies for attaining the SDGs by 2030.
| Item Type: | Journal Article |
|---|---|
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management |
| Divisions: | School of Business and Management (SBM) |
| Depositing User: | Shavneet Sharma |
| Date Deposited: | 24 Jun 2021 01:25 |
| Last Modified: | 24 Jun 2021 01:25 |
| URI: | https://repository.usp.ac.fj/id/eprint/12807 |
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