A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning (2023)

Kumar, S., Shukla, A. K., Muhuri, P. K., & Danish Lohani, Q. M. (2023). CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning. Ecological Informatics, 77, Article 102206. https://doi.org/10.1016/j.ecoinf.2023.102206

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Kumar, Sandeep; Shukla, Amit K.; Muhuri, Pranab K.; Danish Lohani, Q. M.

Lehti tai sarja: Ecological Informatics

ISSN: 1574-9541

eISSN: 1878-0512

Julkaisuvuosi: 2023

Ilmestymispäivä: 11.07.2023

Volyymi: 77

Artikkelinumero: 102206

Kustantaja: Elsevier

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

DOI: https://doi.org/10.1016/j.ecoinf.2023.102206

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/89139

Tiivistelmä

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.

YSO-asiasanat: bruttokansantuote; kasvihuonekaasut; hiilidioksidi; päästöt; mallintaminen; ennusteet; koneoppiminen; sumea logiikka

Vapaat asiasanat: Atanassov intuitionistic fuzzy sets; Hausdorff distance; Yager's generating function; GDP prediction; fuzzy sets

Liittyvät organisaatiot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2023

JUFO-taso: 1