E-governance (G2C) in the public sector

Marzooqi, Samira Al; Nuaimi, Eiman Al; Qirim, Nabil Al

doi:10.1145/3018896.3025160

Cited by 10 publications

(1 citation statement)

References 4 publications

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“…E-governance uses ICT tools at various levels of interaction in internal government operations and between government and public sector to simplify and enhance democratic governance [145]. Through e-governance, government services, information exchange, and business transactions between governments, businesses, and citizens will be made available in a more convenient, efficient, and transparent manner [146].…”

Section: Or Peer Review 19 Of 29mentioning

confidence: 99%

Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means

2021

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The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

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Section: Or Peer Review 19 Of 29mentioning

confidence: 99%