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Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means
Sustainability ( IF 3.3 ) Pub Date : 2021-03-07 , DOI: 10.3390/su13052876
Anne Parlina , Kalamullah Ramli , Hendri Murfi

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.

中文翻译:

使用基于深度自动编码器的模糊C均值揭示智能可持续城市研究中的新兴趋势

讨论智慧城市的概念,技术和基于ICT的城市创新方法的文献不断增长,并且来自世界各地的城市也在积极竞争以改善其服务并使其变得智能和可持续。但是,目前尚缺乏能够提供全面理解并揭示明智且可持续的城市研究趋势和特征的研究。同时,决策者和从业者都需要追求渐进式发展。针对这一缺陷,本研究提供了基于主题建模方法的内容分析研究,以捕获有关智能和可持续城市研究的科学文献中主题的演变和特征。更重要的是,一种基于深度学习和聚类技术的新颖主题检测算法,介绍了基于深度自动编码器的模糊C均值(DFCM),以分析研究主题趋势。与以前使用的主题检测方法(即非负矩阵分解(NMF),潜在狄利克雷分配(LDA)和基于特征空间的模糊C均值(EFCM))生成的主题相比,该算法产生的主题具有相对较高的相干性值。 。使用DFCM算法在主题建模中出现的30个主要主题被分为六个组(技术,能源,环境,交通,电子政务以及人力资本和福利),它们代表了智能,可持续城市研究的六个维度。与以前使用的主题检测方法(即非负矩阵分解(NMF),潜在狄利克雷分配(LDA)和基于特征空间的模糊C均值(EFCM))生成的主题相比,该算法产生的主题具有相对较高的相干性值。 。使用DFCM算法在主题建模中出现的30个主要主题被分为六个组(技术,能源,环境,交通,电子政务以及人力资本和福利),它们代表了智能,可持续城市研究的六个维度。与以前使用的主题检测方法(即非负矩阵分解(NMF),潜在狄利克雷分配(LDA)和基于特征空间的模糊C均值(EFCM))生成的主题相比,该算法产生的主题具有相对较高的相干性值。 。使用DFCM算法在主题建模中出现的30个主要主题被分为六个组(技术,能源,环境,交通,电子政务以及人力资本和福利),它们代表了智能,可持续城市研究的六个维度。
更新日期:2021-03-07
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