Analytic network process: Academic insights and perspectives analysis

Chen, Yong; Jin, Qiuxia; Fang, Hui; Liu, Hui; Hu, Jian; Wu, Yutao; Chen, Jie; Wang, Cheng; Wan, Yuehua

doi:10.1016/j.jclepro.2019.07.016

Cited by 87 publications

(58 citation statements)

References 129 publications

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“…Unlike traditional narrative literature reviews, which rely on the experience and knowledge of the researchers, bibliometric analysis provides a perspective that can easily be scaled from micro to macro levels [26], [27]. Bibliometrics is defined as measuring academic and organizational performance based on several indicators such as number of publications, number of citations, and collaboration networks [28].…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

et al. 2020

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Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords cooccurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details. INDEX TERMS Time-series classification, deep learning, remote sensing, signal processing, bibliometrics, research productivity.

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Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

et al. 2020

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“…Detailed applications of these steps are presented in the case studies (chapters 3-5). Detailed descriptions of the method can be found in Saaty (2001), Bottero and Ferretti (2010b), Molinos-Senante et al (2015), Chen et al (2019) and Mu and Stern (2018) among others.…”

Section: Figure 12 Structural Difference Between Ahp and Anpmentioning

confidence: 99%

“…ANP also facilitates arriving at participative solutions and achieving consensus in the resolution of multicriteria and multi-expert problems. Since its appearance, it has been widely used in the solution of complex problems of decision making (Chen et al 2019). In this dissertation, ANP has been selected as it considers the relationships of interdependence among elements of the model, typical in problems regarding sustainable development.…”

Section: Figure 12 Structural Difference Between Ahp and Anpmentioning

confidence: 99%

“…Since it allows complex, interdependent and feedback relationships between the elements (Saaty and Peniwati 2008) several authors have moved towards this approach. Following its introduction (Saaty 1996) its uses have increased over the years, especially since 2009 (Chen et al 2019). Evidence regarding the use of ANP is widespread in the literature for the evaluation and selection of alternatives in different fields.…”

Section: Introductionmentioning

confidence: 99%

“…This SLR was carefully designed to comply with traditional recommendations such as replicability and transparency, but also some specific ones proposed by some authors regarding AHP and ANP techniques. Two main reviews related to AHP (Dos Santos et al 2019) and ANP (Chen et al 2019) reported detailed overviews of how both techniques have been used and developed. The first one conducts an SLR on AHP supporting decision for SD from 2014 to 2018 (Dos Santos et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of regional development needs according to criteria based on the Sustainable Development Goals in the Meta Region (Colombia)

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When a regional government considers investing funds for the sustainable development of its region, it must consider the real needs of the population and the fulfillment of the Sustainable Development Goals (SDGs). The identification of needs should be carried out with the support of local stakeholders representing various social groups. This paper seeks to answer the question of how to guide public investment policies at the local level, such as improving education, health, transportation and others, to better meet the SDGs. To answer this question, a multi-criteria decision-making process is followed, whereby, once the needs are classified by investment areas, they are prioritized based on criteria derived from the SDGs. The problem is complex because of the difficulty of the local stakeholder engagement process and also because of the interrelationships and influences that arise between all the elements of the decision problem: criteria and alternatives. To address this complexity, the Analytical Network Process method combined with DEMATEL will be followed. This process is applied to the case study of the distribution of funds allocated by the Government of Colombia to the Meta Region (Colombia) with the participation of 48 stakeholders to identify needs and the support of six experts in sustainability and project management for their prioritization.

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Analytic network process: Academic insights and perspectives analysis

Cited by 87 publications

References 129 publications

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

A participatory multicriteria approach to evaluate strategic projects for local sustainable development.

Assessment of regional development needs according to criteria based on the Sustainable Development Goals in the Meta Region (Colombia)

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