Towards a patient satisfaction based hospital recommendation system

Tabrizi, Tannaz Sattari; Khoie, Mohammad Reza; Sahebkar, Elham; Rahimi, Shahram; Marhamati, Nina

doi:10.1109/ijcnn.2016.7727190

Cited by 14 publications

(22 citation statements)

References 12 publications

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“…SOMs have important advantages over other statistical models that assume the linearity of relationships between variables [47]. Thus, SOMs are more powerful than linear models for analyzing the properties of variables and are of singular interest for their topological representations [48].…”

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

“…Thus, SOMs are raised as a tool that the analyst has at his or her disposal to analyze, represent, and visualize data [71]. SOMs have a number of operational advantages: (1) they are suitable for exploratory analysis [60]; (2) they allow all original variables to be visualized simultaneously [47];…”

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

“…(3) they show much more powerful representations than methods using classical linear analysis [48]; (4) they cluster in a more robust and complete way than traditional descriptive methodologies [72], specifically than K-means [50]; (5) they allow an effective visual exploration and validation of the results [73]; and (6) facilitate a powerful visualization easy to interpret [47], maintaining the topological relationships of the data [48]. The SOM can be used with both quantitative and qualitative variables [68,74,75] and results similar to those achieved with a panel of experts [76] can be obtained, thus allowing for improved results and savings in operating costs in the process.…”

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

“…Thus, prototypes are generated by modeling patterns and trends in information [43]. The choice of the number of clusters or profiles to be reached has been a much-researched problem, with multiple different methods and criteria [47]. The use of a combination of such methods is common [77].…”

Section: Obtaining Profilesmentioning

confidence: 99%

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Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

2019

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In recent decades, the concept of urban density has been considered key to the creation of sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has been observed in defining valid measurement indicators universally. With the intention of identifying the variables that allow the best characterization of the shape of urban fabrics and of obtaining the metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence is proposed. The main objective of this paper was to evaluate the capacity and interest of such a methodology from standard indicators of the built density, measured at various urban scales, (i) to cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to obtain the metrics that characterize them with an identity. As a case study, this methodology was applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13 regular parameters to qualify urban shape and density. It was verified that the profiles obtained were more robust than those based on a limited number of indicators, evidencing that the proposed methodology offers operational opportunities in urban management by allowing the comparison of a fabric with the identified profiles.

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Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

Section: Introduction To Artificial Intelligence Methods Used In Resementioning

confidence: 99%

Section: Obtaining Profilesmentioning

confidence: 99%

See 2 more Smart Citations

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

2019

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“…Once the SOM model is created, clusters of local adaptation initiatives are defined through a Ward-Cluster analysis [89]. The number of profiles to be reached can be defined by means of different methods and well-differentiated criteria [90], and even the use of several of them at the same time is frequent [91]. There are methods to define the number of clusters or profiles with a statistical approach, using cohesion and/or separation metrics, generally based on dispersion measurements based on the sum of squares [92], with the Ball and Hall index [93] or Calinski and Harabasz [94], the Davies-Bouldin index (DB) [95], the Silhouette Coefficient [96], the Cubic Clustering Criterion (CCC) [91] or the dendrogram observation method [91] particularly worth highlighting.…”

Section: Classification Of Strategies Into Profilesmentioning

confidence: 99%

European Strategies for Adaptation to Climate Change with the Mayors Adapt Initiative by Self-Organizing Maps

et al. 2019

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The European Union (EU) has assigned municipal governments a key role in the transformations needed to achieve its climate and energy objectives. One of the main initiatives of the EU has been the “The Covenant of Mayors”, launched in 2008, with impacts beyond Europe due to integration with the “Global Covenant of Mayors for Climate and Energy”. This research focuses on local measures to adapt to climate change, verifying their differences between themselves, and aims to identify and characterize patterns in the different adaptation strategies examined. Further aims are (i) the collection of good practices, framed in the Mayors Adapt initiative, managing multidimensional data from the context and from its adaptation proposals; (ii) the classification of strategies in profiles and patterns using artificial neural networks based on the previous variables; (iii) the characterization and comparison of such profiles. The results substantiate the existence of several well-differentiated approaches, connected with their geographical context, vulnerability and politics. These results provide valuable information for its interpretation and for the planning of climate change adaptation actions, highlighting the value of the creation of networks of institutional collaboration targeted at each strategic framework.

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