A Comparative Study Employing CIA Methods in Knowledge-Based Urban Development with Emphasis on Affordable Housing in Iranian Cities (Case: Tabriz)

nia, Behzad Ranjbar; Murgante, Beniamino; Qelichi, Mohamad Molaei; Rustaei, Shahrivar

doi:10.1007/978-3-319-62401-3_35

Cited by 5 publications

(3 citation statements)

References 17 publications

(9 reference statements)

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“…After that, the experts set the impact range for each pair of elements using linguistic values. Finally, the upper-and lower-order ideals describe a new set of linguistic values for the overall dependencies and influences [45,46]. Source: [47].…”

Section: Resultsmentioning

confidence: 99%

Investigating the Drivers of Knowledge-Based Growth Management Using Fuzzy MICMAC: A Case Study in Iran

2023

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Attention to a city’s physical development is necessary for urban development plans. In Iran, the rapid physical expansion of cities in the form of unplanned and unbalanced growth due to various reasons, including indiscriminate migration, is considered a fundamental problem. This phenomenon is known as urban sprawl, which is one of the results of this type of growth. The research aims to identify and prioritize the indicators of the realization of knowledge-based growth management (KBGM) in the city of Tabriz, using foresight methods to analyze the effective drivers. The fuzzy linguistic MICMAC method was used to analyze mutual effects by converting linguistic variables into fuzzy numbers. In addition, using the theory of fuzzy sets for experts participating in research, the relationships between the 32 research variables were investigated. This research showed that the city of Tabriz had a scattered, isolated, fast, and unplanned growth, and it has relative compatibility. Despite the possibility of development within the limits of urban growth, most of the construction activities were outside the boundaries of urban growth. Key factors affecting the management of knowledge-based growth in Tabriz city were identified. Based on our findings, the university’s reputation, government effectiveness, urban competitiveness, city branding, quality of life, and strategic planning will impact the knowledge-based development process in Tabriz.

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Section: Resultsmentioning

confidence: 99%

Investigating the Drivers of Knowledge-Based Growth Management Using Fuzzy MICMAC: A Case Study in Iran

2023

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“…The two categories are used for training the deep learning network; the error between the aforementioned value and the real value is calculated by the loss function (MSE, MAE, Huber Loss) using the backpropagation of the error in the neural network and constantly adjusting the weight of each convolutional layer of the network to complete the training of the model. With this process outlined in Figure 3, which represents the algorithm pipeline, the loss function determines the direction in which the model is trained [47].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

et al. 2021

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This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows us to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed, with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level.

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Section: Figure 3 Schema Proposedmentioning

confidence: 99%

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

Maria¹,

Fiumi²,

Mazzei³

et al. 2021

Preprint

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This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level.

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A Comparative Study Employing CIA Methods in Knowledge-Based Urban Development with Emphasis on Affordable Housing in Iranian Cities (Case: Tabriz)

Cited by 5 publications

References 17 publications

Investigating the Drivers of Knowledge-Based Growth Management Using Fuzzy MICMAC: A Case Study in Iran

Investigating the Drivers of Knowledge-Based Growth Management Using Fuzzy MICMAC: A Case Study in Iran

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

A System for Monitoring the Environment of Historic Places Using Convolutional Neural Network Methodologies

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