جستجوی مقالات مرتبط با کلیدواژه « machine learning » در نشریات گروه « هنر و معماری »

Numerous scholars have examined the issue of examining urban layout in order to encourage social activities and walking to improve interactions and urban life. Therefore, it is crucial to pay close attention to structures that, on the one hand, encourage more walking in the city's public areas and, on the other hand, extend the length of walking in the public space. Because it improves the quality of life and health of the populace. Complicating the nature of growth and city structure patterns, we must pay attention to several aspects in order to identify the elements that influence the increase in citizen walking in diverse urban structures and use more complicated methodologies. Consequently, the questionnaire approach and machine learning employing the regression method will be used in this work. Using machine learning allows the researcher to investigate the intricate interactions between many factors. The objective of this study is to analyses the overt and covert interactions between numerous environmental elements that influence residents' walking in Shiraz's metropolitan area. The findings of this study indicate that the five factors of personal privacy, improving access to public spaces, free of garbage, high-quality architecture and connecting different elements, and sufficient opportunity for pedestrian movement have the greatest influence on the amount of time spent walking in the urban environment. The element with the least influence is supervision, which includes a larger police presence, a greater emphasis on personal safety and security, and a CCTV system. In general, the maximum amount of walking time per day may be predicted with roughly 92% accuracy using these 16 criteria. Managers, designers, and urban planners may apply the findings to promote urban vibrancy and activities, particularly walking.

This study presents an approach for the automatic spatial arrangements in creating more economical buildings. More than 60 years of studies in the field of production of automatic spatial layouts have proved that architectural layouts are strongly affecting the course of such benefits; nevertheless, they mainly examined the subject mathematically. The purpose of this research was to provide a new category in the production of architectural layouts while it investigated the related applications. In addition, it compared the existing approaches and methods. Finally, the study introduced a model for automatic generation of spatial arrangements.

From out of 105 reliable national and international databases, 34 studies on the production of spatial arrangements were selected and analyzed using the content analysis method.

The results indicated that the production of automatic spatial layouts could be organized in six approaches from the perspective of problem representation approaches. Additionally, the benefits and applications of each approach examined based on qualitative criteria.

At the same time that a general model was provided by the study, the automatic spatial architectural layout design was also established in three different methods of part to whole and whole to part relationships along with the principle of expertise and its applications.

The examination of users’ emotions through social media has developed into an impactful domain across diverse scientific fields, appealing not only to business proprietors and politicians but also to general users. In the meantime, this field has infiltrated urban studies and has been used by urban planners and designers due to its methodology; whether in the form of research that aims solely at emotion analysis or as an integrated layer within broader research endeavors. The aim of this article is to explain this field in the analysis of urban emotions as modeling methods in order to identify the position of this field in urban studies by examining the importance of emotion and the methods of its study in the city.

This research used the supervised machine learning approach and analyzed the sentiments of tweets related to eight major cities in Iran. The data collection consists of 930 tweets that were collected in a period of 10 years from 2011 to 2022. Initially, over 5000 tweets were collected, and during the tagging process, 80% of them were excluded due to their limited relevance to the city, emphasizing tweets related to urban space. The name of cities and tourist areas were searched to establish a balance between positive and negative data. The tweets are downloaded through Twitter streaming API and the metadata along with the text, including the number of retweets, number of likes and tweet ID, language and location. The data sets have been used for machine training after standard and normalization steps. In this research, the ratio of training data to testing data is 80 to 20. According to the supervised approach, the data were labeled by the researcher with three negative, neutral, and positive labels, and where the researcher had doubts, the opinions of two other experts were used. In general, both machine learning and deep learning have been used. In order to check the validity of the model and to test it, the confusion matrix has been used.

Firstly, the machine was trained based on 3 algorithms that were used in many research related to text sentiment analysis. Based on the test results presented on the confusion matrix, the accuracy of the trained machine in determining the polarity of the text in three polarities was defined. Among the three used algorithms, support vector machine and random forest have performed better than other algorithms. Given that the model’s highest accuracy was approximately 70%, deep learning was employed to train the machine in order to assess the potential for achieving improved results. In the following, machine learning with a convolutional neural network algorithm and a hybrid algorithm were considered. At first, the machine was trained using a convolutional neural network. The results of the accuracy of the model showed that the model is predictable by up to 75%. Next, an attempt was made to improve the predictive accuracy of the model by writing a hybrid algorithm based on the convolutional neural network. The architecture of this network is such that two types of data are considered as input to the neural network, text data and other features in the data set, including location, number of retweets, number of likes, city codes and searched content (as metadata). Therefore, based on this input and output (classification based on the polarity of the text by the researcher), the machine was trained and finally tested. As depicted in the structure of the hybrid algorithm, the significance of the text is assigned a weight of 90%, while the importance of metadata is weighted 10%. It should be noted that different percentages were given to the importance of each of the inputs and the predictability accuracy of the model was checked. As the model test results show, the designed algorithm has improved the predictability of the machine by 4%.

In this article, sentiment analysis based on model-oriented methods - machine learning and deep learning - was scrutinized, and therefore, while comparing it with traditional methods and lexical methods, the process of urban sentiment analysis was developed and the different levels of the process were described in detail. As stated, these methods have many advantages and can be useful for analyzing the current situation or predicting different urban projects. Besides, compared to traditional methods, they are less expensive, faster and have sufficient accuracy. According to the appropriate capability of the trained machine, this machine can predict the polarity of the data. This means that by using the text data published in social networks, it is possible to analyze the feelings of users. Certainly, when these data are geodatabases, there is also the capability to geolocate emotions. This approach allows for a swift, accurate, and cost-effective general assessment of city spaces. By identifying areas where users perceive negative emotions, the reasons can be investigated and addressed accordingly. This research has been innovative in two aspects, 1) preparing a collection of data related to the sentiment of Persian language users related to the city and 2) analyzing urban sentiment in the country using machine learning in the field of urban planning and design. Some limitations of this research include limited access to all data published on Twitter using Twitter streaming API; the small amount of available and relevant data; low use of Twitter by users due to filtering; and the unavailability of financial resources to prepare and use a larger set of data.

The issue of the physical vulnerability of neighborhoods against earthquakes is the subject of this research. Crisis management and smart crisis management are generally considered in three stages: before, during, and after the crisis. The management of a smart crisis in all three stages, with emphasis on preparedness and anticipation of vulnerability against disasters such as earthquakes, can provide a way to predict vulnerability and increase power in decision-making. The purpose of this research is to present a predictive model for the vulnerability of the physical context against earthquakes in District 1 of Tehran municipality using machine learning. The research method is analytical and quantitative. Some of the data was collected from GIS and some were extracted from the map analysis. According to the use of a supervised learning approach in this research, labeling was performed by researchers in five different spectrums. Decision tree algorithm, support vector machine (SVM), and multilayer neural network (MLP) were used as machine learning algorithms. The portion of training data for the test was considered to be 70 to 30. By examining the accuracy of the model by the confusion matrix, it was found that the decision tree algorithm with an accuracy of 99.50, sensitivity of 99.42, and error of 0.5 has better performance than the other two algorithms. Moreover, the neural network with an accuracy of 97.85, sensitivity of 97.57, and error of 2.15 showed better performance than support vector machine.

Today, researchers in the field of design and research are looking for approaches to upgrade machines from dumb agents to artificially intelligent agents, so that these machines can participate in design processes as collaborators and increase productivity in the processes. be designed; Therefore, the purpose of this research is to discover and present a method for applying this field in the learning process of designing and drawing facades and presenting a theoretical and practical model, mainly designing and drawing facades of residential buildings by machine with a supervised learning approach; Therefore, the question of this research is how to arrange the facade elements and apply the rules and regulations of the facade drawing by machine and how the theoretical and practical steps of this process are. Explanatory research method for conveying complex ideas and information is based on data collection through documentary studies, library studies, educational samples, mathematical calculations and formulas, and quantitative and qualitative data. In fact, the explanatory method seeks to present new knowledge or describe a process or develop a new scientific concept, and the results show that the automatic facade design intelligence can arrange the main building elements of the facade from architectural plans with the help of machine learning algorithm (KNN &  K-mean) To understand and read means and arrange the elements using distance measurement formulas and apply the national building rules and regulations related to facade design in the drawing process using expert systems.

The first step in developing the prevention and safety plan for cities is to identify the existing hazards and crises and to prioritize the risk factors according to historical records and scientific studies to explain the probability of danger. Flood is one of the most high-risk natural hazards that should be given particular attention in crisis management. The study aims to model and predict flood hazard according to the characteristics of Nowshahr port city in Mazandaran province using machine learning models. The criteria and indicators affecting flood hazard, including climate criteria (6-hour rainfall indicator), hydrological criteria (surface runoff height indicators, drainage density, and groundwater depth), topographic criteria (slope and altitude indicators), Soil science criteria (soil type indicator) and land use criteria (land use type indicator) were identified based on the literature review. The indicators were then converted into raster data in the ArcGIS environment and finally standardized by fuzzy method for using in models. K-nearest neighbor models (Fine KNN, Cubic KNN, Cosine KNN, Weighted KNN), decision tree (Fine tree, Medium tree, Coarse tree), ensemble learning (Subspace KNN)) and WLC were used in the present study for flood risk zoning. ROC method was used to validate the models and choose the best one based on assessing the accuracy and area below the diagram of the flood. The layers were prepared in the ArcGIS environment and transferred into MATLAB to perform the modelling process and define the coefficients. The results show that the ensemble learning model with an accuracy of 0.96 and the area below the graph of 0.90 is the best and the WLC model with the accuracy of 0.61 and the area below the graph of 0.62 is the weakest model of flood hazard prediction in Nowshahr. Therefore, the output result of the Ensemble (Subspace KNN) algorithm was chosen to present the final flood risk map.

This article examines artificial intelligence and its effects on industrial design. In today's world, artificial intelligence plays an important role in developments, and given the widespread changes in recent scientific and products developments, there is a need to become more familiar with the industrial design community. This article focuses on the Literature review on the impact of this technology on various design fields. The results indicate a better understanding of users with the help of this science that improves the level of project management and design process. The topic of personalization of various digital and industrial products is important and is followed in the design; artificial intelligence has helped this and has led to positive developments in terms of user-centricity and personalization of appliances. The results also point to the positive impact of this science on reducing human error and managing energy and energy resources to protect the environment, which are followed in industrial design. Threats to this science for humans are also mentioned in the article, the most important of which is the complete replacement of systems with human resources  and data gathering and analysis of users, which can be exploited. In an era where attention is paid to good user experience and convenience, focusing on the benefits of this science will result in the correct motion in a new direction where industrial design is located. Using the features mentioned above, designers gain a better understanding of needs and create more efficient products that will help the industry grow.