جستجوی مقالات مرتبط با کلیدواژه « Fuzzy Inference System » در نشریات گروه « عمران »

Spatial equity of urban health (USEH) is closely related to quality-of-life standards and urban development. Its evaluation is very important in the performance of public health and urban planning.  The aim of this research is to develop an approach to prepare and evaluate the USEH map using two data sets including reference location information and Volunteered Geographic Information (VGI).  To determine the accuracy of the maps obtained from both types of data sets. This research was carried out in district 6 of Tehran metropolis. In this research, the effective criteria of the amount of USEH were first determined. The criteria map was prepared with spatial analysis and multi-criteria decision-making, and the final USEH map was prepared by combining the criteria map. To validate the prepared map, the USEH map was again produced based on the VGI information provided by the citizens and compared with the map obtained from the reference data. The amount of USEH for citizens is classified into 5 levels, from very suitable to very unsuitable. Based on the USEH map obtained from the reference data, approximately 62% of the study area has good or very good spatial equity. Comparison of the VGI map with the reference map showed a 72% match. Determining and evaluating USEH for different regions using new methods and technologies is a fundamental step to help health decision-makers in order to manage and allocate resources. It is expected that the results of the present research will be considered an essential criterion for decision-making in the field of health and increasing spatial equity in citizens' enjoyment of health facilities and components.

Chronic obstructive pulmonary disease restricts airflow in the respiratory system. After the age of 40 or 50, the symptoms of this disease become more apparent. According to studies, there were 251 million cases of COPD worldwide in 2016, which is expected to become the third leading cause of death in 2030. The symptoms of this disease include shortness of breath, chronic coughing, and phlegm over several days. While COPD cannot be cured, it can be identified by its symptoms, which will help to improve quality of life and reduce mortality risks. The fundamental principle in this type of disease is early diagnosis and timely access to medical care (including hospitalization). With the advancement of smart and remote technologies in many fields, especially health and treatment, humans have been able to improve and solve problems more efficiently. In recent years, context awareness has become one of the new and practical concepts in the area of health diagnosis and treatment, due to its combination with fuzzy expert systems. Based on this, a system can be context-aware, which includes information to describe the activities of users of a program, so the information is the context. Because the diagnosis process is patient-centered, this information provides them with the possibility of quick notification of abnormalities, which in turn prevents wasted time and money. As a result, this information provides a suitable background for accurate and early prediction of abnormalities for efficient treatment. It seems necessary to design a system that compensates for the previous shortcomings and uses modern concepts of context and context awareness since no study has been conducted regarding the diagnosis of COPD disease by tissue-awareness method and direct communication with the user has not been used in their treatment methods.

The purpose of this model is to create high-level context information based on low-level data. To achieve this goal, low-level context data are converted into different fuzzy sets that form inputs for the fuzzy inference engine, and then they are converted into high-level meaningful information, and based on this knowledge base, conclusions are drawn about the risk level of the disease. There are several factors that can be used to diagnose the level of COPD, including: 1) Air pollution level 2) Disease symptoms 3) Age 4) Cold and hot environmental conditions (temperature). In order to investigate the effective tissues in determining the severity of the disease, 60 patients with chronic obstructive pulmonary disease during one year (2018) in 25 medical centers of Ilam city were asked to provide their medical history and personal characteristics, as well as they completed their questionnaire under the supervision of a doctor. The slope of belonging to a set slowly changes into "not belonging to it" in fuzzy sets, unlike classical sets. There are four basic steps in a fuzzy inference system: fuzzification, knowledge base, decision making unit, and defuzzification. Using the existing rule base, the decision-making unit obtains the diagnostic levels of COPD disease (normal, moderate, severe), which can then be used with the de-fuzzification of the outputs. All the data received from the patients were entered into the fuzzy system and the output was compared with the recorded diagnosis of the specialist doctor to evaluate the system. In order to evaluate and perform the fuzzy system in the diagnosis of COPD, the Kappa test, accuracy, sensitivity, and F1 scores were used.

The determination of effective individual and environmental contects for diagnosing chronic obstructive pulmonary disease was first prepared using library research and risk assessments of determining factors, and then based on a lung disease specialist's opinion. As a result, the fuzzy expert system was designed based on the parameters of the symptoms (such as coughing, chest phlegm, feelings of heaviness in the chest, breathing, sleeping, energy level, range of physical activity, relief from leaving the house - despite the physical condition), air pollution level, age, cold and hot environmental conditions (temperature), etc. After determining the membership functions, it is necessary to create the rule base. According to four system inputs, 183 "if-then" rules were performed. By using the Kappa statistical test, we compared the fuzzy expert system's diagnosis with that of the relevant doctor's final diagnosis. The Kappa statistical test had a value of 0.77, which indicated a very strong relationship between the variables. In the confusion matrix, which is used to evaluate the model's performance, the accuracy value was equal to 0.8167, the sensitivity value was equal to 0.8182, and the F1 score was equal to 0.8791.

A fuzzy context-based expert system is a new approach that provides accurate and quick information to patients by inferring individual and environmental contexts. The purpose of this study is to gain a deeper understanding of the user's current condition and to determine the level of their illness with the addition of the user's participation in receiving personal and environmental contexts, as well as his participation in decision-making. The approach presented in this article can be repeated and implemented in other situations. This method can be applied in different regions and cities or different division levels such as districts or provinces.

Classification of soil is a function of the geological conditions of the region, which according to the 2800 earthquake standard, is itself a function of average shear wave velocity, average standard penetration number and average undrained shear strength in cohesive soils in different layers to a depth of 30 meters from the surface. Due to the uncertainties in the mentioned parameters and also considering that the values of these parameters in the real material are not the same as the values obtained from the experimental tests, so for this and determination of these parameters requires the use of statistical and probabilistic methods. Due to the computational complexity of statistical and probabilistic methods, in this research, a fuzzy inference system has been used to the decision of the choice of soil type, which can consider uncertainties without the need for complex mathematical calculations. For this purpose, after determining the effective parameters in determining the soil type, triangular membership functions were selected for them and finally, a fuzzy inference system was designed. A comparison of the results of this study with the results of field research shows the efficiency of the proposed fuzzy inference system.

The first and the most important step in preparation of seismic retrofit plan for existing buildings is the analysis of their vulnerability by conducting retrofit studies. However, most of the existing buildings in Tehran are in urgent need of retrofit studies due to reasons such as high seismicity, upgration of building and seismic codes, the abundance of old buildings and so on. In these studies, it is very important to identify the seismic status of the buildings which are in the first priority of seismic retrofit, especially the ones with public use like schools. A seismic risk prioritization technique for steel buildings is proposed in this paper, using a risk assessment hierarchical structure and fuzzy inference system. Afterward, this technique is applied in a case study, validating the results obtained for the steel buildings of the schools in Tehran. At the first of the prioritization process, the required information of the buildings is classified according to the designed hierarchical structure; Then, after quantification of the qualitative data and the fuzzification, the data are modeled, evaluated and defuzzificated based on the fuzzy inference system; This process is performed for all stages of the hierarchical structure to obtain the seismic risk parameter. After the qualification, this parameter indicates the risk of buildings and their requirement for retrofit or rehabilitation. The results that are distinguished by urban districts, determined the high-risk steel school buildings requiring retrofit studies and have shown the role of each effective parameters on the seismic risk of the buildings.

Landslide is one of these natural hazards which causes a great amount of financial and human damage annually allover the world. Accordingly, identification of areas with landslide threat for implementation of preventive measures in order to confront against the instability of hillsides for reduction of potential threats and related risks is very important. In this research a new method for classification of landslide risk according to geographical analysis and uncertainty modeling is presented which is based on data mining in previous events. In order to do so, adaptive neuro-fuzzy algorithm which is adjusted by means of sensitivity analysis is used in inferential basis of proposed model, which analyze landside risk efficiently. The selected region for this study is available lands in Alborz province. In proposed method factors like altitude, petrology, gradient, gradient direction, distance to fault and rainfall which are some of the most serious causes of hillside's instability had been inserted and their raster maps produced in GIS context and stored in georeference database. In the next step, areas prone to landslide had been identified according to findings of proposed model and finally in addition to model evaluation according to validation outputs, another round of validation is done by field monitoring of hih-risk regions and interpretation of provided 3D models. Results show that the proposed model with root mean square error of 0.819 and correlation factor of 0.934 has a relatively high accuracy in classification of landslide risk. In addition in landslide risk geographical distribution map inside studied region, the area of landslide-prone area is the highest with respect to total area of province which shows high-risk of Alborz province against landslides.

Building extraction from different data resources, which can be applied in 3D city modeling, is one of the most growing research topics in Photogrammetry and Remote Sensing. The aim of this study is proposing a new method for extracting buildings from LiDAR point clouds using a fuzzy inference system. In the proposed method, first, a pre-processing step is performed to eliminate low altitude points and also noisy points. In this regard, an optimal noise removal technique is used to remove noise and outliers from LiDAR point clouds. The proposed method can almost eliminate all of the noises and outliers from the point clouds. After pre-processing step, only high altitude points in the form of points groups including buildings and trees will remain. Then, due to the uncertainty in distinguishing building groups from tree groups, a fuzzy inference system is designed and implemented. In the proposed fuzzy system, three geometric descriptors of "SumD", "Area" and "Volume" are considered as input variables, and feature type as output variable is defined. In order to evaluate the proposed method, a test area of Belgium is used, and the obtained results proved the ability of the proposed fuzzy inference system in resolving the uncertainty in detecting building points group. Moreover, the proposed noise removal method increased 10% in accuracy and improving the quality of the fuzzy system.
3D city Modeling using the technology of LiDAR is one of the most growing research topics in Photogrammetry and Remote Sensing.

Nowadays, powerful detection systems, which the learning procedure of them is black box and is not available, have been widely used to classify data. However, the understandability of the acquired knowledge from these detection systems can significantly help operator in carrying out classification performance with high accuracy and precision. Hence, knowledge acquisition in a form of fuzzy rule set is an important issue in the image processing that causes to comprehend the classification methods appropriately and to improve them subsequently. The purpose of this paper is proposing a method to extract fuzzy rules in IF-THEN form via an Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification LiDAR data and digital aerial images. Detection of building and tree in urban areas needs to determine some features to perform the detection procedure; because classification algorithms decide about pixel entity based on its feature vector. These features can make the object separation possible by the textural, the spectral, and the structural characteristics. Nowadays, by increasing the number of the active and passive sensors, it is possible to record the textural, the spectral, and the structural characteristics of objects in different wavelengths by various approaches. In this paper, some potentially features were generated, and then optimal features were selected using the genetic algorithm. Using the selected optimum features, an ANFIS was used to recognize the objects accurately. In this regard, at first, the prepared training data was utilized as inputs of grid partitioning algorithm and a Sugeno fuzzy inference system with one output was generated by determining the type and the number of input membership functions, as well as theS type of output membership functions. Then, the grid partitioning algorithm figured out the best state of the membership functions after investigating the whole of the possible states. Afterwards, the training and checking data were entered into the generated ANFIS and during the training procedure, the final classifier was concluded to detect buildings and trees. Finally, by proposing a different fuzzy-based method and using the selected training data, as well as the output membership functions of the proposed ANFIS, a set of effective fuzzy rules were extracted. The proposed method has three main steps. In the first step, the tuned premise parameters (after training process) of inputs training data of ANFIS were extracted according to the mean values of the membership functions. In the second step, firstly, based on the number of membership functions of each feature, the total number of feasible fuzzy rules was determined. Then, for each training data, the fired values for all rules were computed. The rule that had the most effect in the process was chosen as the fired fuzzy rule of each training data related to the desired object class. In the third step, the fuzzy rules which has the importance more than a specified threshold in the classification procedure were extracted. The extracted fuzzy rules were considered and analyzed logically regarding the feature layers, and the results show the high capability of the fuzzy-based proposed method in extracting rules from the objects detection procedure.

In GIS, data is organized in themes as spatial layers. One of the most significant tasks of GIS is to analysis, spatial layers in order to model spatial phenomena. Since spatial data in GIS are inherently uncertain, a system that handles and infers from such uncertain data is of vital importance. Insufficient consideration to spatial modeling can lead to several problems in spatial decision making, death toll, property damage, financial loss and other hardships.There is no analytical solution in most spatial modeling. For these modeling, methods inspired by nature sometimes work very efficiently and effectively. These biologically inspired methods are called Soft Computing. The modeling of mineral potential is one of the special cases of the problem of spatial modeling in which no comprehensive model has been developed for it. A mineral potential mapping which depicts the favorability of mineralization occurring over a specified area is an important process for mineral deposit exploration. The purpose of this paper is to suggest several soft computing methods such as a fuzzy inference system (FIS), neural networks and genetic algorithms in a GIS framework for mineral potential mapping.A typical FIS is composed of two main parts: The Knowledge Base (KB) and the Inference System. The KB composed of Data Base (DB) and Rule Base (RB) stores the available knowledge about in the form of linguistic “IF-THEN” rules. One of the major problems in constructing an FIS is to build the knowledge base. In the conventional design methods, the desired rules and functions are based on the expert's knowledge and experiences. However, we cannot perfectly represent the expert's knowledge by linguistic rules nor choose appropriate membership functions for fuzzy sets. Moreover, converting the expert's knowledge into if-then rules is difficult and often results are incomplete, unnecessary and include conflicting knowledge, since experts cannot express all their knowledge. These problems can be sorted out applying techniques to construct a fuzzy knowledge base of numerical input-output data. In this research, two methods of dividing the input-output spaces and neural network are implemented to deal with this problem.To evaluate the constructed FIS, the genetic neural network has been applied and its results compared to the results of the FIS. In a genetic neural network, genetic algorithm is used for neural networks in order to optimize the network architecture. As a matter of fact, the topology of the networks is encoded as a chromosome and some genetic operators are applied to find an architecture which fits best the specified task according to some explicit design criteria.Numerical experimentations showed that the genetic neural network used in this study is the most successful method. It could predict the characteristic of 85% boreholes correctly. The results also indicated that genetic neural network and FIS with RMSE of 4 and 15 respectively are more accurate methods.

A fuzzy predictive model is compatible with the multiclass and continuous nature of geodata and can properly handle inherent informational fuzziness in most geodata. Although the fuzzy based approaches are been used by different researchers and varied deposits, the structure and pattern of these method were almost similar. In this study, we produce a mineral potential map for the porphyry copper mineral deposit of Nochoun, south east of Iran. In order to improve the conventional methods, we used a fuzzy inference system (FIS) for preparing geophysical and geochemical factor maps. These factor maps are integrated by an appropriate inference networks and mineral potential map was produced. Results of the prepared mineral potential map are in a good accordance with drilling data (80.7 % in 5 class and 87.1 % in 3 class classification). Also, using FIS in preparing factor maps, has improved the compatibility of mineral potential map, (almost 10% in 3 classes and 7 % in 5 classes classification).

A fuzzy predictive model is compatible with the multiclass and continuous nature of geodata and can properly handle inherent informational fuzziness in most geodata. Although the fuzzy based approaches are been used by different researchers and varied deposits, the structure and pattern of these method were almost similar. In this study, we produce a mineral potential map for the porphyry copper mineral deposit of Nochoun, south east of Iran. In order to improve the conventional methods, we used a fuzzy inference system (FIS) for preparing geophysical and geochemical factor maps. These factor maps are integrated by an appropriate inference networks and mineral potential map was produced. Results of the prepared mineral potential map are in a good accordance with drilling data (80.7 % in 5 class and 87.1 % in 3 class classification). Also, using FIS in preparing factor maps, has improved the compatibility of mineral potential map, (almost 10% in 3 classes and 7 % in 5 classes classification).