Geographic Information System (GIS)
Hossein Etemadfard; Hamed Kharaghani; Mahdi Najjarian; Rouzbeh Shad
Abstract
Extended AbstractIntroduction:The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant ...
Read More
Extended AbstractIntroduction:The increasing demand for sustainable food consumption as well as the change in the consumption pattern has led to efforts to improve the food distribution process. This is to speed up service delivery and prevent the spoilage of perishable materials. Among the most significant topics in the food supply chain is perishability, a phenomenon that occurs in certain categories of products such as fruits, vegetables, and dairy products. Perishability refers to the property in which a product loses its commercial value and usability after a certain period. However, meeting the general needs of citizens, especially the supply of food, is one of the most significant axes of urban service activities on the city's economic platform. In addition, the provision of comfort and well-being for residents depends on the proper establishment, optimal distribution, and sufficient variety of products offered in shopping centers. Day markets as well as fruit and vegetable fields provide fast and appropriate daily needs for residents. In addition, choosing fast and reliable routes for food distribution in the city is one of the other significant and influential factors in providing quality services. It should also be noted that in vehicle routing problems (VRP) related to food products, routes for vehicles must be created that match the schedules of some stores to deliver products.Materials and Methods:To optimize the fruit and vegetable distribution routes between the fruit and vegetable fields and Shahre-ma stores in Mashhad, this research will use genetic algorithms and particle swarm algorithms. This research will have the aim of optimizing distribution time, which was not addressed in previous research. This research presents its innovation by considering a three-hour time limit in the problem-solving algorithm. Genetic Algorithm (GA) is a learning method based on biological evolution and influenced by the hypothesized mechanism of natural selection in which the fittest individuals in a generation survive longer and produce a new generation. And in this article, it is implemented in such a way that the algorithm itself determines the most appropriate number of vehicles. The number of vehicles should be such that distribution among all stores is done in less than three hours and five minutes in each store. There should be a stop. And if distribution among all stores is not done in less than 3 hours, a new vehicle will be added to the number of vehicles. Also, particle swarm optimization (PSO) is a technique inspired by the behavior of birds when searching for food. In this research, the data collected include the location of Shahre-ma stores and the fruit and vegetable square in Mashhad city. These data were prepared from the information of Mashhad municipality. Also, to implement these algorithms, MATLAB software has been used. Network analysis has been done to determine the distance between Bar Square and Shahre-ma stores in ArcGIS software using network analysis.Results and discussion:This research proposes several hypotheses, including that the maximum optimal time is 3 hours and products should be distributed by 7 am in all places. Also, city traffic is uniform from 4 to 7 in the morning and the same product package is distributed in all stores. Comparing the results of two genetic algorithms and particle swarm shows that the genetic algorithm has a higher efficiency in optimizing the distribution path of fruits and vegetables. Because the time of the four routes derived from the genetic algorithm is approximately 92 minutes, 84 minutes, 80 minutes, and 82 minutes respectively. The total length of all routes is 127 km and 779 meters and the total time of all routes is 338 minutes. And the time of the four routes obtained from the particle swarm algorithm is approximately 102 minutes, 103 minutes, 89 minutes, and 91 minutes respectively. The total length of all routes is 175 km and 390 meters and the total time of all routes is 385 minutes. And in total, the times obtained for four vehicles in the genetic algorithm were 47 minutes less than the particle swarm algorithm. In addition, the total length of the paths in the genetic algorithm was 47 km and 611 meters less than the particle swarm algorithm.ConclusionThe genetic algorithm was able to achieve the optimal solution by evaluating the objective function 12,000 times. This is 2,900,000 in the particle swarm algorithm. Accordingly, the time required to reach the optimal solution differs significantly between the two algorithms.
Saied Sadeghian; Asghar Milan Lak; Hamed Ahmadi Masine; Roohollah Karimi
Abstract
Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ...
Read More
Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ground control pointsnecessarydespitetheexistenceof GPS/IMU data. Therefore, reducing systematic errorsin data naturally increases the accuracy of triangulation and reduces the number of ground control points required forblock adjustment andthe number of crossstrips used to eliminate systematic errorsin GPS data.
Materials
Digital images captured by the National Cartographic Centerof Iran from an area in Fars province usingUltraCam-Xpcamera in2010 were used in the present study to investigate the roleof self-calibration parameters in the reduction of ground control points and cross strips requiredfor block adjustmentin aerial triangulation. The intended block consists of 58 images and four strips; two of which are cross strips. Control points in this block include eight horizontal control points, eight vertical control points and eight full control points. Each image has a dimension of 11310 by 17310 pixels, a pixel dimensionof 6 microns, afocal length of 10500 microns, an end lap of 70%, and a side lap of 30%. Theregion has an average elevation of 760 m. Given the focal length, flight height and pixel dimensions, ground resolution is around 12 centimeters. Each image covers anarea of 2077.2 mlength and 1357.2 mwidth on the ground.
Methodology
The present study investigates theroleof self-calibration parameters, such as elimination of systematic error in GPS/IMU data and image sensor,in increased accuracy oftriangulation, and reduced number of ground control points and cross strips required for block adjustment. To reach this aim, optimal self-calibration parameters are determined using a genetic algorithm and the identified parameters are used in the bundle block adjustment. Variance components estimation method was used to solve the problem of equationsinstability. This method not only stabilizes the equation, but also determines the optimal weight matrix during the adjustment process.
Results and Discussion
Since images at a scale of 1:2000 were used in the present study, maximum RMSE equals 60 cm and maximum residual errorsequal 1.2 m. Using additional parameters to eliminate systematic errors results in an acceptable maximum error at the control points, but absence of additional parameters results in an unacceptable maximum error at the horizontal and vertical control points even in the presence of crossstrips. In addition to the evaluation of horizontal and vertical errors at the ground control points, horizontal and vertical RMSE of the checkpointsare also used to evaluate the geometric accuracy of aerial triangulation. Again, applying additional parameters keeps the RMSE at a much lower level than the accepted limit, while absence of additional parameters results in a horizontal and verticalRMSE higher than the accepted limit even in the presence of cross strips. It should be noted that using cross strips reduces RMSE at the vertical component.
Conclusion
Results indicated that using self-calibration parameters and reducing errorsin data used for the adjustment process decreases the number of control points and cross strips required for block adjustment.Using optimal self-calibration parameters(even in the absence of control points) resultsin a maximum RMSE of 0.143 m at the checkpoints, while absence of these parameters results in a maximum RMSE error of around one meter with or without cross strips. Genetic algorithm is capable of determining optimal self-calibration parameters. It is also capable of optimizing nonlinear functions. Therefore, it is not necessary to linearize the equations before determination of self-calibration parameters, which reduces the amount of necessary calculations. Variance components estimation can also be used along with the bundle block adjustment method to stabilize the equations and determine the optimal weight matrix. As a result, it is suggested to take advantage of these three methods, i.e. block adjustment, stabilization and optimal weight matrixdetermination, simultaneously.
Nikrouz Mostofi; Hossein Aghamohammadi Zanjiirabad; Alireza Vafaeinezhad; Mahdi Ramezani; Amir Houman Hemmasi
Abstract
Introduction Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature ...
Read More
Introduction Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries. Materials and Methods Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed: a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters. Result and Discussion The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).
Mehrdad Kaveh; Mohammad Saadi Mesgari
Abstract
Extended Abstract Introduction Site selection for health centers and hospitals in proper locations and the allocation of population to them is an important issue in urban planning. The location and allocation of health and medical facilities including hospitals, have long been an important issue ...
Read More
Extended Abstract Introduction Site selection for health centers and hospitals in proper locations and the allocation of population to them is an important issue in urban planning. The location and allocation of health and medical facilities including hospitals, have long been an important issue for urban planners that has become more complicated with the growth of population. Location and allocation of hospitals is basically planned to ensure the availability of proper and comprehensive health services as well as the reduction of the establishment costs. Improper planning of the health centers has created multiple problems for big cities in developing countries in recent years. In the present study, the Genetic Algorithm (GA), Hybrid Particle Swarm Optimization algorithm (HPSO), Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used for selecting proper sites of hospital and allocating the demanded locations to these centers in District 2 of Tehran. Materials & Methods The main goal of this research is to compare and evaluate the performance of the Genetic Algorithm (GA) and Hybrid Particle Swarm Optimization algorithm (HPSO) for determining the optimal locations of hospital centers and allocating the population blocks to them. In order to limit the search space, the analyzing capabilities of the Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used to select the candidate sites satisfying the initial conditions and criteria. The locations of such candidate centers are the input of the optimization section. The accuracy of the entire process strongly depends on the selection of these candidate sites. Hence, in this paper, the Analytic Hierarchy Process (AHP) method has been used to select the candidate centers. Then, two optimization algorithms were applied in choosing six optimum sites from the candidate locations and allocating the population to them through minimizing the overall distances between the centers and their allocated blocks. In this study, to improve the Particle Swarm Optimization, a simple neighborhood search has been proposed for better exploitation of the elite particles. The main purpose of this neighborhood search is to increase the convergence rate of the algorithm without decreasing the random search. Since the neighborhood search has a specific definition proportional to each issue, and the issues of location and allocation are spatial issues as well, therefore, the geographic principle of appropriate distribution of the centers in space has been used to define the neighborhood search (the distance between the centers should not be less than a certain amount). In an elite particle, two centers with the lowest distance are selected and one of them is replaced by a new and randomly selected center. If such a change provides a better objective function, the newly created solution in the elite particle is replaced. To calibrate the algorithms parameters, a simulated data set has been used. Having proper values for those parameters, the algorithms were tested on the real data of the study area. Results & Discussion Given the results of algorithms on real data, the performances of both algorithms are highly dependent on the initial population and the allowed number of iterations. In general, lower numbers of iterations and more populations brings better results than the higher iterations and lower populations. The results show that the Hybrid Particle Swarm Optimization (HPSO) has better performance than the Genetic Algorithm (GA). The convergence rate of the Hybrid Particle Swarm Optimization (HPSO) algorithm is faster than the genetic algorithm (GA), which can be attributed to the particle’s motion toward the best personal and global experiences. Furthermore, the proposed neighborhood search has caused the HPSO algorithm to converge earlier. To evaluate the repeatability of the algorithms, they were performed 40 times for both simulated and real data. Both algorithms have displayed high levels of repeatability, but the Hybrid Particle Swarm Optimization (HPSO) algorithm is more stable. However, the use of Genetic Algorithm (GA) on simulated data has shown more stability than its use on real data. For both the simulated data and real data, the Hybrid Particle Swarm Optimization (HPSO) algorithm performs faster than the Genetic Algorithm (GA). Conclusion Simplicity and repeatability of the algorithm are among the important factors which are very significant from the user’s point of view. In this research, the HPSO algorithm has not only been repeatable and simple, but has performed faster than the GA. Therefore, considering these criteria, regarding the special case of this research, the HPSO seems to be more promising than the GA.
Alireza Arofteh; Taher Reza Mohammed; Ali Hossingholizade; Ehsan Hoghoghi fard
Abstract
Increasing development of urban areas, the need for various information from the urban environment, and also technological advancements have increased the importance of automatic and semi-automatic classification and identification of this type of land cover. The diversity of remote sensing data have ...
Read More
Increasing development of urban areas, the need for various information from the urban environment, and also technological advancements have increased the importance of automatic and semi-automatic classification and identification of this type of land cover. The diversity of remote sensing data have created a wide scope for urban feature detection. Moreover, by launching satellite sensors with a spatial resolving power of less than 1 meter, a dramatic revolution has occurred in the tendency of remote sensing researchers toward classification of urban features. The existence of various features and different applications of spatial information in urban areas have made it possible to integrate various data sources with the aim of identifying different urban features. The present study seeks to integrate optimal properties extracted from optical and LiDAR data in order to identify urban features in the study area. In this regard, colored features, normal difference vegetative index (NDVI), first-order statistical texture in three windows of 5×5, 7×7 and 9×9, second-order statistical texture in three windows of 7×7, 11×11 and 15×15 extracted from the multispectral optical data were calculated along with features of normalized difference index (NDI), slope, slope direction, profile curve, surface curve, roughness, variance, laplacian, smoothness and normalized digital surface model (nDSM) extracted from the LiDAR data. Since increased amount of information has made the process of identifying features in the region time-consuming, the present study applies intelligent genetic algorithm to select optimal features from the calculated features. A total number of 361 features were produced from this data, including 9 colored features, a vegetation index, 144 first-order statistical texture, and 192 second-order statistical texture from multispectral optical data and 14 features from LiDAR data. Then, 17 features including seven features of the LiDAR data and 10 features of the multispectral optical data were determined using genetic algorithm as the optimal features for more appropriate identification of urban features. Finally, support vector machine (SVM) classification method was used to identify the desired features. Results indicate that compared to LiDAR data, multispectral optical data have a better performance in classifying vegetation features, while LiDAR data have been more suitable for the classification of road and building features. In other words, multispectral optical data work appropriately in identifying features with different radiometric information, while classification of features with similar radiometric information, such as roads and buildings is problematic. Thus, LiDAR elevation data help in identification of these features. Additionally, using optimal features along with the primary bands have increased the accuracy of urban features classification. Using optimal features and initial data, the accuracy of support vector machine algorithm classifier in the study area is calculated to be 88.734, which shows 25.438% improvement compared to the initial multispectral optical data classification, and 18. 236% improvement compared to the initial LiDAR data classification.
Mostafa Kheyrollahi; saeed nadi; Najmeh Neisany Samany
Abstract
Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time ...
Read More
Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time of arrival of an emergency vehicle, some of which are qualitative or quantitative, dynamic or static. In this paper, the modern approach used, is based on composing conflation models, Gamma quantification methods, travel time prediction formulas and meta-heuristic algorithms in order to find most optimal route. In this paper, first we have tried to introduce all the calculated, available, qualitative and quantitative, affecting factors related to emergency routing, thenwith converting qualitative parameters to quantitative ones, we normalize each parameter by the maximum approach and conflate them in such a way that thepriority and impact of each parameteris determined to find the optimal route. In order to calculate the priority and impact of factors, the Gamma test method, as a data derived method is selected. The procedure is implemented by the use of road network and traffic volume data from two regions of Tehran. Based on this approach, the considered weights for each following criterion of degree of difficulty including quality, width, slope, category, and route directness are 0.331, 0.286, 0.188, 0.172 and 0.020, respectively. Finally, genetic meta-heuristic algorithm is used to select the optimal route and the results compared with common Dijkstra routing algorithm. The length of the selected route by GA is about 130 meters in one time and about 300 meter in the other time more than the selected one by Dijkstra algorithm. Based on the implemented comparison, the represented approach in this paper had a considerable superiority over the simple current methods.
Hadi Babapour; Mahdi Mokhtarzadeh; Mohammad Javad Valadanzoj; Mahdi Modiri
Abstract
The importance of spatial-referenceddata in all developmental and research affairs is not overlooked. Among the methods for the preparation and production of spatial data, the photogrammetry method has a unique position due to speed, cost-effectiveness and above all, the lack of need to direct human ...
Read More
The importance of spatial-referenceddata in all developmental and research affairs is not overlooked. Among the methods for the preparation and production of spatial data, the photogrammetry method has a unique position due to speed, cost-effectiveness and above all, the lack of need to direct human presence on the site. In photogrammetric method, airborne cameras play a key role in the success and achievements of other stages, as the main means of providing input data and the first operational loop. Today, technological advances have led to the presentation of high quality digital cameras that promise the provision of the required spatial information by photogrammetric method with high accuracy, speed and efficiency. Given the emergence of new digital cameras and the variety of construction and technology used in these types of cameras, the need for their calibration is recognized as a primary requirement. Considering the high costs and executive problems with performing laboratory calibration, the use of self-calibration equations is considered as one of the most useful solutions in this field. For this purpose, in this paper, the use of Fourier equations with optimal terms derived from the genetic algorithm was proposed, and was evaluated and compared with previous models on the simulated data. Based on the results, this model is able to model multiple distortions with minimal dependency. The accuracy presented for modeling multiple distortions in simulated images of the Ultra Cam digital camerashows an about 30% improvement in modeling accuracy with the least dependency,compared with other additional parameters.
Hamid Reza Ranjbar; Ali Reza Azmoude Ardalan; Hamid Dehghani; Mohamad Reza Serajeyan; Ali Alidousti
Abstract
Earthquake is one of the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster ...
Read More
Earthquake is one of the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster area. On the other hand, to deal with the situation requires well organized and effective emergency planning. How quickly the event is responded and how efficiently response activities are managed are the main determinants of the overall costs of a disaster, both in terms of economic damages and fatalities. Remote sensing techniques play an important role in obtaining building damage information because of their non-contact, low cost, wide field of view, and fast response capacities. Now that more and diverse types of remote sensing data become available, various methods are designed and reported for building damage assessment. This paper provides a comprehensive review of these methods based on using optical images in three categories: mono, multi temporal and combination of images and vector map approach and also implements an automatic damage assessment method of buildings using high resolution satellite images and GIS layers. In this method, after extracting texture features of candidate buildings from both pre- and post-event images and defining optimized features, a neurofuzzy inference system was designed that determines buildings to four damage levels: Undamaged, Moderate damaged, Heavy damaged and Destroyed levels. Evaluation results show that the designed system has the overall accuracy of 89% in classifying buildings to the four damage levels.
Parviz Ziaeian Firuzabadi; Alireza Matkan; Vahid Babazadeh
Volume 19, Issue 73 , May 2010, , Pages 86-93
Abstract
In this research, a method for extracting effective and interpretable fuzzy rules from GIS data using a neuro-fuzzy system is presented. The fuzzy model has passed through three stages to achieve high accuracy and interpretability. In the first stage, the primary weights of the neuro-fuzzy network were ...
Read More
In this research, a method for extracting effective and interpretable fuzzy rules from GIS data using a neuro-fuzzy system is presented. The fuzzy model has passed through three stages to achieve high accuracy and interpretability. In the first stage, the primary weights of the neuro-fuzzy network were obtained using the FCM clustering algorithm. In order to categorize the educational data in the second phase, a neuro-fuzzy CANFIS system was used and genetic algorithms were utilized to overcome the fuzzy models loss of interpretability. The proposed method has been tested on the data of 5th and 11th districts of Tehran for the diagnosis of decayed tissues. The issue at hand is of the type of classification and the aim is to determine the degrees of membership of the textures in each of the classes. The decay of tissues has been examined in 4 categories including low, moderate, high and very high decay. A total of 300 educational samples were used, and after network training all data were categorized correctly and with RMS = 0.0045. The results show that the proposed method in this study has high accuracy and interpretability and is capable of generalization to issues in which sufficient knowledge of the target system is not available.
Mahdi Modiri
Volume 17, Issue 68 , February 2008, , Pages 2-8
Abstract
In order to conduct spatial analysis of clustering, the principles and characteristics of genetic algorithms are utilized. The present paper presents a new method of spatial analysis of clustering based on genetic algorithm. The results of the scientific and practical experiments show that this method ...
Read More
In order to conduct spatial analysis of clustering, the principles and characteristics of genetic algorithms are utilized. The present paper presents a new method of spatial analysis of clustering based on genetic algorithm. The results of the scientific and practical experiments show that this method can maintain the overall characteristic of distribution and obtain an appropriate result.
