Urban land price is the primary indicator of land development in urban areas. Land prices in holly cities have rapidly increased due to tourism and religious activities. Public agencies are usually facing challenges in managing land prices in religious areas. Therefore, they require developed models or tools to understand land prices within religious cities. Predicting land prices can efficiently retain future management and develop urban lands within religious cities. This study proposed a new methodology to predict urban land prices within holy cities. The methodology is based on two models, Linear Regression (LR) and Support Vector Regression (SVR), and nine variables (land price, land area, distance to a river, distance to main roads, distance to heritage locations, distance to historical mosques, distance to commercial locations, distance to educational locations, and distance to hospital and clinics. Our findings showed that the SVR model had outperformed the LR model, where SVR achieved an accuracy of 82.9%.In contrast, LR has achieved 75.40%. Therefore, the presented models can assess land prices in holly cities like Al-Kufa. Furthermore, this tool can retain land pricing, land management, and urban planning in Iraq.
Abstract
Binary logistic regression model used in data classification and it is the strongest most flexible tool in study cases variable response binary when compared to linear regression. In this research, some classic methods were used to estimate parameters binary logistic regression model, included the maximum likelihood method, minimum chi-square method, weighted least squares, with bayes estimation , to choose the best method of estimation by default values to estimate parameters according two different models of general linear regression models ,and different s
... Show More
The clayey soils have the capability to swell and shrink with the variation in moisture content. Soil stabilization is a well-known technique, which is implemented to improve the geotechnical properties of soils. The massive quantities of waste materials are resulting from modern industry methods create disposal hazards in addition to environmental problems. The steel industry has a waste that can be used with low strength and weak engineering properties soils. This study is carried out to evaluate the effect of steel slag (SS) as a by-product of the geotechnical properties of clayey soil. A series of laboratory tests were conducted on natural and stabilized soils. SS was added by 0, 2.5, 5, 10, 15, and 20% to the soil.
... Show More
(11)
Abstract
The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.
In this, search th
... Show More
The past decades of the last century and until now have witnessed many crises in the housing sector, and these crises were the result of many problems that led to a weak quality and quantity of housing stock, especially for lowincome people, and Goal 11 of the Sustainable Development Goals states, "Make cities and human settlements inclusive, safe, and capable of enduring and being sustainable, we have prepared this research that attempts to answer the following question: Is the current empowerment in the housing sector real and efficient support for people with limited income? The research assumes that empowerment in the housing sector has an important and effective role in reducing housing problems, the research aims to reach the most imp
... Show More
(3)
(2)
The cities are not yet a temporal status in the process of the human and civilization development. The cities consider as a life pattern, this mean that people cannot live without cities, which surround us. Several phenomena like globalization, revolution of information, technical development, increased of consumption, do not confirmed without the living in cities. The urbanization – in its simple meaning – means transferring from rural community to city community. In other word, it means that socioeconomic changes happened subject to the patterns and conditions of the urban life. Each government depend a certain policy, this policy contains set of plans and programs, that targeting to affect the population factors and the st
... Show More
(5)
(1)
We studied at the morphology, structural setup, and optical characteristics of thin cadmium (CdSe) films a thickness of 250 nm that were created by thermal evaporation over glass, The films exhibited a hexagonal shape were crystalline, and tended to form grains in the (111) crystallographic direction, according to the X-ray diffraction examinations. These characteristics were established using the investigation's findings. Through the use of thin films of CdSe doped with Ag at a concentration of 1.5%, the crystal structure orientations for pure CdSe (25.32, 41.84) and CdSe:Ag (25.39, 41.01) that were both pure as well as those that were doped with silver were both determined. The band gap of the optical spectrum decreased by 1.93–
... Show More
(6)
(5)
In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as the Bayes method. The comparison was made using the mean error squares (MSE), where the best estimator is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).
(5)
(4)
The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample
... Show More
(2)