Estimation of trip attraction and analyzing its main influencing factors are powerful for offering different classifications for business districts and presenting recommendations for improving attractiveness in long term. This is beneficial for designing transportation facilities and infrastructures. The paper presents the prediction of trip attraction using an artificial intelligence technology due to the profits that the technology can possess in shortening time, lowering expenses and saving effort. The new model has utilized six input parameters that have not been considered previously within the area of Nasiriyah city including; age and educational level of the passengers, mode of transport that the passengers use, purpose of the trip, frequency of the weekly visit, and the distance towards the central business district. In this study, the independences - trip attraction data of 224 sets are collected through field observations and home interviews within the area. Neural Network Toolbox in MATLAB is utilized, which is dealt with the six key independences as input whereas with the trip attraction as the output desired to be expected. The model has been generated by adoption of twenty-five artificial neurons in only one single hidden layer. The outcomes have showed a good performance in predicting the trip attraction by utilizing artificial neural network. The coefficient of correlation for training is 0.81445 and for all, including training, testing, and validation, it is 0.73825. The study produces a reliable model as an alternative to complex, high-priced and/or time-consuming models.
This research aims to provide insight into the Spatial Autoregressive Quantile Regression model (SARQR), which is more general than the Spatial Autoregressive model (SAR) and Quantile Regression model (QR) by integrating aspects of both. Since Bayesian approaches may produce reliable estimates of parameter and overcome the problems that standard estimating techniques, hence, in this model (SARQR), they were used to estimate the parameters. Bayesian inference was carried out using Markov Chain Monte Carlo (MCMC) techniques. Several criteria were used in comparison, such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R^2). The application was devoted on dataset of poverty rates acro
... Show MoreVarious simple and complicated models have been utilized to simulate the stress-strain behavior of the soil. These models are used in Finite Element Modeling (FEM) for geotechnical engineering applications and analysis of dynamic soil-structure interaction problems. These models either can't adequately describe some features, such as the strain-softening of dense sand, or they require several parameters that are difficult to gather by conventional laboratory testing. Furthermore, soils are not completely linearly elastic and perfectly plastic for the whole range of loads. Soil behavior is quite difficult to comprehend and exhibits a variety of behaviors under various circumstances. As a result, a more realistic constitutive model is
... Show MoreThe way artists deal with body in their artistic works has had so many forms and methods, whether as an object for their drawings or as a material to create live artistic performances that relate to the idea of correspondence and interaction between different artistic categories such as: drama, dance, and painting as it is the case of the artist Marina Abramovic who has always used her body as an artistic unit to generate meaning and to perform her lively shows.
To go deeper into her career, our work was divided into 3 sections:
The first section was devoted to follow the main artistic stages that her body had gone through, starting with paintings she performed using concepts based on acting, simulation and nudity and ending wi
Various simple and complicated models have been utilized to simulate the stress-strain behavior of the soil. These models are used in Finite Element Modeling (FEM) for geotechnical engineering applications and analysis of dynamic soil-structure interaction problems. These models either can't adequately describe some features, such as the strain-softening of dense sand, or they require several parameters that are difficult to gather by conventional laboratory testing. Furthermore, soils are not completely linearly elastic and perfectly plastic for the whole range of loads. Soil behavior is quite difficult to comprehend and exhibits a variety of behaviors under various circumstances. As a result, a more realistic constitutive model is
... Show MoreAl-Si alloys which are widely used in engineering applications due to their outstanding properties can be modified for more enhancements in their properties. Current work investigated the ability of these alloys to be modified by casting them through the addition of nanoparticles. So, Multi-wall carbon nanotubes (CNT) and titanium carbide ceramic particles (TIC) with size of (20 nm) were added with different amounts started from (0.5 up to 3%) weight to cast alloy A356 that was considered to be the base metal matrix, then stirred with different speeds of (270, 800, 1500, 2150) rpm at 520 °C for one minute. The results showed change in microstructure’ shape of the casted alloys from the dendritic to spherical gra
... Show More<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c
... Show MoreCOVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in
Zinc-indium-selenide ZnIn2Se4 (ZIS) ternary chalcopyrite thin film on glass with a 500 nm thickness was fabricated by using the thermal evaporation system with a pressure of approximately 2.5×10−5 mbar and a deposition rate of 12 Å/s. The effect of aluminum (Al) doping with 0.02 and 0.04 ratios on the structural and optical properties of film was examined. The utilization of X-ray diffraction (XRD) was employed to showcase the influence of aluminum doping on structural properties. XRD shows that thin ZIS-pure, Al-doped films at RT are polycrystalline with tetragonal structure and preferred (112) orientation. Where the