Experimental programs based test results has been used as a means to find out the response of individual elements of structure. In the present study involves investigated behavior of five reinforced concrete deep beams of dimension (length 1200 x height 300 x width150mm) under two points concentrated load with shear span to depth ratio of (1.52), four of these beams with hallow core and
retrofit with carbon fiber reinforced polymer CFRP (with single or double or sides Strips). Two shapes of hallow are investigated (circle and square section) to evaluated the response of beams in case experimental behavior. Test on simply supported beam was performed in the laboratory & loaddeflection, strain of concrete data and crack pattern of

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

المقدمة

تمثل الأسعار تعبيرا" نقدياً لقيمة السلع والخدمات المقدمة للزبون، وتعد عملية التسعير من العمليات المهمة وذات الأثر البالغ على نجاح واستمرار منظمات الأعمال، نظراً لما لها من تأثير على المقدرة الربحية وإعادة توزيع الدخل القومي المخصص للاستهلاك، إذ أنه بتحديد الأسعار يستطيع الفرد أن يوزع دخله بين ما يعده ضرورياً من السلع وما يعد كمالياً.

وعموماً فإن لعملية التسعير م

      Bilinear interpolation and use of perceptual color spaces (HSL, HSV, LAB, and LUV) fusion techniques are presented to improve spatial and spectral characteristics of the multispectral image that has a low resolution to match the high spatial resolution of a panchromatic image for different satellites image data (Orbview-3 and Landsat-7) for the same region. The Signal-to-Noise Ratio (SNR) fidelity criterion for achromatic information has been calculated, as well as the mean color-shifting parameters that computed the ratio of chromatic information loss of the RGB compound inside each pixel to evaluate the quality of the fused images. The results showed the superiority of HSL color space to fuse images over the rest of the spac

The main objective of e-learning platforms is to offer a high quality instructing, training and educational services. This purpose would never be achieved without taking the students' motivation into consideration. Examining the voice, we can decide the emotional states of the learners after we apply the famous theory of psychologist SDT (Self Determination Theory). This article will investigate certain difficulties and challenges which face e-learner: the problem of leaving their courses and the student's isolation.
Utilizing Gussian blending model (GMM) so as to tackle and to solve the problems of classification, we can determine the learning abnormal status for e-learner. Our framework is going to increase the students’ moti

Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms,

     Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the prefe

    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c