Each project management system aims to complete the project within its identified objectives: budget, time, and quality. It is achieving the project within the defined deadline that required careful scheduling, that be attained early. Due to the nature of unique repetitive construction projects, time contingency and project uncertainty are necessary for accurate scheduling. It should be integrated and flexible to accommodate the changes without adversely affecting the construction project’s total completion time. Repetitive planning and scheduling methods are more effective and essential. However, they need continuous development because of the evolution of execution methods, essentially based on the repetitive construction projects’ composition of identical production units. This study develops a mathematical model to forecast repetitive construction projects using the Support Vector Machine (SVM) technique. The software (WEKA 3.9.1©2016) has been used in the process of developing the mathematical model. The number of factors affecting the planning and scheduling of the repetitive projects has been identified through a questionnaire that analyzed its results using SPSS V22 software. Three accuracy measurements, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to check the mathematical model and to compare the actual values with predicted values. The results showed that the SVM technique was more precise than those calculated by the conventional methods and was found the best generalization with R 97 %, MAE 3.6 %, and RMSE 7 %.
Grapes and grape seeds are important samples employed for environmental medical studies . The air of this work was to identify and concentration calculation of the elements in grapes fruit and thier seeds by using X-Ray fluoresces technique (XRF) . Samples were collected from Abo Ghraib of Baghdad city ,the grape seeds were obtained from those samples . Both samples were taken under experimental procedure to obtain the sample which were ready for analysis . The samples were then submitted to experimental conditions using a radiation source and then samples were applied for counting analysis shows the elements Na , Mg , Al , Si , P , S , Cl , K , Ca , and Sr as major components of the samples. Fe , Sr , I , Ba and V were
... Show MoreBackground Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the prediction performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS–ML framework which captures the global search capacity of HMPCS and predictive power of sophisti
... Show MoreThe consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying
... Show MoreThe objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.
Was conducted to compare the two methods above and it became clear by comparing the logistic regression model best of a Linear Discriminant function written
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Business organizations are using the technological innovations like cloud computing (CC) as a developmental platform in order to improve the performance of their information systems. In that context, our paper discusses know-how in employing the public and private CC to serve as platforms to develop the evaluation system of annual employees' performance (ESAEP) at Iraqi universities. Therefore, we ask the paper question which is “Is it possible to adopt the innovative solutions of ICTs (Like: public and private CC) for finding the developmental vision about management information systems at business organizations?”. In addition, the paper aim
... Show Moreتعد لعبة كرة السلة من الألعاب الرياضية التي تحتاج متطلبات بدنية ووظيفية خاصة بها، وذلك من خلال الانتقال داخل الملعب بالكرة أو بدونها والسبل للتخلص من ملاحقة الخصم أثناء الدفاع وكيفية المناورة أثناء الهجوم مع إجادة التصويب بكافة أنواعه داخل الملعب، ومن هنا تكونت مشكلة البحث في كيفية تطوير تلك العوامل الوظيفية والتي لها الأثر في الارتقاء بمستوى أداء اللاعب أثناء اللعب، وعن طريق استخدام الباحثان لطريقة جهاز ا
... Show MoreAdvanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
... Show MoreAn optimization calculation is made to find the optimum properties of combined quadrupole lens which consists of electrostatic and magnetic lens. Both chromatic and spherical aberration coefficients are reduced to minimum values and the achromatic aberration is found for many cases. These calculations are achieved with the aid of transfer matrices method and using rectangular model of field distribution, where the path of charged-particles beam traversing the field has been determined by solving the trajectory equation of motion and then the optical properties for lens have been computed with the aid of the beam trajectory along the lens axis. The computations have been concentrated on determining the chromatic and spher
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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