<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

The current study aims to compare between the assessments of the Rush model’s parameters to the missing and completed data in various ways of processing the missing data. To achieve the aim of the present study, the researcher followed the following steps: preparing Philip Carter test for the spatial capacity which consists of (20) items on a group of (250) sixth scientific stage students in the directorates of Baghdad Education at Al–Rusafa (1^st, 2^nd and 3^rd) for the academic year (2018-2019). Then, the researcher relied on a single-parameter model to analyze the data. The researcher used Bilog-mg3 model to check the hypotheses, data and match them with the model. In addition

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To

This paper deals with prediction the effect of soil re-moulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil re-moulding due to actual pile driving. The re

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

This paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T