Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

In our article, three iterative methods are performed to solve the nonlinear differential equations that represent the straight and radial fins affected by thermal conductivity. The iterative methods are the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM) to get the approximate solutions. For comparison purposes, the numerical solutions were further achieved by using the fourth Runge-Kutta (RK4) method, Euler method and previous analytical methods that available in the literature. Moreover, the convergence of the proposed methods was discussed and proved. In addition, the maximum error remainder values are also evaluated which indicates that the propo

Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is sol

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

Extraction and Description of Urease Enzyme Produced from Staphylococcus saprophyticus and study of its effect on kidney and bladder of white mice

Phoenix dactylifera l. pinnae (the green leaves of dates palm) were used as natural reinforcing (strengthening) fibers to improve the mechanical properties of polyester as a matrix material, the fibers of the green leaves of dates palm were used in two lengths, 10 and 20mm with five rates of 0, 2.5, 5, 10, and 20% , where the reinforcing with the leaves fibers increases the hardness strength from 76.5 to be about 86.55 , the Impact value raised from about 0.313 to 0.461 , in addition to that the flexural strength from 2.66 to be about 55 , and the thermal conductivity increases from 2.54 𝑤∕𝑚.℃ to 5.41 𝑤∕𝑚.℃. The results of the present search explains that the composite samples reinforced at rate 20% and 10mm fiber length

The effect of gamma radiation at the doses (0 , 5 , 10 or 15 ) Gray on the callus of four Triticum aestivum immature embryos genotypes (AL-Hashmiya , AL-Noor AL-Zahraa and AL-Mellad ) were studied . The fresh and dry weight for callus and shoot tips beside numbers and lengths of the shoots were used as indicators after 8 weeks . Results revealed that (AL-Noor and AL-Zahraa ) was superior by giving highest fresh and dry weight reached 274.2 and 269.2 mg and 26 and 24.3 mg respectively as compared with AL-Hashmiya and AL-Mellad. Moreover, the control treatment and the dose 10 Gray gave highest fresh weight reached 277.4 and 259.1 mg while the dry weight was highest in the control treatment and the dose 5 Gray. addition 10 Gra

The structural, optical and electrical properties of ZnS films prepared by vacuum
evaporation technique on glass substrate at room temperature and treated at different
annealing temperatures (323, 373, 423)K of thickness (0.5)µm have been studied. The
structure of these films is determined by X-ray diffraction (XRD). The X-ray diffraction
studies show that the structure is polycrystalline with cubic structure, and there are strong
peaks at the direction (111).
The optical properties investigated which include the absorbance and transmittance
spectra, energy band gab, absorption coefficient, and other optical constants. The results
showed that films have direct optical transition. The optical band gab was