This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Bacteria strain H8, which produces high amount of exopolysaccharide (EPS), was isolated from soil, and identified as strain of Azotobacter chrococcum by its biochemical /physiological characteristics, EPS was extracted, partially purified and used as bioflocculant. The biochemical analysis of the partially purified EPS revealed that it was an alginate. analysis of EPS by Fourier transform infrared spectrometry (FTIR) show that the -OH groups present in bioflocculant are clearly seen at 3433.06 cm-1, the peaks attributed to the -CH3 groups present at 2916.17 cm-1 , and some distinct peaks such as carboxyl group showed strong absorption bands at 1604.66 cm-1, 1411.80 cm-1 and 1303.79 cm-1 indicate the chemical structure of alginate. The effe

Advanced 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

The risk of breast cancer development is believed to be attributed to the alterations of a number of key biological components. Within this context, elevated levels of some chemokines that act as growth factors and can promote cancer development. The current study was designed to evaluate CXCL3 (a chemokine C-X-C Motif Ligand 3) and leptin (a peptide hormone synthesized by adipose tissue with cytokine activity) serum of Iraqi breast cancer patients in comparison to healthy controls. A total of 90 participants consisted of 60 patients diagnosed with breast cancer and 30 healthy women as control group were enrolled into this case-control study. Venous blood samples were collected from all participants to evaluate CXCL3 and leptin serum levels

In this study, the zinc oxide NPs have been synthesized from the fresh pomegranate peels extract using the precipitation method. The ZnO nanoparticles were produced from the reaction of fresh peels extract with zinc acetate salt which was used as zinc source in the presence of 2 M NaOH. The green synthesized nanoparticles were characterized through X-ray diffraction (XRD), UV-Vis diffuse reflection spectroscopy, Fourier transform infrared spectroscopy (FTIR), and Atomic force microscopy (AFM). The XRD patterns confirm the formation of hexagonal wurtzite phase structure for ZnO synthesized using pomegranate peels extract with average crystalline size of 28 nm. FTIR spectra identify the presence of many active functional groups for the pom

In this paper a system is designed on an FPGA using a Nios II soft-core processor, to detect the colour of a specific surface and moving a robot arm accordingly. The surface being detected is bounded by a starting mark and an ending mark, to define the region of interest. The surface is also divided into sections as rows and columns and each section can have any colour. Such a system has so many uses like for example warehouses or even in stores where their storing areas can be divided to sections and each section is coloured and a robot arm collects objects from these sections according to the section’s colour also the robot arm can organize objects in sections according to the section’s colour.

Robot manipulator is a multi-input multi-output system with high complex nonlinear dynamics, requiring an advanced controller in order to track a specific trajectory. In this work, forward and inverse kinematics are presented based on Denavit Hartenberg notation to convert the end effector planned path from cartesian space to joint space and vice versa where a cubic spline interpolation is used for trajectory segments to ensure the continuity in velocity and acceleration.  Also, the derived mathematical dynamic model is based on Eular Lagrange energy method to contain the effect of friction and disturbance torques beside the inertia and Coriolis effect. Two types of controller are applied ; the nonlinear computed torque control (CTC