In this work, Elzaki transform (ET) introduced by Tarig Elzaki is applied to solve linear Volterra fractional integro-differential equations (LVFIDE). The fractional derivative is considered in the Riemman-Liouville sense. The procedure is based on the application of (ET) to (LVFIDE) and using properties of (ET) and its inverse. Finally, some examples are solved to show that this is computationally efficient and accurate.

In this work, Elzaki transform (ET) introduced by Tarig Elzaki is applied to solve linear Volterra fractional integro-differential equations (LVFIDE). The fractional derivative is considered in the Riemman-Liouville sense. The procedure is based on the application of (ET) to (LVFIDE) and using properties of (ET) and its inverse. Finally, some examples are solved to show that this is computationally efficient and accurate.

     Widespread COVID-19 infections have sparked global attempts to contain the virus and eradicate it. Most researchers utilize machine learning (ML) algorithms to predict this virus. However, researchers face challenges, such as selecting the appropriate parameters and the best algorithm to achieve an accurate prediction. Therefore, an expert data scientist is needed. To overcome the need for data scientists and because some researchers have limited professionalism in data analysis, this study concerns developing a COVID-19 detection system using automated ML (AutoML) tools to detect infected patients. A blood test dataset that has 111 variables and 5644 cases was used. The model is built with three experiments using Python's Auto-

In this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.

     In this paper, the oscillation of a Hematopoiesis model in both cases delay and non-delay are discussed. The place and are continuous pstive -rdic functions. In the nn-dlay cse, we will exhibit that a nonlinear differential equation of hematopoiesis model has a global attractor  for all different pstive solutions. Also, in the delay case, the sufficient conditions for the oscillation of all pstive solutions of it aboutare presented and we establish sufficient cnditions for the global attractive of. To illustrate the obtained results some examples are given.

Tau-P linear noise attenuation filter (TPLNA) was applied on the 3D seismic data of Al-Samawah area south west of Iraq with the aim of attenuating linear noise. TPLNA transforms the data from time domain to tau-p domain in order to increase signal to noise ratio. Applying TPLNA produced very good results considering the 3D data that usually have a large amount of linear noise from different sources and in different azimuths and directions. This processing is very important in later interpretation due to the fact that the signal was covered by different kinds of noise in which the linear noise take a large part.

    The aim of this study is to estimate the survival function for the data of lung cancer patients, using parametric methods (Weibull, Gumbel, exponential and log-logistic).

Comparisons between the proposed estimation method have been performed using statistical indicator Akaike information Criterion, Akaike information criterion corrected and Bayesian information Criterion, concluding that the survival function for the lung cancer by using Gumbel distribution model is the best. The expected values of the survival function of all estimation methods that are proposed in this study have been decreasing gradually with increasing failure times for lung cancer patients, which means that there is an opposite relationshi

The present work aims to achieve pulsed laser deposition ofTiO₂ nanostructures and investigate their nonlinear properties using z-scan technique.The second harmonic Q-switched Nd: YAG laser at repetition rate of 1Hz and wavelength of 532 nm with three different laser fluencies in the range of 0.77-1.1 J/cm² was utilized to irradiate the TiO₂ target. The products of laser-induced plasma were characterized by utilizing UV-Vis absorption spectroscopy, x-ray diffraction (XRD), atomic force Microscope (AFM),and Fourier transform infrared (FTIR). A reasonable agreement was found among the data obtained usingX-Ray diffraction, UV-Vis and Raman spectroscopy. The XRD results showed that the prepared TiO₂

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att