Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Background. “Polyetheretherketone (PEEK)” is a biocompatible, high-strength polymer that is well-suited for use in dental applications due to its unique properties. However, achieving good adhesion between PEEK and hydrophilic materials such as dental adhesives or cement can be challenging. Also, this hydrophobicity may affect the use of PEEK as an implant material. Surface treatment or conditioning is often necessary to improve surface properties. The piranha solution is the treatment of choice to be explored for this purpose. Methods. PEEK disks of 10 mm diameter and 2 mm thickness were used in this study. Those samples were divided into five groups (each group has five samples). The first is the control group, in which no

A spectrophotometric determination of azithromycin was optimized using the simplex model. The approach has been proven to be accurate and sensitive. The analyte has been reacted with bromothymol blue (BTB) to form a colored ion pair which has been extracted in chloroform in a buffer medium of pH=4 of potassium phthalate. The extracted colored product was assayed at 415 nm and exhibited a linear quantification range over (1 - 20) g/ml. The excipients did not exhibit any interferences with the proposed approach for assaying azithromycin in pharmaceutical formulations.

Neonatal sepsis refers to the bacterial bloodstream infections of the newborn during the neonatal period as usually the first twenty-eight days of life. The current study was done in the laboratories of AL-Batool Teaching Hospital for Gynecology and Pediatrics in Baqubah, Diyala Governorate, including 140 blood specimens collected from the neonates admitted to the hospital with suspected sepsis, the ages of the both groups was ranged from 1 day to 28 days. Out of the total cultured samples, 32.14% (45 of 140) were positive and 67.86% (95 of 140) were negative blood culture. 45 of 140 samples were negative to the blood culture chosen as control group. The results showed highest isolates were Coagulase Negative Staphylococcus (CoNS) 19 (42.2%

In the present study, mixed ligand compounds of Mn(II), Ni(II), Co(II), Cu(II), Cd(II) and ‎Hg(II) were synthesized using new Ligand N1,N4-bis (pyrimidin-2-ylcarbamothioyl) ‎succinimide (NPS) derived from [Butanedioyl diisothiocyanate with 2- aminipyridine] as ‎first ligand, proline (pro) as second ligand and evaluation of their antioxidant activities for ‎ligand, nickel and cobalt complex towards 1.1-Di-phenyl-2picrylhydrazyl (DPPH) will be ‎compared to the standard anti-oxidants (i.e. the ascorbic acid). Those materials that have ‎been prepared provided results are a result of exhibiting different activities of the radical ‎scavenging for all of the compounds. Compounds were observed then confirmed through ‎the Fourier-tra

This paper deals with the Magnetohydrodynyamic (Mill)) flow for a viscoclastic fluid of the generalized Oldroyd-B model. The fractional calculus approach is used to establish the constitutive relationship of the non-Newtonian fluid model. Exact analytic solutions for the velocity and shear stress fields in terms of the Fox H-function are obtained by using discrete Laplace transform. The effect of different parameter that controlled the motion and shear stress equations are studied through plotting using the MATHEMATICA-8 software.

Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted