This paper uses Artificial Intelligence (AI) based algorithm analysis to classify breast cancer Deoxyribonucleic (DNA). Main idea is to focus on application of machine and deep learning techniques. Furthermore, a genetic algorithm is used to diagnose gene expression to reduce the number of misclassified cancers. After patients' genetic data are entered, processing operations that require filling the missing values using different techniques are used. The best data for the classification process are chosen by combining each technique using the genetic algorithm and comparing them  in terms of accuracy.

         In the present paper, the authors introduce and investigates two new subclasses and,  of the class k-fold bi-univalent functions in the open unit disk. The initial coefficients for all of the functions that belong to them were determined, as well as the coefficients for functions that belong to a field determining these coefficients requires a complicated process. The bounds for the initial coefficients and are contained among the remaining results in our analysis are obtained. In addition, some specific special improver results for the related classes are provided.

This paper discusses reliability of the stress-strength model. The reliability functions 𝑅1 and 𝑅2 were obtained for a component which has an independent strength and is exposed to two and three stresses, respectively. We used the generalized inverted Kumaraswamy distribution GIKD with unknown shape parameter as well as known shape and scale parameters. The parameters were estimated from the stress- strength models, while the reliabilities 𝑅1, 𝑅2 were estimated by three methods, namely the Maximum Likelihood,  Least Square, and Regression.

 A numerical simulation study a comparison between the three estimators by mean square error is performed. It is found that best estimator between

Abstract  

Magnetic abrasive finishing (MAF) process is one of non-traditional or advanced finishing methods which is suitable for different materials and produces high quality level of surface finish where it uses magnetic force as a machining pressure. A set of experimental tests was planned according to Taguchi orthogonal array (OA) L27 (3⁶) with three levels and six input parameters. Experimental estimation and optimization of input parameters for MAF process for stainless steel type 316 plate work piece, six input parameters including amplitude of tooth pole, and number of cycle between teeth, current, cutting speed, working gap, and finishing time, were performed by design of experiment

Surfaces quality is one of the most specified customer requirements for machine parts. The major indication of surfaces quality on machined parts is surface roughness. The research aim is to study the cutting conditions and their effects on the surface roughness. This paper utilizes regression models to predict surface roughness over the machining time for variety of cutting conditions in turning. In the experimental part for turning, different types of materials (Aluminum alloy, Copper alloy, and Gray cast iron) were considered with different cutting speed ( ) and feed rate ( ). A mathematical Model depending on statistical-mathematical method between surface roughness (Rz ) and cutting condition ( , ) were derived, for the three materials

Due to the rapid advancement of technology and the technology of things, modern industries start to need a highprecision equipment and surface finishing, so many finishing processes began to develop. One of the modern processes is Magnetic Abrasive Finishing (MAF), which is a high-precision process for internal and external finishing under the influence of a magnetic field of abrasive particles. Boron Carbide (B4C) ceramics was tested by mixing it with iron (Fe) and produced abrasive particles to reduce the intensity of scraping on the surface, reduce the economic cost and achieve a high finishing addition to remove the edges at the same time. The material selected for the samples was mild steel (ASTM E415) under (Quantity of Abrasives, Mac

The effects of using aqueous nanofluids containing covalently functionalized graphene nanoplatelets with triethanolamine (TEA-GNPs) as novel working fluids on the thermal performance of a flat-plate solar collector (FPSC) have been investigated. Water-based nanofluids with weight concentrations of 0.025%, 0.05%, 0.075%, and 0.1% of TEA-GNPs with specific surface areas of 300, 500, and 750 m2/g were prepared. An experimental setup was designed and built and a simulation program using MATLAB was developed. Experimental tests were performed using inlet fluid temperatures of 30, 40, and 50 °C; flow rates of 0.6, 1.0, and 1.4 kg/min; and heat flux intensities of 600, 800, and 1000 W/m2. The FPSC’s efficiency increased as the flow rate and hea

The effect of electrolysis operating parameters on the removal efficiency of cadmium from a simulated wastewater was studied by adopting response surface methodology combined with Box–Behnken Design. As a new electrode design, spiral-wound woven wire mesh rotating cylinder electrode was used for cadmium removal. Current (240–400 mA), rotation speed (200–1000 rpm), initial cadmium concentration (200–600ppm), and cathode mesh number (30–60) were chosen as independent variables while the removal efficiency of cadmium was considered as a response function. The results revealed that the rotation speed has the major effect on the removal efficiency of cadmium. Regression analysis showed good fit of the experimental data to the second-or

In our research, we dealt with one of the most important issues of linguistic studies of the Holy Qur’an, which is the words that are close in meaning, which some believe are synonyms, but in the Arabic language they are not considered synonyms because there are subtle differences between them. Synonyms in the Arabic language are very few, rather rare, and in the Holy Qur’an they are completely non-existent. And how were these words, close in meaning, translated in the translation of the Holy Qur’an by Almir Kuliev into the Russian language.

ءأرﻘﻟا ةﺎﯾﺣﺑ ًﺎﻘﯾﺛو ًﻻﺎﺻﺗا لﺻﺗﺗ. نﻣ ﮫﺑﺗﺎﮐﻟﻟ ﻲﺻﺧﺷﻟا ﻊﺑﺎطﻟا ﻲﻔﺣﺻﻟا دوﻣﻌﻟا لﻣﺣﯾ ا فﻟﺗﺧﻣﻟ ﮫﻟوﺎﻧﺗ لﻼﺧ وا ﮫﺋارا وا هرظﻧ ﺔﮭﺟو لﻣﺣﺗ ﻲﺗﻟا ﺔﯾﻣوﯾﻟا ثادﺣﻻاو ﺎﯾﺎﺿﻘﻟ ﺢﺿﻔﺑ موﻘﯾو ثادﺣﻻاو ﺔﯾﺑﻟﺳﻟا رھاوظﻟﻟ ىدﺻﺗﯾ وا، ءيرﺎﻘﻟا ﯽﻟا ﮫﺑرﺎﺟﺗ وا هرﺎﮐﻓا ءﺎطﺧﻻا دﺻرﯾ بﯾﻗرﺑ ﮫﺑﺷا وھو، ءيرﺟﻟا دﻘﻧﻟا نﻋ مﻧﯾ بوﻟﺳﺎﺑ ﺔﺋطﺎﺧﻟا تﺎﺳرﺎﻣﻣﻟا ﺎﮭﺣدﻣﯾو تﺎﯾﺑﺎﺟﯾﻻا ﯽﻟﻋ