The green production of iron oxide nanoparticles (FeONPs) due to its numerous biotechnological uses has attracted a lot of attention and clean and eco-friendly approaches in the medical field.

The present study examines the extraction of lead (Pb), cadmium (Cd) and nickel (Ni) from   a contaminated soil by washing process. Ethylenediaminetetraacetic acid disodium salt (Na₂EDTA) and hydrochloric acid (HCl) solution were used as extractants.  Soil washing is one of the most suitable in-situ/ ex-situ remediation method in removing heavy metals. Soil was artificially contaminated with 500 mg/kg (Pb , Cd and Ni ).  A set of batch experiments were carried out at different conditions of  extractant concentration , contact time, pH and agitation speed. The results  showed  that the  maximum removal efficiencies  of (Cd, Pb  and Ni ) were (97, 88 and 24 )&nbs

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

This research has been prepared to isolate and diagnose one of the most important vegetable oils from the plant medical clove is the famous with Alaeugenol oil and used in many pharmaceuticals were the isolation process using a technique ultrasonic extraction and distillation technology simple

Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well-  Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.

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A liquid-solid chromatography of Bovine Serum Albumin (BSA) on (diethylaminoethyl-cellulose) DEAE-cellulose adsorbent is worked experimentally, to study the effect of changing the influent concentration of (0.125, 0.25, 0.5, and 1 mg/ml) at constant volumetric flow rate Q=1ml/min. And the effect of changing the volumetric flow rate (1, 3, 5, and 10 ml/min) at constant influent concentration of Co=0.125mg/ml. By using a glass column of (1.5cm) I.D and (50cm) length, packed with adsorbent of DEAE-cellulose of height (7cm). The influent is introduced in to the column using peristaltic pump and the effluent concentration is investigated using UV-spectrophotometer at 30oC and 280nm wavelength. A spread (steeper) break-through curve is gained

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a