In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

The research includes the preparation of a new Schiff base(4-methyl-2-((2-phenyl hydrazineylidene)methyl)naphthalen-1-ol), which was subsequently, used to prepare a series of complexes using chlorides of Mn2+, Co2+, Cu2+, Cr3+, and Fe3+ ions. The synthesized compounds were characterized using various techniques such as elemental microanalysis (C.H.N), chloride content determination using Mohr’s method, FT-IR spectroscopy, UV-Visible, mass spectra, conductivity, DSC (Differential Scanning Calorimetry), and thermogravimetric analysis. Overall, the decay of the ligand and its metal complexes was recorded to determine their thermal stability and weight-loss profiles. The results indicated that ligand acts as a bidentate doner, coordinating wi

      Manganese-zinc ferrite Mn_xZn_1-xFe₂O₄ (MnZnF) powder was prepared using the sol-gel method. The morphological, structural, and magnetic properties of MnZnF powder were studied using X-ray diffraction (XRD), atomic force microscopy (AFM), energy dispersive X-ray (EDX), field emission-scanning electron microscopes (FE-SEM), and vibrating sample magnetometers (VSM). The XRD results showed that the MnxZn1-xFe2O4 that was formed had a trigonal crystalline structure. AFM results showed that the average diameter of Manganese-Zinc Ferrite is 55.35 nm, indicating that the sample has a nanostructure dimension. The EDX spectrum revealed the presence of transition metals (Mn, Fe, Zn, and O) in Mang

Electrochemical method was used to prepare carbon quantum dots (CQDs). Size of matter was nature when evaluate via X-ray diffraction (XRD). A distinct peak at 2θ equal to 31.6° and three other small peaks at 38.28°, 56.41° and 66.12° were observed. The measures of Fourier Transform Infrared Spectroscopy (FTIR) showed the bonds in the transmittance spectrum are manufactured with carbon nanostructures in view. The first peaks are the O–H stretching vibration bands at (3417 and 2922) cm−1, (C–O–H at 1400, and 1317) cm−1, (C–H), (C=C), (C–O–H), (C=O), and (C–O) bonds at 2850, 1668, 1101, and 1026 cm−1 sequentially. The transmission electron microscopy (TEM) results presented that the spherical CQDs are in shape and on a

Accurate predictive tools for VLE calculation are always needed. A new method is introduced for VLE calculation which is very simple to apply with very good results compared with previously used methods. It does not need any physical property except each binary system need tow constants only. Also, this method can be applied to calculate VLE data for any binary system at any polarity or from any group family. But the system binary should not confirm an azeotrope. This new method is expanding in application to cover a range of temperature. This expansion does not need anything except the application of the new proposed form with the system of two constants. This method with its development is applied to 56 binary mixtures with 1120 equili