Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

A new set of metal complexes by the general formula [M(C)2(H2O)2]Cl2 has been prepared through the interaction of the new Ligand [N1, N4-bis(4-chlorophenyl)succinamide] (C) derived from succinyl chloride with 4-Chloroaniline with the transition metal ions Mn(II), Co(II), Ni(II), Hg(II), Cu(II) and Cd(II). Compounds diagnosed by TGA, 1 H, 13CNMR and Mass spectra (for (C)), Fourier-transform infrared and Electronic spectrum, Magnetic measurement, molar conduct, (%M, %C, %H, %N). These measurements indicate that (C) is associated with the metal ion in a bi-dentate fashion by nitrogen atoms (the amide group) and the octahedral composition of these complexes is suggested. The anti-bacterial action of the compounds towards three types of bacteria

In this work, γ-Al2O3NPs were successfully biosynthesized, mediated aluminum nitrate nona hydrate Al(NO3)3.9H2O, sodium hydroxide, and aqueous clove extract in alkali media. The γ-Al2O3NPs were characterized by different techniques like Fourier transform infrared spectroscopy (FT-IR), UV-Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy–dispersive x-ray spectroscopy, transmission electron microscope (TEM), Energy-dispersive X-ray spectroscopy (EDX), and atomic force microscopy (AFM). The final results indicated the γ-Al2O3NPs nanoparticle size, bonds nature, element phase, crystallinity, morphology, surface image, particle analysis – threshold detection, and the topography parameter. The id

The current study was designed to compare some of the vital markers in the sera of diabetic and neuropathy patients via estimating Adipsin, Fasting blood Glucose(FBG), Glycated(HbA1c) hemoglobin, Homeostasis Model Assessment Index (Homa IR ), Cholesterol, High density lipoprotein (HDL), Triglycerides (T.G), Low-density, and lipoprotein (LDL), Very Low Density Lipoprotein (VLDL), in sera of Iraqi patients with diabetes and neuropathy. A total  of ninety  subjects  were divided into three groups: group I (30 diabetic with neuropathy males) and group II (30 diabetic males without neuropathy), and 30 healthy sujects were employed as control group. The results showed a significant decline in Adipsin levels (p>0.05) in neuropathy, T2DM g

A series of new imides compounds[1-4] were synthesized from reaction of tetrachlorophthalic anhydride or nitro phthalic anhydride or malic anhydride or Succinic anhydride with 4-amino benzene thiol under fusion conditions. Chloroacetic acid has been added after compounds [1-4] reacted with distilled H2O and Na2CO3, producing compounds [5-8]. In benzene, compounds [5-8] also interacted with the thionyl chloride to produce [9-12]. Poly (vinyl alcohol) was chemically modified by reacting PVA with compounds [9-12] and dimethyl formamide to produce compounds [13-16]. Iron oxide nanoparticles (IONPs) are mixed with modified PVA [13-16] to create nanocomposites [17-20]. Spectral and analytical data from synthesized compounds, such as 1H-NMR, FTI

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi