Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Reaxys Chemistry database information SciVal Topics Metrics Abstract A novel CoO–ZnO nanocomposite was synthesized by the photo irradiation method using a solution of cobalt and zinc complexes and used as a coating applied by electrophoretic deposition (EPD) for corrosion protection of stainless steel (SS) in saline solution. The samples were characterized using powder XRD, scanning electron microscopy (SEM) and electrochemical polarization. It was also found that the coating was still stable after conducting the corrosion test: it contained no cracks and CoO–ZnO nanocomposites clearly appeared on the surface. SEM showed that the significant surface cracking disappeared. XRD confirmed that CoO–ZnO nanocomposites comprised CoO and Zn

Lithium doped Nickel-Zinc ferrite material with chemical formula Ni0.9−2x Zn0.1LixFe2+xO4, where x is the ratio of lithium ions Li+ (x = 0, 0.01, 0.02, 0.03 and 0.04) prepared by using sol-gel auto combustion technique. X-ray diffraction results showed that the material have pure cubic spinal structure with space group Fd-3m. The experimental values of the lattice constant (aexp) were decreased from 8.39 to 8.35 nm with doped Li ions. It was found that the decreasing of the crystallite size with addition of lithium ions concentration. The radius of tetrahedral (rtet) and octahedral (roct) site were computed from cation distribution. SEM images have been taken to show the morphology of compound. The dielectric parameters [dissipation fa

Phosphorus (P) is an element that is potatoes require in large amounts. Soil pH is a crucial factor impacting phosphorus availability in potato production. This study was conducted to evaluate the influence of P application rates on the P efficiency for tuber yield, specific gravity, and P uptake. Additionally, the relationship between soil pH and total potato tuber yield was determined. Six rates of P fertilization (0–280 kg P ha−1) were applied at twelve different sites across Northern Maine. Yield parameters were not responsive to P application rates. However, regression analysis showed that soil pH was significantly correlated with total potato tuber yield(R2 = 0.38). Sites with soil pH values < 6 had total tuber yields,

 In the present work polymer electrolytes were formulated using the solvent casting technique. Under special conditions, the electrolyte content was of fixed ratio of polyvinylpyrolidone (PVP): polyacrylonitrile (PAN) (25:75), ethylene carbonate (EC) and propylene carbonate (PC) (1:1) with 10% of potassium iodide (KI) and iodine I₂ = 10% by weight of KI. The conductivity was increased with the addition of ZnO nanoparticles. It is also increased with the temperature increase within the range (293 to 343 K). The conductivity reaches maximum value of about (0.0296 S.cm^-1) with (0.25 g) ZnO. The results of FTIR for blend electrolytes indicated a significant degree of interaction between the polymer blend (PVP and PAN)

This project sought to fabricate a flexible gas sensor based on a short functionalized multi-walled carbon nanotubes (f-MWCNTs) network for nitrogen dioxide gas detection. The network was prepared by filtration from the suspension (FFS) method and modified by coating with a layer of polypyrrole conductive polymer (PPy) prepared by the oxidative chemical polymerization to improve the properties of the network. The structural, optical, and morphological properties of the f-MWCNTs and f-MWCNTs/PPy network were studied using X-ray diffraction (XRD), Fourie-transform infrared (FTIR), with an AFM (atomic force microscopy). XRD proved that the structure of f-MWCNTs is unaffected by the synthesis procedure. The FTIR spectra verified the existence o