In this study we focused on the determination of influence the novel synthesized thiosemicarbazide derivative "2-(2-hydroxy-3-methoxybenzylidene) hydrazinecarbothioamide" (HMHC) influenced the corrosion inhibition of mild steel (MS) in a 1.0 M hydrochloric acid acidic solution.This is in an effort to preserve the metal material by maintaining it from corrosion.The synthesized inhibitor was characterized using elemental analysis, and NMR-spectroscopy. Then the corrosion inhibition capability of (HMHC) was studied on mild steel in an acidic medium by weight loss technique within variables [temperature, inhibitor concentration, and time]. The immersion periods were [1:00, 3:00, 5:00, 10:00, 24:00, and 72:00] hours and the tem

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

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

<span lang="EN-GB">Transmitting the highest capacity throughput over the longest possible distance without any regeneration stage is an important goal of any long-haul optical network system. Accordingly, Polarization-Multiplexed Quadrature Phase-Shift-Keying (PM-QPSK) was introduced lately to achieve high bit-rate with relatively high spectral efficiency. Unfortunately, the required broad bandwidth of PM-QPSK increases the linear and nonlinear impairments in the physical layer of the optical fiber network. Increased attention has been spent to compensate for these impairments in the last years. In this paper, Single Mode Fiber (SMF), single channel, PM-QPSK transceiver was simulated, with a mix of optical and electrical (Digi

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

Laue back reflection patterns for quartz crystal are indexed by using Orient Express- program to simulate orientation of single crystals from assignment of principle zones. An oriented quartz single crystal was used as a substrate to deposit Zn metal by controlled thermal evaporation to achieve single crystal films of Zn that are subsequently evaluated by x-ray powder diffraction.