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

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

      TV medium derives its formal shape from the technological development taking place in all scientific fields, which are creatively fused in the image of the television, which consists mainly of various visual levels and formations. But by the new decade of the second millennium, the television medium and mainly (drama) became looking for that paradigm shift in the aesthetic formal innovative fields and the advanced expressive performative fields that enable it to develop in treating what was impossible to visualize previously. In the meantime, presenting what is new and innovative in the field of unprecedented and even the familiar objective and intellectual treatments. Thus the TV medium has sought for work

In this study, the mechanical properties of an epoxy and unidirectional woven carbon with fiberglass composite were experimentally investigated. When preparing the composite samples, American Society for Testing and Materials (ASTM)standard was used. Tensile, impact and flexural test were conducted to investigate the mechanical properties of the new produced epoxy Unidirectional Woven Carbon and Epoxy Fiberglass composites. The outcome showed that the strength of the produced samples increased with the increase in the number of unidirectional woven carbon layers added. Two methods were utilized: (1) woven carbon composite with glass fiber (2) woven carbon composite). The two methods of composite were compared with each other.  The resul

In this study, chemical oxidation was employed for the synthesis of polypyrrole (PPy) nanofiber. Furthermore, PPy has been subjected to treatment using nanoparticles of neodymium oxide (Nd2O3), which were produced and added in a certain ratio. The inquiry centered on the structural characteristics of the blend of polypyrrole and neodymium oxide after their combination. The investigation utilises X-ray diffraction (XRD), FTIR, and Field Emission Scanning Electron Microscopy (FE-SEM) for PPy, 10%, 30%, and 50% by volume of Nd2O3. According to the electrochemical tests, it has been noted that the nanocomposites exhibit a substantial amount of pseudocapacitive activity.

The purpose of my thesis is to synthesis two new bidentate ligands ‎which were used to prepare series of metal complexes by reacting the ‎ligands with (M+2 = Mn, Co, Ni, Cu, Cd and Hg) ‎ ‎ Succinyl chloride was used as starting material to synthesis two ‎bidentate ligands (L1) and (L2) by reaction it with 4-chloroaniline ‎‎(L1) and ‎ ‎ (4-aminoacetophenone) (L2) in dichloromethane as a solvent, that ‎are:‎ ‎(L1) = N1,N4-bis (4-chloro phenyl ) succinamide ‎(L2) =N1,N4-bis(4-acetylphenyl)succinamide ‎ The new ligands were characterize by using spectroscopic study ‎‎(Fourier-transform infrared spectroscopy (FT-IR), electronic spectra ( ‎UV-Vis) ,nuclear magnetic resonance(1H,13C-NMR), Mass spectra ‎