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

In this work, the effect of the addition of bright nickel plating and silver carried out by the electroplating method has been studied, on the coating of copper nanoparticles on the copper base metal via the process of thermal evaporation. The improvement of the solar absorber using CuNP in combination with the bright nickel and silver was obtained to be better than copper nanoparticles individually. A bright nickel enhanced the absorbed thermal stability. Also, other optical properties, absorptions, and emissivity slightly decreased from (93% to 87%), while the existence of silver had a slight impact on absorption of about (86.50%). On the other hand, thermal conductivity was evaluated using hot disk analyzer. The results showed a good

BACKGROUND: Dorsal reconstruction and augmentation for a saddle nose or generalized under projecting nose (familial ) require some type of grafting that increase the volume, raise the radix , raise the entire dorsum ,camouflage the osseocartilaginous junction or any irregularity along the dorsum, diced cartilage graft wrapped with fascia lata used to achieve that . AIM OF THE STUDY : The purpose of the study was to assess the aesthetic and functional outcome of dorsal augmentation by diced cartilage graft wrapped with fascia lata. PATIENTS AND METHODS: Between July 2018 and May 2019. 7 patients included ,4 females and 3 males ,their age ranged between 20 to 37 years ,5 were primary and 2 secondary case rhinoplasty,2 operated by closed appro

Abstract

Semiconductor-based gas sensors were prepared, that use n-type tin oxide (SnO₂) and  tin oxide: zinc oxide composite (SnO₂)_1-x(ZnO)_x at different x ratios using pulse laser deposition at room temperature. The prepared thin films were examined to reach the optimum conditions for gas sensing applications, namely X-ray diffraction, Hall effect measurements, and direct current conductivity. It was found that the optimum crystallinity and maximum electron density, corresponding to the minimum charge carrier mobility, appeared at 10% ZnO ratio. This ratio appeared has the optimum NO₂ gas sensitivity for 5% gas concentration at 300 °C working temperat

The pure ZnS and ZnS-Gr nanocomposite have been prepared
successfully by a novel method using chemical co-precipitation. Also
conductive polymer PPy nanotubes and ZnS-PPy nanocomposite
have been synthesized successfully by chemical route. The effect of
graphene on the characterization of ZnS has been investigated. X-ray
diffraction (XRD) study confirmed the formation of cubic and
hexagonal structure of ZnS-Gr. Dc-conductivity proves that ZnS and
ZnS-Gr have semiconductor behavior. The SEM proved that
formation of PPy nanotubes and the Gr nanosheet. The sensing
properties of ZnS-PPy/ZnS-Gr for NO2 gas was investigated as a
function of operating temperature and time under optimal condition.
The sensitivity,

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton