“Smart city” projects have become fully developed and are actively using video analytics. Our study looks at how video analytics from surveillance cameras can help manage urban areas, making the environment safer and residents happier. Every year hundreds of people fall on subway and railway lines. The causes of these accidents include crowding, fights, sudden health problems such as dizziness or heart attacks, as well as those who intentionally jump in front of trains. These accidents may not cause deaths, but they cause delays for tens of thousands of passengers. Sometimes passers-by have time to react to the event and try to prevent it, or contact station personnel, but computers can react faster in such situations by using ethical

ZnS nanoparticles were prepared by a simple microwave irradiation method under mild condition. The starting materials for the synthesis of ZnS quantum dots were zinc acetate (R & M Chemical) as zinc source, thioacetamide as a sulfur source and ethylene glycol as a solvent. All chemicals were analytical grade products and used without further purification. The quantum dots of ZnS with cubic structure were characterized by X-ray powder diffraction (XRD), the morphology of the film is seen by scanning electron microscopy (SEM). The particle size is determined by field effect scanning electron microscopy (FESEM), UV-Visible absorption spectroscopy and XRD. UV-Visible absorption spectroscopy analysis shows that the absorption peak of the as-prep

Abstract

The aim of the present work is to control of metal buried corrosion by alteration the media method. This method depended on the characteristics of each media. The corrosion rates in different media (soil, sand, porcelanite stone and gravel) for specimens of low carbon steel were measured by two methods weight loss method and polarization method, weight loss measured by buried specimens in these medias separately for 90 days. The polarization method includes preparing of specimen and salt solutions have electrical resistivity equivalent electrical resistivity of these media. The corrosion rate of two method results in (soil > sand> porcelainte stone> gravel). The lower corrosion rate happene

The economical design of plate loaded by pressure can be obtained by using stiffeners instead of increasing the thickness of plate. The main subject of this work is to obtain the effect of stiffener height on the maximum stress in the plate subjected to pressure load. Different plate-stiffener sets are selected to find the effects of stiffener thickness, plate dimensions and pressure, on the optimum stiffener height. The models under consideration are square plates clamped rigidly from four edges. Finite Element method is used to analyze 160 different models by using the Finite Element software package ANSYS version 11. Another analysis method based on maximum stress equation is used to analyze 30 models. The graphical comparison of results

In this research PbS and PbS:Cu films were prepered with thicknesses (0.85±0.05)?m and (0.55±0.5)?m deposit on glass and silicon substrate respectively using chemical spray pyrolysis technique with a substrate temperature 573K, from lead nitrate salt, thiourea and copper chloride. Using XRD we study the structure properties for the undoped and doped films with copper .The analysis reveals that the structure of films were cubic polycrystalline FCC with a preferred orientation along (200) plane for the undoped films and 1% doping with copper but the orientation of (111) plane is preferred with 5% doping with the rest new peaks of films and appeared because of doping. Surface topography using optical microscope were be checked, it was found

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, polyvinylpyrrolidone (PVP), Multi-walled carbon nanotubes (MWCNTs) nanocomposite was prepared and hybrid with Graphene (Gr) by casting method. The morphological and optical properties were investigated. Fourier Transformer-Infrared (FT-IR) indicates the presence of primary distinctive peaks belonging to vibration groups that describe the prepared samples. Scanning Electron Microscopy (SEM) images showed a uniform dispersion of graphene within the PVP-MWCNT nanocomposite. The results of the optical study show decrease in the energy gap with increasing MWCNT and graphene concentration. The absorption coefficient spectra indicate the presence of two absorption peaks at 282 and 287 nm attributed to the π-π* electronic tr

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

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121