Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).
Background:
Treatment of the Covid-19 pandemic caused by the highly contagious and pathogenic SARS-CoV-2 is a global menace. Day by day this pandemic is getting worse. Doctors, Scientists and Researchers across the world are urgently scrambling for a cure for novel corona virus and continuously working at break neck speed to develop vaccine or drugs. But to date, there are no specific drugs or vaccine available in the market to cope up the virus.
Objective:
The present study helps us to elucidate 3D structures of SARS-CoV-2 proteins and also to identify best natural compounds as potential inhibitors against COVID-19.
Methods:
The 3D structures of the proteins were constructed using Modeller 9.16 modeling tool. Modelled proteins were validated with PROCHECK by Ramachandran plot analysis. In this study a small library of natural compounds (fifty compounds) was docked to the ACE2 binding site of the modelled surface glycoprotein of SARS-CoV-2 using Auto Dock Vina to repurpose these inhibitors for SARS-CoV-2. Conceptual density functional theory calculations of best eight compounds had been performed by Gaussian-09. Geometry optimizations for these molecules were done at M06-2X/ def2-TZVP level of theory. ADME parameters, pharmacokinetic properties and drug likeliness of the compounds were analyzed in the swissADME website.
Results:
In this study we analysed the sequences of surface glycoprotein, nucleocapsid phosphoprotein and envelope protein obtained from different parts of the globe. We have modelled all the different sequences of surface glycoprotein and envelop protein in order to derive 3D structure of a molecular target which is essential for the development of therapeutics. Different electronic properties of the inhibitors have been calculated using DFT through M06-2X functional with def2-TZVP basis set. Docking result at the hACE2 binding site of all modelled surface glycoproteins of SARS-CoV-2 showed that all the eight inhibitors (Actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) studied here many folds better compared to hydroxychloroquine which has been found to be effective to treat patients suffering fromCOVID-19 pandemic. All the inhibitors meet most of criteria of drug likeness assessment.
Conclusion:
We will expect that eight compounds (Actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) can be used as potential inhibitors against SARS-CoV-2.
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