ABSTRACT
The conventional procedure used in all of India's major regions is attendance monitoring on paper with pens. Although the final data is computerized, it takes a long time to get from a classroom to a database. The effectiveness of the classes is directly impacted by the number of absences. The attendance takes up almost half of the lecture's allotted time. The alternative method that is being used involves using fingerprints, but even this approach is ineffective since it takes so long. Due to the illnesses (COVID-19) spreading over the world, however, the situation as it stands right now does not make this the best course of action. Therefore, it will be advisable to develop a contactless and more efficient. © 2023 IEEE.
ABSTRACT
Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we'll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine. © 2022 IEEE.
ABSTRACT
Digital devices are presently being used in most of the public places like ATM, ticket booking counters, exit surveys in showroom. The digital devices used in these places needs direct contact with the screen. In pandemic situation what we are presently in, this physical contact paths way for the spread of disease. Use of digital devices without the physical contact is the need of the day. In this work a virtual mouse is proposed which does not need any contact and can be used in public places without any harm. The current technologies both wired and wireless needs a physical mouse for the cursor control. The proposed model made the Human Computer Interface (HCI) technology move to the next stage of development with Hand Gesture Recognition. So this avoids the need of physical mouse. The initial step begins capturing of the image with the inbuilt or external camera. Further the image is processed and then move with the tracking of various points in the hand with the algorithm developed. The next process is allocating various cursor controls to the various operations in the cursor to different hand gestures, like clicking is done by joining index finger and middle finger. The software's we utilise in this project are python. The output of the image is shown in the system screen to move with further process. Through this hand gestures we could able to perform various clicking and cursor moving operations without using physical mouse. Hence, the proposed work helps us to minimise the spreading of covid-19 by avoiding human interaction and the computer's reliance on hardware devices. © 2022 IEEE.
ABSTRACT
BACKGROUND: Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. RESULTS: pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. CONCLUSIONS: We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.