ABSTRACT
Video conferencing applications have become an integral part of today's world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code;a video/audio conferencing feature using Peer JS;a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches. © 2021
ABSTRACT
The world is currently under the grip of the COVID-19 pandemic. The only possible escape from this pandemic is wearing a mask. Mask checks are being done in most public places. This project brings out a good technique for recognizing a mask on a face. Principal component analysis (PCA) is a dimensionality reduction method which is used in image and signal processing. It uses only the principal components of a dataset and ignores rest of the components. In this paper, three different pre-processing methods (modular, wavelet and a combination of both) have been performed on an image dataset. The resultant data has been processed through PCA. Through comparison of the processed data vectors, the similarity between images has been established. Also, a comparison between different PCA techniques is developed through recognition of distorted faces. Initially, a comparison between different PCA techniques is developed through recognition of distorted faces. Then, a comparison is done between the Modular PCA, Wavelet PCA and Modular-Wavelet PCA techniques by observing the performance for mask recognition. Finally, a conclusion is drawn as to which method is the best in terms of mask recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The world is currently under the grip of the COVID-19 pandemic. The only possible escape from this pandemic is wearing a mask. Mask checks are being done in most public places. This project brings out a good technique for recognizing a mask on a face. Principal component analysis (PCA) is a dimensionality reduction method which is used in image and signal processing. It uses only the principal components of a dataset and ignores rest of the components. In this paper, three different pre-processing methods (modular, wavelet and a combination of both) have been performed on an image dataset. The resultant data has been processed through PCA. Through comparison of the processed data vectors, the similarity between images has been established. Also, a comparison between different PCA techniques is developed through recognition of distorted faces. Initially, a comparison between different PCA techniques is developed through recognition of distorted faces. Then, a comparison is done between the Modular PCA, Wavelet PCA and Modular-Wavelet PCA techniques by observing the performance for mask recognition. Finally, a conclusion is drawn as to which method is the best in terms of mask recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
This paper provides a method to find out misinformation pertaining to topics such as the COVID-19 pandemic via an altered version of the tweet2vec model. In this version of the model, tweets with reliable and false news on the pandemic are converted into vectors based on the CBOW (Common Bag of Words) model and plotted graphically. After this, a K-Means clustering algorithm is used to determine a threshold value for the vector form to determine if the tweet has credible information or not. © 2022 IEEE.
ABSTRACT
Covid-19 pandemic is ravaging the world and humankind is facing one of the toughest challenges of this century. The main requirement is to stay safe. According to the Covid-19 protocol, a healthy person who is adjacent to an infected person for more than 15 minutes has a very high chance to get infected. Body temperature more than the normal temperature is one of the symptoms of the symbiotic Covid-19 infected person. This paper presents an idea to design a low-cost affordable wrist band that alerts the user if a person with higher body temperature is in his/her proximity. A simple component like thermopile temperature sensor, amplifier, monostable multivibrator, and LEDs are used to design this wrist band. The paper discusses the outline circuit to achieve this, which can be used by all people. © 2021 IEEE