A Deep Learning Based Approach to Measure Confidence for Virtual Interviews

Confidence means trust and a strong belief in something or someone. During an interview, demonstrating self-confidence, i.e., showing trust and faith in your own abilities can be the deciding factor for your success versus the other candidates. It is as important as showing your skills, as without self-confidence one cannot convince the interviewer to trust one’s skills, one’s ability to learn and the ability to get the work done. Demonstrating a positive body image and strong, confident personality can make a lasting impression on an interviewer, and in the future on other colleagues as well. In the age of coronavirus, the interviews have migrated to virtual platforms, so only a candidate’s face is used to gauge his/her self-confidence. Deep Learning can be helpful to gain best results on image classification for confidence detection. With the help of different types of facial expressions present in dataset we can classify and help in telling the measure of self-confidence. This work aims to develop a confident and unconfident image classifier which will help interviewees to practice confident expressions and improve their skills. The Deep Learning CNN model is trained with the help of two optimizers, namely, Adam and SGD. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Detection Using Deep Learning: A Comparative Study of Segmentation Algorithms

Lung abnormality is a prevalent condition that affects people of all ages, and it can be caused by a variety of factors. The lung illness caused by SARS-CoV-2 has recently spread across the globe, and the World Health Organization (WHO) has labelled it a pandemic disease owing to its quickness. Covid-19 mainly attacks the lungs of those infected, resulting in mortality from ARDS and pneumonia in extreme instances. Internal body organ disorders are thought to be more acute, making diagnosis more complex and time-consuming. The source of any illness, location and severity are determined by a pulmonologist basing upon a good number of tests taken in the laboratories or even outside these after the hospitalization of a patient. In between a lot of time is taken to carry out these tests and prediction of COVID 19 is done. The purpose of this work is to propose a model based on CNN and finding out the best fit segmentation algorithm to apply to the chest X-ray scans in order to predict the test result. Most importantly the result is instantaneous. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Social Distance Monitoring and Face Mask Detection Using Deep Learning

As there is no evidence of COVID-19 slowing down in several components of the world, maintaining “social distancing” (also mentioned as physical distance) between indoor and outside people is more vital than ever. It is counseled that two people keep a distance of 1.8 m (approximately, vi feet) apart. Python can be used to capture people and monitor social distancing. Deep learning, TensorFlow, Keras, and OpenCV are used to acknowledge masks;it uses a good computer vision-based technique that focuses on the automatic period observance of individuals to sight safe social distance twenty-four hours a day, seven days every week, for inspectors in public places, commercial centers, and different locations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Decision Making on Covid-19 Containment Zones’ Lockdown Exit Process Using Fuzzy Soft Set Model

Covid-19 is one of the biggest pandemics in the history of mankind. It has kept the modern world hostage for more than one and a half years now. Strict lockdown is creating more havoc in the minds of everybody. The decision of exiting from a lockdown and deciding the kind of strictness needed as per the scenario is not an easy task for any administration. This paper provides a new approach to estimate the seriousness of the situation to strategize the exit from lockdown. There are many mathematical models to handle uncertainty such as fuzzy set, rough set, soft set, generalizations of these models, and their hybrid models. In this paper, a decision-making application using the notion of a fuzzy soft set is provided to assess the seriousness of the corona situation, which helps to decide on lockdown relaxations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.