ABSTRACT
The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed. © 2023 IEEE.
ABSTRACT
The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.
ABSTRACT
Coronavirus was first detected in the year 2019 in Wuhan, China. The disease rapidly spread across the country in a short span of time. The Government had imposed strict rules and restrictions for lockdown and social distancing, work from home, and online classes to prevent the further spread of these covid cases During this phase, the morality of the covid cases was significantly controlled. But the larger population was affected by this. So, the mindset of the people has been changed. Sentimental analysis is an opinion mining approach to NLP which is used to detect and categorize the data as positive, negative, and neutral. In a situation like the COVID pandemic, one must stay in a positive mindset. In our project, we are implementing sentimental analysis using the Random Forest algorithm along with comparing the trend in variation of COVID 19 cases using the LSTM and KNN algorithms. © 2022 IEEE.
ABSTRACT
The entire globe is going through a severe health crisis due to the rapid spread of Covid-19 Disease, which in turn creating a deep impact on the human lives and their day-to-day livelihood. Only hope of preventing it from further spread of disease is by following all the precautionary measures provided by the WHO. One of the most important safety measures is to wear face mask and maintain social distancing. Hence, we have proposed to develop a system can monitor whether a person is wearing a facemask correctly/not in real time. This will help to reduce the rapid spread of the disease in public places and various other organizations. We have proposed a solution that uses Artificial Intelligence and has a capability to detect the violation of wearing a face mask in real-time using Image Recognition and Video Techniques. The main intention of the proposed work is to provide the front-end software (Web or mobile application) for administrators to monitor the violation. The system will capture the violated instance and store the data. Our system will also include a User registration form where the users have to register their information along with their face images. This will be an input to the face recognition model, which will train and alert the user whenever violation occurs. © 2021 IEEE.