Prolonged Stress, Anxiety, and Depression in Frontline Medical Staff Working with COVID-19 Patients

Huxley, Barbara

doi:10.22381/pihrm8220208

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is sad to realize that frontline workers are insufficient to treat an enormous group of infected people. There has been a shortage of PPEs and this reduces the efficiency of the treatment of infected patients (Huxley et al, 2020). The Covid-19 has been a strong catalyst for the development of robots.…”

Section: Literature Reviewmentioning

confidence: 99%

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

al.¹

2022

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

The development of robotic partners to take care of daily human life has been expanded recently. Mobile robots have spread their presence within the public environment to assist people in a variety of problematic activities. Mobile Robots are developed with the underlying artificial intelligence technology. Adequate training is provided to the mobile robots under the classifications of supervised learning. The interaction of robots is very important to practice everything that is told to the robotic systems from domestic robots to high-risk work environments that threaten the health of the spinal cord, which focuses on robotic support during the COVID-19 epidemic. In the present research work, a mobile agent is trained using Computerized Tomography (CT) scan reports and X-rays under VGG-16 processing standards for classifying covid and non-covid patients. A hybrid model is designed using Deep Learning Network (DNN) and Convolutional Neural Network (CNN). CNN is trained using images collected using a camera and thermal camera with RGB values ranging from 0 to 255. The advantage of the proposed model in training the mobile agent is making use of CT scan and X-ray images and providing recommendations to the victim about the criticality of being affected by covid. In addition to that, the Machine Learning Algorithm like Decision Tree and Random Forest is constructed and achieved a classification accuracy of 95%. The proposed technique has efficiently provided a reliable recommendation system based on ReLu activation. The other evaluation parameters used to estimate the performance of the proposed model are precision, recall, F1-score. The proposed model achieves 0.84 Precision over the inception technique with 0.79 precision. The reason behind the improvement of accuracy in the present work is the filter used to extract the features.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

al.¹

2022

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

show abstract

Prolonged Stress, Anxiety, and Depression in Frontline Medical Staff Working with COVID-19 Patients

Cited by 1 publication

References 14 publications

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

Contact Info

Product

Resources

About