ABSTRACT
Due to the global spread of COVID-19, the world's educational institutions had been ordered to close. As a direct result of this, the time-tested method of acquiring knowledge by visiting classes is gradually being replaced by online education. In virtual classrooms, teachers had difficulty detecting student postures and determining whether or not students were comprehending the material. This research suggests using a computationally efficient method based on computer vision and machine learning to determine the attention levels of e-learning students. The method extracts characteristics using HoG and SIFT. Using K-means and PCA, the resulting feature vector is optimized for dimension reduction. The attentiveness is classified using the classifiers Decision Tree, KNN, Random Forest, and SVM. Random Forest yielded the best accuracy at 99.2% with a dataset of 15000 images. © 2022 IEEE.
ABSTRACT
Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated. © 2022 IEEE.
ABSTRACT
Advancements in technology have enabled researchers to gather large-scale mobility information cost-effectively. In fact, with millions of active users, location-based social media (LBSM) platforms such as Facebook, Twitter, Instagram, Flickr, etc., have become potential big data sources to measure individual behaviour. Despite such passive data collection techniques primarily not providing individual-level information, the sheer volume of such data facilitates a better understanding of aggregate patterns. Besides, conducting the conventional transportation survey during the initial waves of the COVID-19 pandemic was nearly impossible due to social- distancing and lockdown rules. In such context, the present research showcases a method for extracting the mobility traces and identifying the travel patterns of visitors and residents in Delhi from geo-labelled posts on Twitter. Initially, a heuristic classification strategy has been developed based on a few spatiotemporal assumptions to identify and differentiate visitors and residents based on user coordinates. Also, three supervised machine learning techniques, i.e., support vector machine (SVM), k-nearest neighbours (kNN) and decision tree, were used to classify users based on their historical coordinates. Afterwards, the spatial variation of their destination preferences was studied using K-Means, DBSCAN, and Means-Shift clustering techniques, out of which the K- Means clustering method performed best. Lastly, the travel patterns from tweets during pre and during pandemic (COVID-19) were compared using respective clusters. We observed that the performance of the proposed heuristic classifier is comparable with the supervised machine learning (ML) technique used for classification. Furthermore, the results indicate that the proposed model can successfully identify the cluster coordinates for visitors' spots as well as the locations of residents. During the pandemic situation, the mean distance travelled by users is significantly reduced. The study also shows that the number of long-distance trips has also decreased. Also, during COVID, tweets were done from very few unique tourist spots. This suggests lower tendencies of people to travel for tourism purposes. The proposed methods for classification and clustering in the present study will be crucial to obtain individual travel patterns from LBSM data. © 2023 World Conference on Transport Research Society
ABSTRACT
Context: We describe the treatment of cancer patients carried out in a Government of India-designated, dedicated coronavirus disease (COVID) hospital (DCH) in a COVID hotspot in India. Aims: The aim was to study the change and delay in the management of cancer patients during the pandemic and its complications. Settings and Design: This was an observational cohort study conducted at a tertiary care center, which was also a DCH. Subjects and Methods: Cancer patients receiving cancer surgery, chemotherapy, and radiotherapy in our DCH, during the lockdown, were studied. Results: A total of 864 patients received treatment for cancer in our hospital during the period of March 20, 2020 - May 31, 2020. There were no COVID-related complications. The treatment of 109/864 patients (12.61%) was delayed due to the pandemic and lockdown situation and the treatment plan was changed for 84/864 (9.72%) patients. There were 21 deaths in these 864 patients (2.43%), but only two deaths were COVID related. Symptomatic patients were tested for COVID, and 3/864 patients (0.34%) were detected to be COVID positive. Conclusions: We successfully delivered cancer treatment to patients in our DCH. The percentage of adverse effects, symptomatic COVID infection, and related mortality has been very low in our study. Cancer care can be continued with due diligence even during this pandemic.