ABSTRACT
COVID-19 is a global health crisis during which mask-wearing has emerged as an effective tool to combat the spread of disease. During this time, non-technical users like health officials and school administrators need tools to know how widely people are wearing masks in public. We present a robust and efficient Mask Adherence Estimation Tool (MAET) based on the pre-trained YOLOv5 object detection model and combine it with explanation methods to help the user understand the mask adherence at an individual and aggregate level. We include two novel explanation methods to compute a high-fidelity importance map based on two black-box explanation methods. For our work, we experimented with one-stage and two-stage object detector architectures. Experiment results show that MAET achieves state-of-the-art results on a public face mask dataset, with improved performance by 2.3 % precision and 0.4 % recall in face detection and 2.0 % precision and 1.7 % recall in mask detection. We used three different evaluation metrics for explanation and find that no method dominates all metrics;therefore, we support multiple explanation methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 is a global health crisis during which mask-wearing has emerged as an effective tool to combat the spread of disease. During this time, non-technical users like health officials and school administrators need tools to know how widely people are wearing masks in public. We present a robust and efficient Mask Adherence Estimation Tool (MAET) based on the pre-trained YOLOv5 object detection model and combine it with explanation methods to help the user understand the mask adherence at an individual and aggregate level. We include two novel explanation methods to compute a high-fidelity importance map based on two black-box explanation methods. For our work, we experimented with one-stage and two-stage object detector architectures. Experiment results show that MAET achieves state-of-the-art results on a public face mask dataset, with improved performance by 2.3 % precision and 0.4 % recall in face detection and 2.0 % precision and 1.7 % recall in mask detection. We used three different evaluation metrics for explanation and find that no method dominates all metrics;therefore, we support multiple explanation methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.
ABSTRACT
We are very pleased to present the conference proceedings of ICIAST-2021. The conference has been organized in virtual mode due to worldwide COVID Protocol and safety measures by Department of Applied Sciences, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India during December, 21-23, 2021, in Greater Noida, Uttar Pradesh, India.In this conference approx. 300 young researchers, engineers, scientists, academicians and industrial delegates participated from more than 20 countries of the globe on common virtual platform of zoom to share and exchange their research findings including theoretical results, novel scientific models and work in progress in the different areas of Science & Technology. During virtual conversations participants from remote areas faced sometime network issues but overall communication was good throughout the even. More than 30 international resource persons from different fields share their expertise with the young academicians and researcher with case studies and concrete examples within allowed time span. The scientific & technical program of the three days international conference have review talks, invited lectures, contributed oral presentations.This proceeding includes papers from Physics, Mathematics and their applications. A sound understanding of the same shall help emergence of new ideas that can be helpful in building trained professionals who can serve in the knowledge-based industries. We are pleased to appreciate our dynamic reviewers who took time and effort for providing their valuable comments in time and help towards the improvement of quality of papers through rigours review process.List of Organizing Committee, image, ICIAST-2021 Proceeding Editorial Team are available in this pdf.