RESUMEN
With the spread of Covid-19, more people wear personal protective equipment such as gloves and masks. However, they are littering them all over streets, parking lots and parks. This impacts the environment and damages especially the marine ecosystem. Thus, this waste should not be discarded in the environment. Moreover, it should not be recycled with other plastic materials. Actually, they have to be separated from regular trash collection. Furthermore, littering gloves and masks yields more workload for street cleaners and presents potential harm for them. In this paper, we design a computer vision system for a street sweeper robot that picks up the masks and gloves and disposes them safely in garbage containers. This system relies on Deep Learning techniques for object recognition. In particular, three Deep Learning models will be investigated. They are: You Only Look Once (YOLO) model, Faster Region based Convolutional Neural Network (Faster R-CNN) and DeepLab v3+. The experiment results showed that YOLO is the most suitable approach to design the proposed system. Thus, the performance of the proposed system is 0.94 as F1 measure, 0.79 as IoU, 0.94 as mAP, and 0.41 s as Time to process one image.
RESUMEN
Community pharmacies play a significant role in providing medicines, vaccines, consultations, and other important health services to the public. Community pharmacies continued to provide their services during the COVID-19 pandemic in most countries around the world, and this was the case in Jordan. During the COVID-19 pandemic, the pharmacy staff needs to avoid the risk of exposure to the virus causing COVID-19 along with reducing the risk for customers. This paper summarizes the safety practices of most community pharmacies in Jordan during the COVID-19 pandemic, to protect staff and customers from the risk of exposure to COVID-19 infection. Data were collected in two folds. First, a survey was distributed online through social media targeting those pharmacists working in community pharmacies. Second, face-to-face interviews were conducted with the staff and owners of pharmacies in Jordan, asking about the procedures followed to enhance the safety practices of pharmacists. Analyzing responses revealed that since the start of the pandemic, about 94% of pharmacists were using personal protective equipment, 88% of pharmacies were frequently sterilizing the pharmacy and the main door handle, and 82% of pharmacies were providing medical masks, gloves, and alcohol at the entrance.Copyright © 2023 DSR Publishers/The University of Jordan. All Rights Reserved.
RESUMEN
With the spread of Covid-19, more people wear personal protective equipment such as gloves and masks. However, they are littering them all over streets, parking lots and parks. This impacts the environment and damages especially the marine ecosystem. Thus, this waste should not be discarded in the environment. Moreover, it should not be recycled with other plastic materials. Actually, they have to be separated from regular trash collection. Furthermore, littering gloves and masks yields more workload for street cleaners and presents potential harm for them. In this paper, we design a computer vision system for a street sweeper robot that picks up the masks and gloves and disposes them safely in garbage containers. This system relies on Deep Learning techniques for object recognition. In particular, three Deep Learning models will be investigated. They are: You Only Look Once (YOLO) model, Faster Region based Convolutional Neural Network (Faster R-CNN) and DeepLab v3+. The experiment results showed that YOLO is the most suitable approach to design the proposed system. Thus, the performance of the proposed system is 0.94 as F1 measure, 0.79 as IoU, 0.94 as mAP, and 0.41 s as Time to process one image. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.