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4th International Conference on Innovative Computing (ICIC) ; : 360-+, 2021.
Article in English | Web of Science | ID: covidwho-1985467


Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61% in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.

4th International Conference on Innovative Computing (ICIC) ; : 541-+, 2021.
Article in English | Web of Science | ID: covidwho-1985465


The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society's well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness;and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.

4th International Conference on Innovative Computing (ICIC) ; : 120-128, 2021.
Article in English | Web of Science | ID: covidwho-1985464


The COVID-19 virus spread around the globe very rapidly during early 2020. Identification of the evolution pattern, and genome scale mutations in SARS-CoV-2 is essential to study the dynamics of this disease. The genomic sequences of thousands of SARS-CoV-2 infected patients from different countries are publicly available for sequence based in-depth analysis. In this study, the DNA sequences of SARS-CoV-2 from the COVID-19 infected patients (having or lacking a travel history) from Pakistan and India, the two highest populous neighboring countries in South Asia, have been analyzed by using computational tools of phylogenetics. These analyses revealed that the SARS-CoV-2 strain in Pakistani traveler COVID-19 patients is closely related to Iranian strains, the strain in non-traveler patients is related to the strain of Wuhan, China. Likewise, in India, the SARS-CoV-2 strains in travelers and non-travelers are closely related to Italy, Germany, and Mexico. The selected approach has also been utilized to find out the identical genomic regions and similar strains around the world. Collectively, our study suggested distinct strains and routes of viral transmission in Pakistan and India. These differences may infer partially the reason for the decline phase in viral propagation in Pakistan two months after the peak COVID-19 load, and rapid viral propagation in India making it the second worst-hit country in the world after the USA.

15th International Conference on Information Technology and Applications, ICITA 2021 ; 350:207-217, 2022.
Article in English | Scopus | ID: covidwho-1844322


COVID-19 has been affecting people around the globe. It is affecting almost every country currently, according to the World Health Organization (WHO). This virus is transmitted to another person if an asymptomatic person makes close contact with the everyday person. There is no cure for this virus, and the only solution is social distancing and avoids the people doing these activities. In this paper, we proposed a system for detecting and recognizing the activities that violate social distancing. These activities involve handshakes and hugging. We implement a system that is capable of detecting and identifying multiple parallel activities. Temporal features are extracted for similar activities in 16 frames. We use the two models for this purpose: Faster RCNN for the detection and ResNet-50 to recognize the activities. First, Faster RCNN detects the group of people and that region of interest ROI saved and passes to the ResNet-50 to recognize the activities. We also generated our dataset on the local environment in multiple parallel activities. This system achieves the accuracy of 95.03% for the detection of testing dataset and recognition of multiple parallel activities 92.88% accuracy accomplished. The system used different public datasets and generated some local datasets for handshake and hugging activities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Ieee Access ; 9:100040-100049, 2021.
Article in English | Web of Science | ID: covidwho-1331655


Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.