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1.
4th International Conference on Innovative Computing (ICIC) ; : 360-+, 2021.
Article in English | Web of Science | ID: covidwho-1985467

ABSTRACT

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.

2.
4th International Conference on Innovative Computing (ICIC) ; : 541-+, 2021.
Article in English | Web of Science | ID: covidwho-1985465

ABSTRACT

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.

3.
4th International Conference on Innovative Computing (ICIC) ; : 120-128, 2021.
Article in English | Web of Science | ID: covidwho-1985464

ABSTRACT

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.

4.
Gastroenterology ; 162(7):S-374, 2022.
Article in English | EMBASE | ID: covidwho-1967301

ABSTRACT

Background: Pancreatic involvement in patients with Coronavirus 2019 (COVID-19) has been reported in the literature. The pancreatic injury in COVID-19 patients might be a result of the direct cytopathic effect of viral replication or indirectly related to the immune response to the viral infection. Methods:Westudied 183 patients diagnosed with symptomatic SARS-CoV-2 and admitted to COVID-19 facilities in Qatar. We included only the patients with documented positive SARS-COV-2 PCR and measured lipase levels. The cohort was categorized into two groups based on the serum lipase level. The cutoff was the elevation of the serum lipase more than three times the upper limit of normal. Patients with lipase levels below the cutoff were included in the first group, and those with lipase levels above the cutoff were included in the second group. The primary outcome was mortality. The secondary outcomes were disease severity on presentation and markers of disease progression. Markers of disease progression (Table 1) included the development of acute respiratory distress syndrome (ARDS), shock, multi-organ failure, the requirement for ICU admission, mechanical ventilation, continuous renal replacement therapy (CRRT), and extracorporeal membrane oxygenation (ECMO). Results: Our study population had a mean age of 49 and a mean BMI of 28. There was a male predominance in the study sample (more than 91%), reflecting the country's demographics. There was no statistically significant difference between the two groups in the mean age, BMI, gender distribution, or patients' reported symptoms. There was an increased prevalence of diabetes mellitus (DM) and hypertension (HTN) in our study population (45.4% and 44.8%). Apart from the increased prevalence of chronic liver disease in the second group, there was no statistically significant difference in the prevalence of comorbidities (e.g., DM, HTN) between the two groups (Table 1). The second group showed a statistically significant increase in mean creatinine, troponin, procalcitonin, ferritin, and amylase compared to the first group. On the other hand, the mean hemoglobin, sodium and albumin were lower (Table 2). Interestingly, more patients in the second group received tocilizumab and oseltamivir (Table 1). The mortality rate in our study population was 15.3%, with a higher mortality rate in the second group (Table 1). Almost 50% of the patients developed ARDS. Multiple markers of disease progression, including the development of ARDS, shock, and multi-organ failure;requirement for ICU, mechanical ventilation, and CRRT were increased in the second group compared to the first group. Also, the mean length of stay was higher in the second group (Table 1). Conclusion: Based on our study, hospitalized patients with COVID-19 who had higher lipase levels had a higher mortality rate and higher risk for disease progression. (Table Presented)

5.
15th International Conference on Information Technology and Applications, ICITA 2021 ; 350:207-217, 2022.
Article in English | Scopus | ID: covidwho-1844322

ABSTRACT

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.

6.
6th International Multi-Topic ICT Conference, IMTIC 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1794833

ABSTRACT

The World Health Organization has designated COVID-19 a pandemic because its emergence has influenced more than 50 million world's population. Around 14 million deaths have been reported worldwide from COVID-19. In this research work, we have presented a method for autonomous screening of COVID-19 and Pneumonia subjects from cough auscultation analysis. Deep learning-based model (MobileNet v2) is used to analyze a 6757 self-collected cough dataset. The experimentation has demonstrated the efficiency of the proposed technique in distinguishing between COVID-19 and Pneumonia. The results have demonstrated the cumulative accuracy of 99.98%, learning rate of 0.0005 and validation loss of 0.0028. Furthermore, cough analysis can be performed for other patients screening of other pulmonary abnormalities. © 2021 IEEE.

7.
4th International Conference on Robotics and Automation in Industry, ICRAI 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1701922

ABSTRACT

In this paper, we have performed transfer learning using different pre-trained convolutional neural networks for binary classification of X-ray images into COVID-19 disease and normal. The dataset is gathered from two open sources. Our dataset is consisting of 254 COVID-19 and 310 Normal X-ray images. The pandemic situation all around the world demands an efficient solution so that the disturbance of global health, daily life, and economy can be controlled. In this regard, we introduced the deep feature fusion-based technique which could help to design an embedded system. We fine-tuned and trained the thirteen independent pre-trained models and we found that the Resnet50V2 model performed efficiently for binary classification scenarios. Our proposed technique using transfer learning gives a detection rate of 99.5% for binary classification (Normal and COVID). © 2021 IEEE.

8.
2021 International Conference on Computing, Electronic and Electrical Engineering, ICE Cube 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1672727

ABSTRACT

Coronavirus (COVID-19) is a catastrophic illness that has already infected several million individuals and caused thousands of fatalities globally. Any technical technique that enables quick testing of the COVID-19 with high accuracy might be essential for healthcare providers. X-ray imaging is an easily available technique that might be a great option for its quick detection. This research was conducted to examine the usefulness of artificial intelligence (AI) to detect COVID-19 quickly and accurately from chest X-ray scans. The objective of this study is to provide a solid technical method for the automatic identification of COVID-19, Pneumonia, Lung opacity, and Normal digital X-ray scans using pretrained, deep learning algorithms while optimizing detection accuracy. Inception v3 with an additional added dense layer is used with image augmentation to train and validate the selected dataset. The obtained accuracy of 99.72% promises speedy detection of COVID-19. © 2021 IEEE.

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

ABSTRACT

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.

10.
J Biomol Struct Dyn ; 40(6): 2851-2864, 2022 04.
Article in English | MEDLINE | ID: covidwho-1026871

ABSTRACT

Ivermectin (IVM) is a broad-spectrum antiparasitic agent, having inhibitory potential against wide range of viral infections. It has also been found to hamper SARS-CoV-2 replication in vitro, and its precise mechanism of action against SARS-CoV-2 is yet to be understood. IVM is known to interact with host importin (IMP)α directly and averts interaction with IMPß1, leading to the prevention of nuclear localization signal (NLS) recognition. Therefore, the current study seeks to employ molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) analysis and molecular dynamics simulation studies for decrypting the binding mode, key interacting residues as well as mechanistic insights on IVM interaction with 15 potential drug targets associated with COVID-19 as well as IMPα. Among all COVID-19 targets, the non-structural protein 9 (Nsp9) exhibited the strongest affinity to IVM showing -5.30 kcal/mol and -84.85 kcal/mol binding energies estimated by AutoDock Vina and MM-GBSA, respectively. However, moderate affinity was accounted for IMPα amounting -6.9 kcal/mol and -66.04 kcal/mol. Stability of the protein-ligand complexes of Nsp9-IVM and IMPα-IVM was ascertained by 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of protein in complex with docked IVM exhibited stable root mean square deviation while root mean square fluctuations were also found to be consistent. In silico exploration of the potential targets and their interaction profile with IVM can assist experimental studies as well as designing of COVID-19 drugs. Communicated by Ramaswamy H. Sarma.


Subject(s)
COVID-19 , Ivermectin , Antiviral Agents/chemistry , COVID-19/drug therapy , Humans , Ivermectin/pharmacology , Ivermectin/therapeutic use , Molecular Docking Simulation , SARS-CoV-2 , alpha Karyopherins
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