ALow Cost QuArm Band for Controlling Contagious Diseases

In the recent times it is found that there is a growing interest in the field of controlling the contagious diseases, especially after the outbreak of the novel COVID-19 (coronavirus). It still remains to be one of the biggest threats to humanity and people are dying and getting infected on a daily basis. Governments across the globe are trying their level best to contain the virus. They are also taking the necessary steps (e.g., travel bans, suspension of recreational and outdoor activities concerning mass audiences or public, isolation and contact tracing, social distancing, etc.). There are many patients who are undocumented just because they have coronavirus in their systems but they show no symptoms. Around 79% patients come under this category. It is to be noted that the total count of the number of cases at present in several countries differ from the actual people who are infected at present. This is because in the maj ority of cases, the symptoms show after a certain period of days and not just instantly. Also testing the whole population of a country in such a limited time is simply not possible. The World Health Organization recommended COVID-19 patients to isolate themselves from the healthy individuals in order to stop the spread of the disease. In order to ensure that this happens more efficiently and smoothly, in this paper an IoT based wearable band called QuArm band (i.e) Quarantine Arm band, which the patient can wear on his/her arm for tracking the real-time location of the patient to ensure that the quarantine rules are being followed is designed. This band is made keeping in mind the requirements of the public and the cost is set accordingly. Web interface alongside the band is made to retrieve the information. Notification on band tampering is also enabled. © 2022 IEEE.

Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine

Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community's interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated. © Published under licence by IOP Publishing Ltd.

COVID-19 and Pneumonia Detection using Hybrid VGG-16 model using Chest X-rays

The time frame of 2020 to present day 2022 primarily highlights the COVID-19 pandemic. The humanity is being largely affected by SARS-CoV-2(The Severe Acute Respiratory Syndrome CoronaVirus 2) because of its highly infectious characteristic which can be even fatal in severe cases. The World Health Organization (WHO), have reported over 544.3 million verified cases of COVID-19 globally till date, including over 6.3 million deaths. The reason why SARS-CoV-2 is considered to be a dangerous illness is due to this relatively high mortality and contagious rates, in addition to asymptomatic individuals also being carriers of the virus. The only way to identify susceptible populations and to attempt to control the spread would be via RT-PCR COVID testing of all individuals, which is time consuming and expensive. The challenges of this testing mechanism and the prolonging end of the pandemic are the primary motivation to bring up an effective system over a large test cases with a reduced time constraints. This paper proposes a combination of the pretrained convolutional neural network, VGG-16(Visual Geometry Group-16) and GRU(Gated Recurrent Unit) to differentiate the Pneumonia and COVID-19 attack from chest X-rays(CXRs). The proposed model employs VGG-16 to extract features from the CXR inputs, and the GRU classifies it. We experimented this model over 6939 CXR images with 3 classes (COVID-19, Pneumonia, and Normal) and the training produced encouraging macro average precision, recall, and f1-score of 0.9525, 0.9524, and 0.9524 respectively. These results indicate hybrid deep learning systems can greatly aid in the early detection of COVID-19 using CXRs and thereby reduce the widespread of the pandemic. We believe that early diagnosis can be easily and effectively done using this model. © 2022 IEEE.

Deep Neural Network Architecture for Face Mask Detection Against COVID-19 Pandemic Using Pre-trained Exception Network

The coronavirus disease (COVID-19) is an infectious disease caused by coronavirus. The COVID-19 virus spreads mostly through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so it is important to practice respiratory etiquette. The COVID-19 is spreading our community in a faster manner, stay safe by taking some simple precautions, such as physical distancing, wearing a mask, keeping rooms well ventilated, avoiding crowds, and cleaning hands. The appropriate use of wearing a mask is a normal part of our life. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel severe acute respiratory syndrome coronavirus. Genetic variants of SARS-CoV-2 have been emerging and circulating around the world throughout the COVID-19 pandemic. To minimize the risk of transmissions, the use of face masks or coverings has been recommended in public settings. Many countries and local jurisdictions encourage or mandate the use of face masks by members of the public to limit the spread of the virus. Masks are also strongly recommended for those who may have been infected and those taking care of someone who may have the disease. In this paper, novel face mask detection on masked face data set is done by using pretrained Xception, deep learning with depth wise separable convolution. The proposed method classifies from the given face image, mask is worn or not. The proposed method is tested and validated using the face mask data set obtained from Kaggle. This data set contains about 503 face images with mask and 503 images without mask. The experimental results show that the proposed face mask detection method significantly dominates other compared pretrained models. The results of the receiver operating characteristic curve and area under curve justify the relevance of the better results in favor of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Learning based Identification of Covid-19 Future Forecasting

The propagation of Covid-19 disease spread across the world has put humanity in serious risk. The largest global economies are alarmed by the widespread infections or contagiousness. As Covid-19 is regarded as a viable concern in the eyes of humanity, the emerging Machine Learning [ML] models have the potential to estimate the number of prospective patients impacted by Covid-19. In particular, the value identification models, namely Exponential Smoothing (ES) and Support Vector Machine (SVM) are used by considering the significant factors of Covid-19. Every model is governed by three types of assumptions: the number of contaminated cases, fatalities, and recoveries. In some cases, the existing model fails to infer the patients' real outcomes. To mitigate this challenge, the proposed technique introduces the Exponential Smoothing (ES) model to extrapolate and predict the number of Covid-19 cases based on the effectiveness of preventive measures such as convivial segregation or confinement based on the disease severity. © 2022 IEEE.

Analysis of Travelers’ Risk Perceptions About Public Transit Systems During COVID-19 Pandemic

The epidemic novel coronavirus disease 2019, abbreviated as COVID-19, has changed people?s mobility choices significantly, which has had a great impact on public transportation because of the public?s risk perception. The pandemic forced many people to shift toward private transport modes, which resulted in a decrease in public transport ridership and significantly altered travel behavior in urban areas. In this context, the present study investigated the public?s COVID-19 risk perception when public transportation is used (i.e., risk-taking behavior) and factors that significantly affect the use of public transportation. To fulfill this objective, a Google form-based questionnaire was prepared and circulated online. A total of 1,720 responses were collected using the survey form. These responses were processed for outliers and incomplete responses, and a total of 1,486 data samples were used for the analysis. A factor-based regression model was developed to study the risk-taking behavior of travelers while using public transportation during the COVID-19 pandemic. From the analysis, it is inferred that the travelers? attitude negatively correlated with risk-taking behavior, whereas technology, motivation, concerns, and education positively affected COVID-19 risk perception when using public transit. Further, the study concluded that the behavior of travelers has a significant impact on their risk-taking behavior through their attitude and social norms. The findings of this study will be useful to urban transport planners in making suitable policies to increase public transportation ridership during pandemics.

Probing the competitive inhibitor efficacy of frog-skin alpha helical AMPs identified against ACE2 binding to SARS-CoV-2 S1 spike protein as therapeutic scaffold to prevent COVID-19.

In COVID-19 infection, the SARS-CoV-2 spike protein S1 interacts to the ACE2 receptor of human host, instigating the viral infection. To examine the competitive inhibitor efficacy of broad spectrum alpha helical AMPs extracted from frog skin, a comparative study of intermolecular interactions between viral S1 and AMPs was performed relative to S1-ACE2p interactions. The ACE2 binding region with S1 was extracted as ACE2p from the complex for ease of computation. Surprisingly, the Spike-Dermaseptin-S9 complex had more intermolecular interactions than the other peptide complexes and importantly, the S1-ACE2p complex. We observed how atomic displacements in docked complexes impacted structural integrity of a receptor-binding domain in S1 through conformational sampling analysis. Notably, this geometry-based sampling approach confers the robust interactions that endure in S1-Dermaseptin-S9 complex, demonstrating its conformational transition. Additionally, QM calculations revealed that the global hardness to resist chemical perturbations was found more in Dermaseptin-S9 compared to ACE2p. Moreover, the conventional MD through PCA and the torsional angle analyses indicated that Dermaseptin-S9 altered the conformations of S1 considerably. Our analysis further revealed the high structural stability of S1-Dermaseptin-S9 complex and particularly, the trajectory analysis of the secondary structural elements established the alpha helical conformations to be retained in S1-Dermaseptin-S9 complex, as substantiated by SMD results. In conclusion, the functional dynamics proved to be significant for viral Spike S1 and Dermaseptin-S9 peptide when compared to ACE2p complex. Hence, Dermaseptin-S9 peptide inhibitor could be a strong candidate for therapeutic scaffold to prevent infection of SARS-CoV-2.

Degree-based molecular descriptors of certain chemical graphs and drugs of COVID 19

Spectral Graph Theory is the interface that connects the graphs and the matrices associated with them. Chemistry is the branch of science which has benefitted the most out of this interaction as the information-theoretic matrices of the molecular graphs offer immensely useful molecular descriptors. Graph energy and topological indices are chemistry-initiated directions of research in mathematics. In this paper, we compute the characteristic polynomial of Circum-coronene(n) series of benzenoid graphs, 1 <= n <= 3 and few degree-based topological indices of Circum-polyacenes(m, n), Circum-pyrene(n) and Circum-trizene(n). Also, the graph invariants namely energy, spectral radius, Wiener index, first Zagreb index, modified first Zagreb index, second Zagreb index and modified second Zagreb index have been computed for few proposed drugs against COVID-19, their extensions and coronoid networks. We have also verified our results using MATLAB programs.

The association between smartphone addiction and insomnia incidence in students of the faculty of medicine udayana university batch of 2020 during the coronavirus disease-19 pandemic

BACKGROUND: The use of smartphones is increasing in the coronavirus disease (COVID-19) pandemic for various purposes, this encourages smartphone addiction. In addition, the incidence of insomnia has also increased in the pandemic era. AIM: This study was conducted to find an association between smartphone addiction and the incidence of insomnia, especially among students of the Faculty of Medicine, Udayana University. METHOD: This research is a descriptive-analytic study with the cross-sectional method, using two main questionnaires, Smartphone Addiction Scale-Short Version, and Insomnia Severity Index. Questionnaires were distributed using Google forms and then collected and analyzed using software SPSS version 25. RESULT: Overall the total research respondents with the inclusion criteria in this study amounted to 364 people. The results showed that 212 respondents (58.24%) had a high level of smartphone addiction and 152 respondents (41.76%) had a low level of smartphone addiction. In addition, 187 respondents (51.37%) experienced mild insomnia, 87 respondents (23.9%) experienced moderate insomnia, 13 respondents (3.57%) experienced severe insomnia, and 77 respondents (21.15%) did not experience insomnia. Based on the results of data analysis, it was found that smartphone addiction had a significant relationship (p = 0.002) with weak and positive correlation (r = 0.162) to the incidence of insomnia. CONCLUSION: It was found that the majority of respondents experienced high levels of smartphone addiction and mild insomnia. Another finding suggests the higher addiction to the smartphones, the more severe insomnia suffered.