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1.
26th International Conference on Pattern Recognition, ICPR 2022 ; 2022-August:5170-5176, 2022.
Article in English | Scopus | ID: covidwho-2191915

ABSTRACT

Due to the rapid spread of COVID-19 as a global pandemic, it has become increasingly critical to have fast, cheap, and reliable tools to assist physicians in diagnosing COVID19. Several automated systems using deep learning techniques have demonstrated promising results by analyzing Computed Tomography (CT-scan) or X-ray data to complement conventional diagnostic tools. In this paper, we aim to emphasize the role of point-of-care ultrasound imaging using deep learning as a tool to detect COVID-19 more prominently. Ultrasound imaging is non-invasive and widely available in medical facilities all over the world. This paper presents an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. It classifies lung ultrasound (LUS) images of patients into COVID-19 and Non-COVID-19 categories. The lack of COVID-19 data makes it challenging to train a traditional CNN from scratch, so we have adapted a transfer learning approach instead of training the base classifiers VGG16, ResNet-50, and GoogLeNet. We apply the gained knowledge in the target domain of small lung ultrasound frames, considering the ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework is evaluated on a publicly available dataset, achieving 96.7% accuracy. The proposed architecture outperforms the state-of-the-art method on the same dataset and proves to be a reliable COVID-19 detector. © 2022 IEEE.

2.
Acs Pharmacology & Translational Science ; 6(1):171-180, 2023.
Article in English | MEDLINE | ID: covidwho-2185538

ABSTRACT

SARS-CoV-2 main protease (Mpro/3CLpro) is a crucial target for therapeutics, which is responsible for viral polyprotein cleavage and plays a vital role in virus replication and survival. Recent studies suggest that 2-phenylbenzisoselenazol-3(2H)-one (ebselen) is a potent covalent inhibitor of Mpro, which affects its enzymatic activity and virus survival. Herein, we synthesized various ebselen derivatives to understand the mechanism of Mpro inhibition by ebselen. Using ebselen derivatives, we characterized the detailed interaction mechanism with Mpro. We discovered that modification of the parent ebselen inhibitor with an electron-withdrawing group (NO2) increases the inhibition efficacy by 2-fold. We also solved the structure of an Mpro complex with an ebselen derivative showing the mechanism of inhibition by blocking the catalytic Cys145 of Mpro. Using a combination of crystal structures and LC-MS data, we showed that Mpro hydrolyzes the new ebselen derivative and leaves behind selenium (Se) bound with Cys145 of the catalytic dyad of Mpro. We also described the binding profile of ebselen-based inhibitors using molecular modeling predictions supported by binding and inhibition assays. Furthermore, we have also solved the crystal structure of catalytically inactive mutant H41N-Mpro, which represents the inactive state of the protein where the substrate binding pocket is blocked. The inhibited structure of H41N-Mpro shows gatekeeper residues in the substrate binding pocket responsible for blocking the substrate binding;mutation of these gatekeeper residues leads to hyperactive Mpro.

3.
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2161373

ABSTRACT

The fast proliferation of the coronavirus around the globe has put several countries' healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep Learning approaches were used in several computer-aided automated systems that utilized chest computed tomography (CT-scan) or X-ray images to create diagnostic tools. However, current Convolutional Neural Network (CNN) based approaches cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. To mitigate the problem, we have developed a self-attention-based Vision Transformer (ViT) architecture using CT-scan. The proposed ViT model achieves an accuracy of 98.39% on the popular SARS-CoV-2 datasets, outperforming the existing state-of-the-art CNN-based models by 1%. We also provide the characteristics of CT scan images of the COVID-19-affected patients and an error analysis of the model's outcome. Our findings show that the proposed ViT-based model can be an alternative option for medical professionals for effective COVID-19 screening. The implementation details of the proposed model can be accessed at https://github.com/Pranabiitp/ViT. © 2022 IEEE.

4.
2022 International Joint Conference on Neural Networks, IJCNN 2022 ; 2022-July, 2022.
Article in English | Scopus | ID: covidwho-2097615

ABSTRACT

The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model. © 2022 IEEE.

5.
6th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021 ; 311:811-819, 2023.
Article in English | Scopus | ID: covidwho-2094541

ABSTRACT

As the global economy grapples with the advent of novel coronavirus and its variants, the aftermath has left all industries with ongoing uncertainties and incalculable loss of life and livelihood in most countries worldwide. In such unpredictable situations, the insurance industry and governments worldwide have become the prominent source of optimism to sail through the situation. This applies to the insurance industry globally, which is currently in the grip of fear due to the COVID-19 outbreak and anticipating significant economic slowdown and hardship because insurance rides on the back of other Industries. Therefore, to overcome a few of the tenacious roadblocks due to the COVID outbreak, Insurers will be forced to reassess all aspects of their business life cycle and take necessary steps to continue operations with minimum disruption. Precisely, the impact of COVID on General Insurers and Life and Health Insurers varied depending on the lines of business, product lines, and a bouquet of benefits offered by the insurers. The pandemic has taken a hit on new gross written premiums on specific lines of business, such as medical, travel, commercial, and business insurance. Few lines of business such as motor and home have remained muted during the COVID timeframe. However, the claims volumes for personal insurance (e.g., motor) have significantly decreased due to the lockdown and travel restriction;the industry has witnessed the highest claims volumes in life and health compared to the past several decades. They say, “As every dark cloud has a silver lining,” it has given an opportunity to many insurers to develop new products (e.g., Pay Mile Auto insurance) and push toward greater productivity, i.e., digital capability across product range which will result in an elevated position to understand and address to the customer and intermediary self-service (such as Portals) and implicit and explicit needs. Notably, the Insurance industry is likely to lean toward offering personalized yet custom-made products and services, which are sharply focused on preventative care and embracing digitalization across the value chain. Besides enabling scalability and connectivity, insurers are strategically focused on digitizing the core of the business and cloud implementation;automation across the insurance value chain is necessary to compete successfully with new innovative product development or inclusive business models. Around the globe, the insurance industry is continuously putting a deep focus on revitalizing the technology paradigm to grow and strive to achieve cost-effectiveness amid emerging markets, rapidly changing economic conditions and stiff competition from Insurtech. According to industry experts across geographies, growth may be a balanced blend of preventative and protective approaches, with a gamut of new and improved services and products, and insurers are deeply fostering redefining service-oriented strategies and innovative products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

6.
ACS Sustainable Chemistry and Engineering ; 10(30):9811-9819, 2022.
Article in English | Scopus | ID: covidwho-2016557

ABSTRACT

For the past two years, doxycycline has been employed hugely for the treatment of COVID 19 over the globe. Excessive use of doxycycline can result in bacteria and gene resistance, which affects the future treatment of infectious diseases. Furthermore, unused doxycycline left from the hospital and pharmaceutical industries may have an adverse effect on the environment, posing a significant menace to modern society. As a result, doxycycline detection is required. Herein, we developed blue luminous nitrogen-doped carbon quantum dots (N-CQDs) using ascorbic acid and diethylenetriamine (DETA) as carbon and nitrogen sources via a microwave-assisted technique for the differential detection of doxycycline (DC) via a fluorescence quenching mechanism, even when other tetracycline derivatives interfere. The quenching mechanism has been elaborately explained by using a Stern-Volmer plot, UV-vis and fluorescence spectroscopy, and TCSPC to attribute the static quenching and inner filter effect. In addition, the limit of detection of our suggested sensor is 0.25 μM. To confirm the structural properties and the size of the N-CQDs, FT-IR, Raman spectroscopy, HRTEM, DLS, and EDX have been performed. Moreover, this approach was used to identify doxycycline in pharmaceutical waste and bacterial cells. Because of its great sensitivity and selectivity, N-CQDs are ideal for measuring DC in environmental applications. © 2022 American Chemical Society. All rights reserved.

7.
EAI/Springer Innovations in Communication and Computing ; : 33-49, 2022.
Article in English | Scopus | ID: covidwho-1826183

ABSTRACT

Internet of Things (IoT) has become one of the important components in developing interconnected smart IoT devices. Data generated from the IoT devices increases rapidly due to the increase in the number of connected devices. The current COVID-19 outbreak condition has led to the need of the Healthcare IoT (H-IoT), which can provide an automatic solution for monitoring. Therefore, IoT data is extremely crucial to be analyzed. Artificial Intelligence (AI) has gained a lot of attentions for automatizing applications based on the big data generated from the IoT devices. This chapter presents the current development of AI applications for monitoring the pandemic. The role of IoT, data acquisition, preprocessing, and analysis is also described here. In depth, we elucidate few methods of data preprocessing using conventional techniques and Machine Learning (ML) algorithms, and data analysis using ML and Deep Learning (DL) algorithms. We list all techniques in handling data preprocessing and analysis, and the challenges of IoT and AI in the new way of living during pandemic which is also known as the era of new normal. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

8.
Benchmarking ; 2022.
Article in English | Scopus | ID: covidwho-1794949

ABSTRACT

Purpose: The already scarce financial resources coupled with the current COVID-19 pandemic have created the worst scenario for Indian micro, small and medium enterprises (MSMEs). The application of supply chain finance (SCF) solutions to MSMEs can enhance the performance and growth of the sector. But, the implementation of SCF solutions faces various obstacles which restrict the MSMEs' ability to meet their financial requirements. The purpose of this paper is to explore and prioritize the various important barriers hindering SCF application in Indian MSMEs. Design/methodology/approach: Literature on SCF and MSMEs are critically reviewed and barriers affecting the SCF application in Indian MSMEs are scrutinized with the consultation of the experts. The present study applies intuitionistic fuzzy-analytic hierarchy process (IF-AHP) methodology to prioritize the identified barriers and thereafter, the sensitivity analysis is also done to observe the identified barriers under different situations. Findings: The results of the study have revealed that poor cash flow management and working capital management disruption are acting as the most prioritized barriers of SCF. The external factor of cultural challenges has been prioritized as the minimum-influence factor that has least negative influence on the operations of SCF in MSMEs. Practical implications: The present study bears an important practical and managerial implication to solve real world problems of financial constraints of MSMEs. The managers should emphasize upon the importance smooth flow of cash and working capital management across the supply chains by which better SCF solution can be implemented in MSMEs. Originality/value: The study conducted is an effort to address the barriers of SCF in Indian MSMEs during the COVID-19 pandemic. The implementation of IF-AHP and sensitivity analysis would help managers and policymakers to comprehend and resolve the prioritized barriers and sub-barriers of SCF in the MSMEs. © 2022, Emerald Publishing Limited.

9.
6th International Conference on ICT for Sustainable Development, ICT4SD 2021 ; 321:139-147, 2022.
Article in English | Scopus | ID: covidwho-1653384

ABSTRACT

In today’s dynamic and hyper-competitive era, knowledge is the backbone of an organization and provides unflagging support in inclusive growth including financial and non-financial terms to the organization. It is essential for organizations including MSME firms to cultivate and nurture knowledge-enriching system within organizations to extract knowledge from external stakeholders, internal stakeholders (like employees), and customers through organization learning ecosystem to understand the essence of past, administer the present, and envision the future roadmap. In recent times, knowledge management practice garners significant attention at leadership level and being considered as strategic initiative in organization which helps in achieving competitive advantage and propel inclusive growth. It also highlights on various factors which are being positively influenced by adopting knowledge management practices in day-to-day operations such as organization’s performance, innovation capability, improve products and services, and job satisfaction. Therefore, organization needs to define a standard practice to acquire, retain, and leverage the knowledge through “knowledge management” initiative constituting creation, acquisition, transfer, storage, and sharing of tacit and external knowledge to reap the benefits across business value chain. To promote the growth of knowledge management in organization’s setting, knowledge management system (KMS) and practice play paramount role with the support of disruptive technologies. Additionally, disruptive technologies arm not only simplify activities to manage knowledge but also enhance experience of the business users. Ubiquitous penetration of disruptive technologies, primarily accelerated by rapid adoption of digital channels (accelerated through COVID), advent of FinTech and InsurTech and adjacencies that many players are getting into with their scale. The small, medium, and micro-sized firms must explore digital technologies to improve customer experience, time to market and reduce costs by honing organizational knowledge. Also, they had to find additional dollars to invest in Next-Gen Technologies-led platforms including Cloud, API, Agile, DevOps, AI/ML, intelligent automation to stay relevant for their customers. Precisely in MSME context, knowledge management is one of the most important factors to secure sustainable growth, create unique offerings (products and services), accelerate business performance and attain inclusive growth. Whether MSME firms are ready to take up the challenge to marry existing knowledge management system and practice with new age disruptive technologies to secure first mover advantage and set an example to Industry and nation for radical growth? © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

10.
2021 6th International Conference for Convergence in Technology ; 2021.
Article in English | Web of Science | ID: covidwho-1364892

ABSTRACT

Tin this paper, we propose and implement a real-time hand motion detection system which uses shadows projected by hand and detected by low cost Light Dependent Resistor (LDR). The main advantage of the shadow approach is real-time recognition and its application in Post COVID 19 world where contactless interactions are required. Our proposed approach uses 2D hand motion shadows to determine sequence of light blocking the LDRs and thus making the system less complex and less compute intensive compared to 3D image recognition based systems. The accuracy of the proposed system is tested under various lighting conditions and 82-95\% accuracy is observed under normal lighting conditions.

11.
Studies in Computational Intelligence ; 963:271-290, 2022.
Article in English | Scopus | ID: covidwho-1353634

ABSTRACT

Presently the world is facing an extremely tough time due to the prevalence of the Novel Coronavirus, 2019-nCoV or COVID-19, which has been declared a pandemic by WHO. The virus usually transmits via droplets of saliva or discharge from the nose when an infected person coughs or sneezes. Since there is no vaccine to prevent the disease, social distancing and proper quarantine of infected persons are needed. To include and quantify the spatial effect of the pandemic regarding the geotemporal development of 2019-nCoV, a mathematical model of partial differential equations is essential. In this chapter a diffusion model has been developed by dividing the total population of constant size into four classes: susceptible population, infected population, quarantined population and recovered population. Here the disease transmission factor for both infected and quarantined population into the susceptible population are in more general form. Looking for a travelling wave solution this model can give the wave speed at which the disease 2019-nCoV spread. Additionally it can be predicted whether the total population in forward time will become susceptible or not in the absence of vaccine. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

12.
Engineered Science ; 14:109-113, 2021.
Article in English | Scopus | ID: covidwho-1239343

ABSTRACT

The time comes now to really think about the national policy over contraceptive pill after COVID-19 mental depression. This planet might face an adverse effect on biodiversity after using such contraceptive pills. After preventing unwanted pregnancy, the consumed birth control pills pass from human to water system and start their undesirable second innings to suppress the fertility in aquatic lives, domestic or wild animals.Besides the aquatic lives, the presence of pill chemicals in urban and suburban water supplies can be boomeranged to innocent people. Thus, a considerable debate is raised in consequence on limit level concern but need to be stopped immediately to stop the infertility problem already, before COVID-19. In detail, steroidal estrogens at pollutant levels can develop breast cancer in women and prostate cancer in men. The steroidal estrogens at pollution level can suppress the growth of root flowering and germination. Ethinyl estradiol (EE2) is a synthetic estrogenic pill, which induces intersex in freshwater fish and caused significant drops in populations. It is the high time to retrospect the national policies on using the estrogenic pill. © Engineered Science Publisher LLC 2021.

13.
International Journal of Current Research and Review ; 13(6 special Issue):142-149, 2021.
Article in English | Scopus | ID: covidwho-1196194

ABSTRACT

Background: The emergence of the Coronavirus disease (COVID-19) pandemic has caused an unprecedented global catas-trophe in the 21st century as a major virus outbreak. The disease as well as the different preventive measures taken to contain the disease especially quarantine and lockdown, loss of income, loss of job and financial insecurity have led to an enormous impact on the mental health of the community and various psychological problems in the form of anxiety, depression and stress. Objective: This article aims to highlight the extent of the impact of COVID-19 on mental health with a special focus on depression in the general population of Odisha. Methods: This is a cross-sectional study carried out among the general population of Odisha through an online semi-structured questionnaire the link of which was sent to the participants by way of e-mails, WhatsApp and other social contacts. Data analysis of the received responses was done. Various statistical analyses were adopted using methods like Microsoft Excel, 2013, R version 4.0.2 software, t-test and Chi-square tests. Significant predictor analysis was done using logistic regression. Results: The incidence of depression to the tune of 43% was found (Mild-10.28%, Moderate-16.19%, Severe-5.56%, Extremely severe – 9.96%). Risk factors associated were the younger age group (21-40 years), unmarried persons (71.5%0, students (51.1%), persons not having symptoms of COVID-19 (78.4%), and persons without jobs (47.8%). Conclusion: The COVID-19 pandemic is associated with highly significant levels of depression and is the topmost priority concern. It is important to take adequate measures to mitigate the severity of the impact. Early identification of worsening mental health and prompt response to address the problem can prevent further damage. ©@IJCRR.

15.
Studies in Economics and Finance ; 2021.
Article in English | Scopus | ID: covidwho-1153339
16.
Int J Environ Sci Technol (Tehran) ; 18(5): 1269-1286, 2021.
Article in English | MEDLINE | ID: covidwho-1107899

ABSTRACT

This paper analyses air quality data from megacity Delhi, India, during different periods related to the COVID-19, including pre-lockdown, lockdown and unlocked (post-lockdown) (2018-2020) to determine what baseline levels of air pollutants might be and the level of impact that could be anticipated under the COVID-19 lockdown emission scenario. The results show that air quality improved significantly during the lockdown phases, with the most significant changes occurring in the transportation and industrially dominated areas. A pronounced decline in PM2.5 and PM10 up to 63% and 58%, respectively, was observed during the lockdown compared to the pre-lockdown period in 2020. When compared to 2018 and 2019, they were lower by up to 51% and 61%, respectively, dropping by 56% during unlock. Some pollutants (NOx and CO) dropped significantly during lockdown, while SO2 and O3 declined only slightly. Moreover, when compared between the different phases of lockdown, the maximum decline for most of the pollutants and air quality index occurred during the lockdown phase 1; thus, this period was used to report the COVID-19 baseline threshold values (CBT; threshold value is the upper limit of baseline variation). Of the various statistical methods used median + 2 median absolute deviation (mMAD) was most suitable, indicating CBT values of 143 and 75 ug/m3 for PM10 and PM2.5, respectively. This results although preliminary, but it gives a positive indication that temporary lockdown can be considered as a boon to mitigate the damage we have done to the environment. Also, this baseline levels can be helpful as a first line of information to set future target limits or to develop effiective management policies for achieving better air quality in urban centres like Delhi. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-021-03142-3.

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