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1.
Mobile Information Systems ; 2022, 2022.
Article in English | Web of Science | ID: covidwho-2005523

ABSTRACT

The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

2.
Mobile Information Systems ; 2022, 2022.
Article in English | Scopus | ID: covidwho-1950372

ABSTRACT

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.

3.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:17297-17308, 2022.
Article in English | Scopus | ID: covidwho-1874880

ABSTRACT

Background: Coronavirus illness is a new pathological ailment that is sweeping the world. Sars (severe acute respiratory syndrome) the virus that causes it is coronavirus 2. (sars-cov-2). The sars-spike cov-2 protein (s) has two subtypes: s1 and s2.it is vital to the recognition of receptors and the fusion of biological membranes that the (s) component of the sars-spike cov-2 influenza is present. Covalent coupling of the transcription factor domain of the s1 component to the host ligand binding of angiotensin-converting enzyme 2 allows it to function, whereas the s2 subunit's two-helix recurring domain enhances the attachment of the virus to cells. In this appraisal, we high spot topical expansions in the edifice, function, and advance of antiviral medications that target the s protein. There has been an explosion of viruses that have been creating a new beginning in recent years such as coronaviruses (covs). Human and animal hosts have been infected with these pathogens, causing disorders that range from upper respiratory tract infections in humans to encephalitis and demyelination in animals, which are all potentially life-threatening. From across the world, they've killed many people and animals, and caused havoc in the healthcare system. The current threat from coronaviruses (covs) is among the most horrifying illnesses. They've infected a wide range of human and animal hosts, instigating diseases that assortment from upper respiratory infections in humans to encephalitis and demyelination in animals, all of which are potentially lethal. They've killed a lot of people and animals, and they've caused a lot of health problems all around the world. Result:- It's vital to create new tactics to avert or switch coronavirus infections, as well as a better understanding of their biology, replication, and pathogenesis. As a result, we used experimental and computational research to characterise the structure and functions of covs proteins in this review. Conclusion:- This knowledge might principal to a better sympathetic of cov proteins' roles in virus repetition and transcription, as well as the development of novel antivirals. © The Electrochemical Society

4.
Ieee Transactions on Engineering Management ; : 11, 2022.
Article in English | Web of Science | ID: covidwho-1819855

ABSTRACT

The Government of India has started the distribution of the Covishield and Covaxin vaccines across all the comprising states. In developing states like Bihar, the vaccine supply chain (VSC) is likely to face many challenges due to poor health infrastructure, multidimensional poverty, and poor literacy. Supply chain practitioners are experimenting with available technologies to deal with supply, demand, and behavioral challenges. The present research work seeks to inspect the potential impact of Internet of Things (IoT) on the performance of the VSC. This study draws literature on the impact of IoT on product management, demand management, supply management, social behavior, and government rules and regulations to develop and test the conceptual model in Bihar. Consequently, the study administrated a survey and used structural equation modeling to investigate the proposed hypothesis. The analysis illustrates the positive influence of IoT adoption on the performance of the VSC in distributing the COVID vaccine. The finding also shows the positive relationship between product, supply, demand, and social behavior in IoT adoption. Analysis displays that Indian politicians can substantially impact vaccine distribution because they have influence and awareness of their local districts. This research work has a significant theoretical and managerial contribution for government and practitioners, supporting regulatory officials and policymakers in improving vaccine distribution.

5.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333686

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) has now caused over 2 million deaths worldwide and continues to expand. Currently, much is unknown about functionally neutralizing human antibody responses and durability to SARS-CoV-2. Using convalescent sera collected from 101 COVID-19 recovered individuals 21-212 days after symptom onset with forty-eight additional longitudinal samples, we measured functionality and durability of serum antibodies. We also evaluated associations between individual demographic and clinical parameters with functional neutralizing antibody responses to COVID-19. We found robust antibody durability out to six months, as well as significant positive associations with the magnitude of the neutralizing antibody response and male sex. We also show that SARS-CoV-2 convalescent neutralizing antibodies are higher in individuals with cardio-metabolic comorbidities. SIGNIFICANCE: In this study we found that neutralizing antibody responses in COVID-19 convalescent individuals vary in magnitude but are durable and correlate well with RBD Ig binding antibody levels compared to other SARS-CoV-2 antigen responses. In our cohort, higher neutralizing antibody titers are independently and significantly associated with male sex compared to female sex. We also show for the first time, that higher convalescent antibody titers in male donors are associated with increased age and symptom grade. Furthermore, cardio-metabolic co-morbidities are associated with higher antibody titers independently of sex. Here, we present an in-depth evaluation of serologic, demographic, and clinical correlates of functional antibody responses and durability to SARS-CoV-2.

6.
6th International Conference on ICT for Sustainable Development, ICT4SD 2021 ; 314:335-343, 2022.
Article in English | Scopus | ID: covidwho-1653373

ABSTRACT

Coronavirus (COVID-19), one of the deadliest pandemic diseases of the century, escalated at such a fast rate that around 25 million people around the world got infected. The impact of the virus made it a compulsion for the people to wear masks and apply sanitizer at regular intervals. Thus, for the safe and hygiene buying process of masks, sanitizer and other pharmaceutical products, an idea of a cashless and contactless dispenser was brought up named Hygieia. Hygieia is the name of the Greek goddess of health and sanitization. The purpose of this project is to make the buying process fast as well as ensure the safety of the customers by using digital payment methods. The customer can place the order with the help of speech instead of touching the keypads which is observed in most of the vending machines. Cashless payment option is also provided which is making this product a smart and viable automated dispenser. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

7.
Kybernetes ; ahead-of-print(ahead-of-print):26, 2021.
Article in English | Web of Science | ID: covidwho-1373721

ABSTRACT

Purpose The paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India. Design/methodology/approach Through an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect. Findings A total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. "Required Learning Skills and Attitude," "Lack of Infrastructure," "Learners' Ability" and "Increased Investment" are found to be the most influential factors. Practical implications The resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education. Originality/value The study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India.

8.
Journal of Multi-Criteria Decision Analysis ; : 15, 2021.
Article in English | Web of Science | ID: covidwho-1351261

ABSTRACT

The prosed study aims to provide COVID-19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID-19 are identified and prioritized using the DEMATEL method. The findings indicate that the high-quality personal protective equipment (PPEs;LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision-makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post-COVID-19 circumstance. The policy/decision-makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision-makers swiftly build up the HCI to cope with the future pandemic.

9.
International Journal of Logistics Management ; ahead-of-print(ahead-of-print):29, 2021.
Article in English | Web of Science | ID: covidwho-1309707

ABSTRACT

Purpose In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI's influence on supply chain risk mitigation (SCRM). Design/methodology/approach This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses. Findings This study's findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence. Originality/value This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

10.
International Journal of Logistics Management ; 2021.
Article in English | Scopus | ID: covidwho-1281935

ABSTRACT

Purpose: This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context. Design/methodology/approach: 20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used. Findings: The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties. Research limitations/implications: This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care. Originality/value: This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP. © 2021, Emerald Publishing Limited.

11.
Nepalese Heart Journal ; 18(1):7-11, 2021.
Article in English | EMBASE | ID: covidwho-1234643

ABSTRACT

Background and Aims: Cardiovascular comorbidities are common in patients with COVID-19 and these patients are at higher risk of morbidity and mortality. It is not known if the presence of cardiovascular co-morbid conditions poses independent risk or whether this is mediated by other factors. Methods: This is a retrospective follow up study done at Shahid Gangalal National Heart Centre (SGNHC). The main objective of this study was to study the clinical profile, baseline comorbidities, and outcome of cardiac patients and health care worker diagnosed with COVID 19. This study retrospectively evaluated case records of all cardiovascular disease (CVD) patients admitted at SGNHC with COVID 19 cases from 1st case diagnosed on July at SGNHC till September 2020. Results: During this study period, 90 patients with COVID 19 with cardiovascular disease were admitted. The mean age of the study population was 52.3±19 years with 65.6% being male. Among the study population 52 (57.8%) had past history of cardiovascular disease, hypertension in 18 (20%) cases, diabetes in 8 (8.9%) cases. Among the patients with cardiovascular diagnosis, acute coronary syndrome was most common cardiovascular diagnosis in 23 (25.6% cases) followed by rheumatic heart disease in 21 (23.4%) cases, dilated cardiomyopathy in 7 (7.8% cases), ischemic cardiomyopathy with reduced ejection fraction (EF) in 7 (7.8%) cases, post coronary artery bypass graft (CABG) in 8 (8.9%), post valve replacement in 5 (5.5%), congenital heart disease in 3.3% cases and complete heart block in 3.3% cases. Most of the cases were symptomatic with moderate illness in 46.7% cases, mild illness in 41.4% cases and severe/critical illness in 11.1% cases. Among COVID patients with cardiovascular disease, the mortality was 11.1%. Conclusion: Patients with cardiovascular disease with COVID 19 have more severe COVID 19 symptoms and has higher COVID 19 related death, so strict vigilance and early intervention is needed to improve its outcome.

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