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      CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

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          Abstract

          The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s41324-021-00408-3.

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          Cell entry mechanisms of SARS-CoV-2

          Significance A key to curbing SARS-CoV-2 is to understand how it enters cells. SARS-CoV-2 and SARS-CoV both use human ACE2 as entry receptor and human proteases as entry activators. Using biochemical and pseudovirus entry assays and SARS-CoV as a comparison, we have identified key cell entry mechanisms of SARS-CoV-2 that potentially contribute to the immune evasion, cell infectivity, and wide spread of the virus. This study also clarifies conflicting reports from recent studies on cell entry of SARS-CoV-2. Finally, by highlighting the potency and the evasiveness of SARS-CoV-2, the study provides insight into intervention strategies that target its cell entry mechanisms.
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            Bidirectional recurrent neural networks

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              Is Open Access

              Online Learning: A Panacea in the Time of COVID-19 Crisis

              Educational institutions (schools, colleges, and universities) in India are currently based only on traditional methods of learning, that is, they follow the traditional set up of face-to-face lectures in a classroom. Although many academic units have also started blended learning, still a lot of them are stuck with old procedures. The sudden outbreak of a deadly disease called Covid-19 caused by a Corona Virus (SARS-CoV-2) shook the entire world. The World Health Organization declared it as a pandemic. This situation challenged the education system across the world and forced educators to shift to an online mode of teaching overnight. Many academic institutions that were earlier reluctant to change their traditional pedagogical approach had no option but to shift entirely to online teaching–learning. The article includes the importance of online learning and Strengths, Weaknesses, Opportunities, & Challenges (SWOC) analysis of e-learning modes in the time of crisis. This article also put some light on the growth of EdTech Start-ups during the time of pandemic and natural disasters and includes suggestions for academic institutions of how to deal with challenges associated with online learning.
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                Author and article information

                Contributors
                sourabhshastri@gmail.com
                kuljeetshan94@gmail.com
                monudeswal19@gmail.com
                skh.sachinkumar@gmail.com
                vibhakar20@yahoo.co.in
                Journal
                Spat. Inf. Res.
                Spatial Information Research
                Springer Singapore (Singapore )
                2366-3286
                2366-3294
                12 June 2021
                : 1-14
                Affiliations
                [1 ]GRID grid.412986.0, ISNI 0000 0001 0705 4560, Department of Computer Science and IT, , University of Jammu, ; Jammu, Jammu & Kashmir 180006 India
                [2 ]GRID grid.413618.9, ISNI 0000 0004 1767 6103, All India Institute of Medical Sciences (AIIMS), ; New Delhi, India
                Author information
                http://orcid.org/0000-0001-6373-398X
                http://orcid.org/0000-0003-2592-8625
                http://orcid.org/0000-0002-1810-708X
                Article
                408
                10.1007/s41324-021-00408-3
                8196282
                a6e7a424-7f37-4b21-aa0f-6a20bafd8160
                © Korean Spatial Information Society 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 5 February 2021
                : 19 May 2021
                : 30 May 2021
                Categories
                Article

                education,lstm,deep learning,forecasting,endothelial dysfunction,covid-19

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