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1.
2022 IEEE International Conference on Communications, ICC 2022 ; 2022-May:1444-1449, 2022.
Article in English | Scopus | ID: covidwho-2029230

ABSTRACT

Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses' variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher's phase, and the support vector machine method to compute the fitness function of optimisation. © 2022 IEEE.

2.
Medical Journal of Dr. D.Y. Patil Vidyapeeth ; 15(7):S14-S23, 2022.
Article in English | Scopus | ID: covidwho-2024831

ABSTRACT

Background: Medical students are already under extreme academic pressure which causes disruption in their sleep patterns. Due to the COVID-19 pandemic, though they have been relieved of their hectic schedules this has also led to complete lack of hands-on training and bedside clinical teaching which might have given rise to increased anxiety in this population. Aims: To assess the sleeping pattern and determinants of poor sleep quality among medical students during the COVID-19 pandemic. Materials and Methods: A cross-sectional study was conducted among 343 undergraduate medical students of a tertiary care teaching hospital from August 17, 2020, to September 17, 2020, via an online questionnaire containing questions on sociodemographic parameters, lifestyle factors, Pittsburgh Sleep Quality Index, COVID-19-related stress and academic delay-related stress. Statistical Analysis: Analysis was done with the Statistical Package for the Social Sciences (SPSS) Version 20.0. Results: About 52.47% had a poor sleep quality;92.4% had a high level of academic uncertainty-related stress and 64.7% experienced a high level of COVID-19-related stress. Significant association was found between Poor Sleep Quality (PSQI score) and urban residence, nuclear family, smoking, excessive caffeine consumption, and high levels of COVID-19 stress. Conclusion: Although poor sleepers had decreased in number from before the COVID-19 pandemic, they were still much higher than the general population. This might be due to high levels of academic delay-related stress present virtually in the entire population. This can be circumvented by proper counseling of the students and sensitive planning of the academic activities once the pandemic will over. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.

3.
AIMS Biophysics ; 8(4):346-371, 2021.
Article in English | Scopus | ID: covidwho-1964164

ABSTRACT

The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

4.
Journal of Scientometric Research ; 11(1):47-54, 2022.
Article in English | Web of Science | ID: covidwho-1897066

ABSTRACT

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

9.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:1199-1210, 2022.
Article in English | Scopus | ID: covidwho-1874751

ABSTRACT

In this study using publicly available panel data on bio-medical waste from all the Apollo-Hospitals in Chennai we try to explore the effect of COVID-19 pandemic on four categories (clinical, infectious, sharp and bottle waste) waste generation. The entire sample study is categorized into pre-lockdown (no-suspension on activities) and lockdown period (all activities were suspended) as was declared in India by the government in 2020. Using a fixed effect model technique and COVID-19 active treatment cases in the district where the hospital is located, we find significant effect of severity of COVID19 on hospital's wastes generation. Severity of COVID-19 in the districts increases the proportion of infectious waste generation but reduces the proportion of sharp and bottle waste generation to the total waste. The results indicate the huge increase in the bio-medical waste generation during the post COVID-19 era which pauses a threat to both sanitation and sustainable development goals, is a result of infectious waste and is generated from some few hospitals with specialty treatment like childcare and cancer, ignoring these features can lead to upward bias in the estimation. © The Electrochemical Society

10.
1st International Conference of IoT and its Applications, ICIA2020 ; 825:293-301, 2022.
Article in English | Scopus | ID: covidwho-1750632

ABSTRACT

The unprecedented rise and spread of the pandemic in form of nCOVID-19 has really raised high concerns in the socioeconomic front. The usual diagnosis is made by an RT-PCR test, which is highly specific can incorrectly identify some nCOVID-19 individuals to cause a serious compromise in overall accuracy. Since the drug application in its full swing is still some months away, hence, the need of the hour is to find a more accurate technique which can be used by health care centers having basic point of care facilities. The increase in the number of cases in India and lack of test kits in some of the less known diagnostic centers has added more concerns to the increasing problems. Additionally, the test kits incur a significant cost making it less affordable to some of the diagnostic centers. Hence, this research group in this article has proposed an algorithm centered around the concept of Internet of Things, a dual deep learning based algorithm, and collating the decision by a strong decision fusion technique. The objective of the algorithm is to detect and isolate the nCOVID-19 subjects in a cost-effective way to keep a check on the spread. This pandemic detection and isolation technique (PANDIT) is based on two different radiography image technology and uses a state-of-the-art deep learning algorithm for the purpose. The radiography technique has long been the most acceptable technique for cases related to pneumonia. The group has developed the algorithm based on X-ray and CT scan as its training data. The novelty of this paper is best described by a multi-fold methodology. Firstly, the significance of radiography imaging for detecting and identification of COVID-19 subjects. A simple connected value chain driven by Internet of Things (IoT) would enable the isolation process in an efficient and accelerated manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

11.
International Journal of Statistics in Medical Research ; 10:146-160, 2021.
Article in English | Scopus | ID: covidwho-1591784

ABSTRACT

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem. © 2021 Lifescience Global. All Rights Reserved.

12.
3rd International Conference on Computational Advancement in Communication Circuits and Systems, ICCACCS 2020 ; 786:349-354, 2022.
Article in English | Scopus | ID: covidwho-1499393

ABSTRACT

As the technology advances, the modern trend of lifestyle also advances. The doorbell has an important responsibility in home safety;it is one of the competent and steady systems needs to be developed for better safety which could be access at a low cost. In this era, there are many doorbells systems doing different operation. This paper focuses on touchless type automatic doorbell systems which will ring the bell automatically when a visitor approaches near the door. This system is intended to people, and due to the spread of COVID-19 pandemic situation, it would be one of the safety steps that can be taken against corona. People are now more careful about their everyday work and their family. In the year 2020, the whole world is trapped in unprecedented COVID-19 pandemic. The situation takes away all our normal lifestyle, and all the researches are going on in controlling the situation and finding a new way of life. In this work, the author is trying to establish a contactless door alarm for the household application. Motivation behind the work is that due to the Corona virus spread around the world, we have to take utmost care in every step of our life. If we use the normal door alarm, then there will be the issue of contact for every people who will arrive in. But if there will be a replacement of the conventional door alarm with the help of antenna technology, then it can solve the issue with a contactless alarm. In this paper, the author have used the HFSS software for the proposed antenna. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

13.
Online Teaching and Learning in Higher Education during COVID-19: International Perspectives and Experiences ; : 199-214, 2021.
Article in English | Scopus | ID: covidwho-1411303
14.
1st International Conference on Advances in Medical Physics and Healthcare Engineering, AMPHE 2020 ; : 393-404, 2021.
Article in English | Scopus | ID: covidwho-1353686

ABSTRACT

The entire world faced locked down scenario due to the outbreak of nCOVID-19 corona virus outbreak. The fast and relentless spread nCOVID-19 has basically segmented the populace only into three subclasses, namely susceptible, infected, and recovered compartments. Adapting the classical SIR-type epidemic modeling framework, the direct person-to-person contact transmission is taken as the direct route of transmission of nCOVID-19 pandemic. In this research, the authors have developed two models of the nation-wide trends of the outburst of the nCOVID-19 infection using an SIR model and also an ARIMA model. They have studied the quantile plots, regression residual plots and R pair plots of the dataset by simple supervised machine learning algorithms. This study compares both models and higher correlation of the developed models with reality which suggests the extent of accuracy of these models. The study also suggested some possible way-out to get rid of this situation by providing a trade-off between ‘flattening of the curve’ as well as less economic turbulence. The projections are intended to provide an action plan for the socioeconomic counter measures to alleviate COVID-19 in India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

15.
2nd International Conference on Intelligent Engineering and Management, ICIEM 2021 ; : 40-45, 2021.
Article in English | Scopus | ID: covidwho-1280230

ABSTRACT

In recent years it has been observed that there is a significant relationship between Geospatial Platform and Tourism Planning especially after the pandemic. Whenever any country's economy has had any significant impact, tourism has always been the first to get affected as people start cutting their travel budgets first. Tourism has a fairly direct role to play when it comes to COVID-19, which has spread extensively across most parts of our country with a large number of tourists. The pandemic COVID-19 has heavily hit the tourism economy of our country. Proper scientific planning is required for tourism that can bring the industry into right position. Recent Development in Geospatial Platform and its significance in Tourism Planning signifies that Geo-portal has a huge potential in the field of Tourism management. The present study is conducted into two parts by using primary and secondary data. In the first part, researchers have conducted a survey among 96 respondents of the Delhi and NCR region, to understand their opinion related to the information-seeking process during pretrip planning stage in context to geoportal use. The analysis of this is done with the help of SPSS indicated that if both COVID and destination related information can be gathered together through a single website will be a great help to plan the future trip. After which the second part of the study has been conducted to analyze the potential of web-based geo-portal in tourism taking Sikkim as sample. The study discusses the basic architecture and techniques of providing accurate travel-related information in a single window that helps visitors in their decision-making process. The final section of this chapter suggests the strategic implementation of a geoportal for effective tourism planning and management by creating a geoportal with ArcGIS 10.0 for Sikkim, India. © 2021 IEEE.

16.
Int. Conf. Adv. Comput. Innov. Technol. Eng., ICACITE ; : 158-164, 2021.
Article in English | Scopus | ID: covidwho-1218993

ABSTRACT

The nCOVID-19 has wreaked our normal lifestyle and has forced us to adopt the protocols under the new normal regime. The conventional diagnostic approach is expected to change in the process as well. Our research group is proposing an aid to such diagnostic approach. The group has proposed in this article the power of using two such diagnostic measures that has been the pivot for many diagnoses to the doctors. The art of using natural language processing based symptomatic measure in combination with a machine learning based approach based on medical vitals can collectively reduce the error percentage of detection. The approach proposed in this article is a first of its kind and the authors have achieved acceptable results on the accuracy front. The other reason of proposing such a technique is the way a fusion algorithm can arrive at the right results from two parallel algorithms doing the same task. Another objective of the group was to provide a valuable opinion to the doctor in form of such an architecture. The proposed architecture can be used at any point of care facilities without any requirement of escalation of the existing amenities. © 2021 IEEE.

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