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1.
Annals of African Medicine ; 22(1):40-44, 2023.
Article in English | MEDLINE | ID: covidwho-2217228

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) reporting and data system (CO-RADS) grade of high-resolution computed tomography (HRCT)-thorax scan investigation is an innovative tool for the diagnosis of COVID-19 patients. By this tool, majority of moderate-to-severe COVID-19 patients are screened to detect lung pathologies. Hardly any study has explored its use vis-a-vis reverse transcriptase-polymerase chain reaction (RT-PCR) in asymptomatic patients.

2.
5th International Conference on Communication, Device and Networking, ICCDN 2021 ; 902:223-232, 2023.
Article in English | Scopus | ID: covidwho-2048169

ABSTRACT

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be “more expensive, difficult, and time-consuming”. Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes “comprehension reading work” is treated as fundamental in the proposal of disclosures. In case of any, “the programme appeared to positively affect contributing students”. Techniques of classification like “decision tree and Mixed Model Database Miner (MMDBM)” are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
2021 4th International Conference on Signal Processing and Information Security (Icspis) ; 2021.
Article in English | Web of Science | ID: covidwho-2042775

ABSTRACT

The modernization of advanced healthcare infrastructure in the early 21st century is still failing to cope up as the whole world is struggling to get rid of the deadly disease named COVID-19. The scarcity of clinical resources is one of the most fundamental as well as critical reasons behind this calamity. The entire healthcare system faces severe challenges to re-stabilize the system. Digitization of technology which is primarily driven by the next-generation communication networks has given an exclusive paradigm shift to resolving the issues. 5th generation of mobile communication-(5G) introduces classical techniques which play crucial roles in e-healthcare transformations. Software-Defined Networking(SDN), Network Function Virtualization (NFV), Network Slicing (NS), and the concept of programmable networks introduce URLLC communication and time-critical service delivery in a resource-restricted environment. Leveraging the concept of programmable slicing approach, in this work, we have framed a flexible e-healthcare slicing model for dedicated and optimized resource provisioning. We have introduced a vSDN server that can significantly balance the healthcare slice and classify the complex medical databases into simplified segments for quick data processing, management, and orchestration. Considering the reformation of global healthcare customization, our proposed approach will play a vital role in the field of the e-healthcare domain.

4.
International Journal of Early Childhood Special Education ; 14(5):3859-3866, 2022.
Article in English | Web of Science | ID: covidwho-2006511

ABSTRACT

Consistent performance evaluation of mutual funds is crucial for investors and fund managers. In India mutual funds have not been as favourable investment options as in other developedcountries.The Indian mutual fund industry continuesextremely underpenetrated. India's AUM-to-GDP ratio is about 14% as compared to a worldwide average of 75-80% in March 2022. The equity AUM-to-GDP ratio is only 5%. Though, the situation is expected to change in the years to come.Retail investors are becoming more and more interested in mutual fund investments as per data shown by AMFI. The retail investors in the mutual funds' assets under management was 24%, which indicates an increase of 300 bps in September 2021 against the number in September 2020.In addition, AMFI's campaign 'Mutual Fund Sahi Hai' has attracted retail investors from smaller cities. To extend the investor base for mutual funds in India, it is essential to know the impact of income on monthly investment behaviour of investors towards mutual funds during COVID-19, and impact of investor awareness program on monthly investment. In these circumstances, the present research work is dedicated to achieving these objectives. The data were collected from individual investors from different states of India in 2022.The study aims to find out the impact of income andawareness programs on monthly investments towards mutual funds investment during COVID-19.An attempt has also been made to study the level of impact of 'Mutual Fund Sahi Hai' campaign on the retail investors. The data analysis has been done with the SPSS software and by using multivariate and bivariate techniques.

5.
14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022 ; : 222-226, 2022.
Article in English | Scopus | ID: covidwho-1722904

ABSTRACT

The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.

6.
4th International Conference on Signal Processing and Information Security, ICSPIS 2021 ; : 88-91, 2021.
Article in English | Scopus | ID: covidwho-1708798

ABSTRACT

The modernization of advanced healthcare infrastructure in the early 21st century is still failing to cope up as the whole world is struggling to get rid of the deadly disease named COVID-19. The scarcity of clinical resources is one of the most fundamental as well as critical reasons behind this calamity. The entire healthcare system faces severe challenges to re-stabilize the system. Digitization of technology which is primarily driven by the next-generation communication networks has given an exclusive paradigm shift to resolving the issues. 5th generation of mobile communication-(5G) introduces classical techniques which play crucial roles in e-healthcare transformations. Software-Defined Networking(SDN), Network Function Virtualization (NFV), Network Slicing (NS), and the concept of programmable networks introduce URLLC communication and time-critical service delivery in a resource-restricted environment. Leveraging the concept of programmable slicing approach, in this work, we have framed a flexible e-healthcare slicing model for dedicated and optimized resource provisioning. We have introduced a vSDN server that can significantly balance the healthcare slice and classify the complex medical databases into simplified segments for quick data processing, management, and orchestration. Considering the reformation of global healthcare customization, our proposed approach will play a vital role in the field of the e-healthcare domain. © 2021 IEEE.

7.
IEEE International Conference on Communications (ICC) ; 2021.
Article in English | Web of Science | ID: covidwho-1559839

ABSTRACT

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models' overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVID-CT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance combined with medical equipment and help ease the examination procedure.

8.
2021 IEEE International Conference on Communications, ICC 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1447821

ABSTRACT

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models' overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVIDCT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID19 detection with excellent performance combined with medical equipment and help ease the examination procedure. © 2021 IEEE.

9.
Ieee Consumer Electronics Magazine ; 10(4):18-27, 2021.
Article in English | Web of Science | ID: covidwho-1307643

ABSTRACT

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives. Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19. However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered. In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.

10.
Bangladesh Journal of Infectious Diseases ; 7(Supplementary Issue):S16-S21, 2020.
Article in English | GIM | ID: covidwho-961598

ABSTRACT

Background: Variation and atypical presentation of COVID-19 in Bangladeshi children has been noticed.

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