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


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.

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


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.

Pharmacologyonline ; 2:962-971, 2021.
Article in English | EMBASE | ID: covidwho-1576301


Medical students have found distance learning to be a difficult challenge as a result of the coronavirus disease 2019 (COVID-19) pandemic. This study investigated the correlation between academic performance, sleep quality, and burnout among Medical students who participated in distance learning during the COVID-19 pandemic. The study included 154 Medical students at Government Erode Medical College in Tamilnadu, data were collected in June 2021. The survey was conducted using a Google Forms containing informed consent along with Demographic Details, self-rated sleep quality, academic performance, and The Maslach Burnout Inventory –Student Survey burnout questionnaire. Correlation between academic performances, sleep quality, Emotional Exhaustion, Cynicism, and Academic Efficacy was analyzed using Karl Pearson correlation method. 18% of students experienced severe burnout during distance learning. 60% of students don’t like online classes. More than 60 % internal mark scored students (P=0.02) are having significantly high Academic Efficacy Scores. Cronbach’s Alpha is 0.7498. Distance learning was reported a significant negative impact on their academic performance. To develop a favourable learning environment for medical students, medical instructors should consider creative learning methodologies.