ABSTRACT
COVID19's global epidemic has wreaked havoc on our lives in every aspect. Healthcare systems, to be more specific, was pushed beyond their limits. Artificial intelligence developments have paved the way for the creation of complicated applications that can meet a wide range of requirements. Precision in clinical practice is necessary. In this study, machine learning-based deep learning models that were customized and pretrained were used. Convolutional Neural Networks that's utilized from detected COVID-19 respiratory pneumonia complications. Then more number of COVID-19 patients' radiographs pictures were collected locally. In Data was also used from three publicly available datasets. There are four options for evaluating performance. The public dataset was utilized first for training and testing. Second, data from both the local and national levels]. A variety of public sources were used to train and test the models. Because all diagnostic procedures have little retrieved data at the moment, medical conciliation should examine the likelihood of incorporating X-rays into illness diagnosis based on the data, while all research-based X-ray is carried out. It is possible to approach the problem from various angle. © 2022 IEEE.
ABSTRACT
Immunisation is the procedure of providing a vaccine to a person in order to protect him or her against disease. This process has been widely recognized and adopted as one of the world's most successful and cost-effective health interventions. Vaccines have been saving millions of lives worldwide from deadly infectious diseases and viruses, such as hepatitis, measles and polio. However, the COVID outbreak in the late 2019 has witnessed huge devastation on the global health front. For now, vaccine is the only cost-effective health intervention to control the spread of virus and completely eradicating it. Technological breakthroughs are making a significant contribution to the improvement of healthcare. Blockchain technology is one example of such a disruptive technology. Blockchains have the potential to improve the healthcare system in a variety of ways. In this paper, we have thoroughly discussed how we can create vaccine awareness across the globe by focusing on parents, healthcare workers, frontline workers, policymakers, media, and ultimately how everyone must work together to ensure that every individual in every country gets the vaccine. We also discussed how blockchain technology may be applied to many sectors of healthcare and the benefits it can provide in terms of enhancing global network healthcare systems. © 2022 IEEE.
ABSTRACT
Virtual Innovation & Research Acceleration Lab (VIRAL) is an ongoing teaching pedagogy project between SRM Institute of Science & Technology, Ramapuram Campus, Chennai and MIT Square, London. In the higher education sector, the year 2020 posed new obstacles. All three waves of the COVID-19 pandemic necessitated a shift in the mentality of educators, teachers, and students from all over the world. The concerns and obstacles associated with higher educational institutions' rapid and abrupt transition from face-to-face learning to technology-assisted virtual learning. This study examines the effectiveness of Virtual Innovation & Research Acceleration Labs (VIRAL) teaching in developing Higher Order Thinking Skills (HOTS) among engineering students. The researchers employed a Quasi-Experimental method. The sample consisted of 98 Engineering students who have regular laboratory classes as part of their curriculum. A group consisting of 49 students was selected as a control group and another group was selected as an experimental group randomly. The result of the study indicates that the experimental group has a greater mean of higher-order thinking skills than the control group. Thus it is concluded that the virtual lab teaching method for research and innovation has a significant positive effect in enhancing higher thinking skills in engineering. The study also has implications for facilitation in the higher order of inquiry processes and improvement of slow learners.