Comparative study of scientific articles on MMR vaccines published from India over the last few decades

The MMR vaccine as we know is a vital vaccine to protect against three disease-causing microbes- measles, mumps, and rubella. To commemorate 75 years of Indian independence, the present study delves into the achievement of Indian research and lists out articles retrieved from the Web of Science Core Collection database on the domain of MMR vaccine research. The data has been restricted to the publication from India, thereby, has throwing some understanding into the MRR vaccine research in India over the last 28 years- 1994 to 2021. The data have been compared based on scientometric analysis. Qualitative and quantitative analysis have also been taken into account in order to give a comparative insight into the research. The comparison was done based on citation data, usage count data, year of publication, journals, publication media, domains focussed on the papers, and type of document. Astonishingly, in 2021, the most number of papers were published, most of them have related MMR vaccine as a potential immunity developer against COVID-19 infection. A total of 43 articles were retrieved from the search, the numbers are quite big, and the highest citation among them being 99 which was published in 2014, which is quite impressive for such a short duration of time. The comparative study suggests a positive growth of MMR vaccine research in India.

Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector

During this Covid-19, face masks are used to avoid cross-contamination as part of an infection protection strategy. Wearing a face mask can help avoid infection by preventing individuals from coming into contact with pathogens. When someone coughs, speaks, or sneezes, there is a chance that the infection will spread into the air and affect those nearby. So to prevent the rate of spreading, face masks are highly mandatory. Tracking every individual manually is an expensive task;therefore, we save a lot of time, cost and effort by automating this process. This proposed automation can be done using Artificial Neural Networks. YOLOv3 and MobileNetv2 are popular architectures used in different object detection applications. Hence, paper compares the above architectures by their performance, accuracy from outputs of both under different scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning Technique for COVID 19 Prediction using CT Scan Images

Corona Disease Virus (COVID-19) is a rapidly spreading contagious viral disease that causes respiratory contaminations and is currently generating a worldwide medical crisis. It has caused a massive influence on people's lives, general well-being, and the global economy. Henceforth, it is critical to straightaway analyze the positive cases in order to keep the illness from spreading further and to regard infected patients as fast as could really be expected. Both patients and specialists will be benefitted by the early recognizable capability of outrageous COVID-19 by utilizing chest CT to examine biomedical images. RT-PCR (switch record polymerase chain response) based tests help to identify COVID-19, which has numerous limits. In this work, different CNN based Classifier model methodologies are utilized to follow the presence of COVID-19 from chest CT filter images of patients. In true indicative situations, a profound CNN-based methodology could be amazingly valuable in accomplishing quick COVID-19 testing. By utilizing irregularity data obtained from sifted images, image expansion enhances the number of profitable models for creating the CNN model. The proposed model has a grouping exactness of 95% for CT examines utilizing this strategy. With picture expansion, CT check pictures have an affectability of 94.78%and a particularity of 95.98%. The trial results were contrasted with ResNet-18, ResNet-50, and VGG-16 models, with freely available datasets containing CT images. © 2021 IEEE.