ABSTRACT
Big data analytics is used to predict and analyze the data which is available in huge amount and having structured, unstructured and sometime semi structured values. Here in our research data analysis will be done on the behalf of data available which is nifty-50 stock market data. We are going to analyze the impact on nifty-50 due to covid-19. We have collected the dataset form Kaggle.com. The techniques used here is apache spark and language is Scala. In our research results will be shown on the basis of analysis done using the closing price and opening price of different stocks in different months and weeks. The results will be expressed in the form of graph using data visualization technique in tableau. © 2022 American Institute of Physics Inc.. All rights reserved.
ABSTRACT
Background, Objective: We studied the effectiveness and safety of Hydroxychloroquine (HCQ) preexposure prophylaxis against COVID-19 in Healthcare workers (HCWs) previous studies being inconclusive due to small sample and lack of risk stratification Design and setting: Prospective, observational, multicenter cohort study in 44 hospitals in 17 Indian states during May-Sept 2020 Participants: 12089 Consenting Doctors, nurses, ancillary staff likely exposed to COVID-19 patients irrespective of whether taking HCQ preexposure prophylaxis (4257) or not(7826) participated,(in 6 data missing) Measurements: Data was collected on a self administered online questionnaire. Statistical analysis was done on SPSS version 20. Results: Age above 45 years, diabetes, hypertension, history of COVID contact were independent risk factors for COVID positivity. HCQ intake did not show an independent association. However, when adjusted for other risk factors, HCQ dose as per Government recommendations, 2-3, 4-5 and 6 or more weeks reduced the probability of COVID positivity by 34%, 48%, 72% respectively. COVID free median survival time was higher in non-diabetics, non-hypertensives, persons below 45 years, with no prior exposure to COVID case and those who took HCQ for more than 6 weeks With modeling extent of risk reduction under different scenarios of risk and HCQ intake was 1-65% . Major adverse events reported were GI disorder, palpitation, giddiness and 140 persons discontinued due to adverse events. Limitations: Limitation of self reporting by HCWs in online form, minimized by specified options,mandatory fields and telephonic verification Conclusion: The study examined individual risk factors including site variations and found that HCQ 800 mg loading followed by 400 mg weekly, dose for more than 2 weeks, reduced the risk of COVID-19, in HCWs, and is a useful option in low resource settings till vaccines are made accessible to all. © 2021 Journal of Association of Physicians of India. All rights reserved.
ABSTRACT
Epidemic diseases are the contagious or infectious diseases which are possible to be spread into the entire country, and are defined as an outbreak that occurs and affects an exceptionally high proportion of the population. However, these infectious ailments if controlled beforehand by using trending technologies for the early prediction would not turn into mortality situations. With this view, this paper is summarising the research work by using machine learning and big data handling techniques for the early prediction of epidemic diseases. The epidemic diseases especially covered in this review are influenza, malaria and dengue ailment. The diseases are compared against machine learning models used and input data contemplated. An observation for the prediction of diseases found that same factors associated with searching techniques give different results for different locations;overall searches are showing diversity and dearth in data. Moreover, dearth of data will mitigate the accuracy. Copyright © 2021 Inderscience Enterprises Ltd.
ABSTRACT
The COVID-19 epidemic has presented many treatment challenges to physicians due to the lack of specific effective medicines. The COVID-19 pandemic is also witnessing irrational antibiotic use. The aggressive use of several antibiotics for treatment of COVID-19, and its complications may lead to another pandemic of Antimicrobial Resistance (AMR). Limiting unnecessary antibiotic use in viral infections, like COVID-19, should be emphasized in antimicrobial stewardship programs.
ABSTRACT
Big data analytics is becoming tremendously popular in every field today. Everyday lots of data are being generated and analyzed using big data analytics tools and technique. Here the technology used is apache spark and language used is Scala. So, in this paper study is being done on the behalf of research done in stock market data using apache spark technique. Here the nifty-50 data is taken to analyze the impact due to covid-19. As it is being seen that Covid-19 has affected almost everything around the globe, so the purpose is to analyze its effect on stock market. Thereafter comparison is done between the techniques used to analyze that massive volume of stock exchange data. Here the comparative analysis between Hadoop maps-reduce and apache spark on the behalf of some important parameter is being done. That concludes which technique is better for the analysis of the stock exchange data. © 2020 IEEE.
ABSTRACT
A In the medical field, the Image dispensation techniques are extensively employed for image amelioration in finding and treatment of lung disease in the big data environment, where the point in time feature is very paramount to determine the idiosyncrasy issues in intention images, particularly in lung disease such as cancer, pneumonia, COVID-19 etc, for early detection and treatment stages of lungs disease, Image processing technique are widely used for identification of genetic as well as environmental factors are very important in developing a novel method of lung disease prevention. The core factors of this research are quality, time, and precision of the dataset. The modification and evaluation of image quality depend on the segmentation techniques, an improved area of the object that is utilized as a rudimentary substructure of feature extraction is obtained and comparison is made on relying feature. Medical images are analyzed by different segmentation techniques of image processing. The segmentation techniques are used dataset to find patterns and retrieve information from the dataset for processing. The goal of this study is discussed various image processing techniques and big data analytics tools for lung disease has been given in the tabular form and provides comparative study. This study provides minutiae of big data analytics tools and image processing techniques, specifically discussed in the context of lung disease images. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
COVID19 is a contagious ailment, which is emanated from the city of Wuhan (Hebei district), China in late of 2019, genesis from newly come across corona virus. Humans are contaminated with the COVID-19 virus and experiencing the clement to passable respiratory ailment, recuperated without necessary special treatment. In this study, a systematic review of prediction of COVID-19 using machine learning and big data is accomplished, by considering all the related imperative features, excluding CT scan and X-Ray Images data sets, with all the available related articles around the globe. The summary concluded that the prediction patterns for some algorithms are not satisfactory, the predicted value and actual values are inversed, and meanwhile some algorithms resulted with good accuracy and lower errors. No study is done to target Indian data set including population index, as India is a densely populated country holding 17.7% of world’s population. With respect to this, our study included the implementation of two classification algorithms for the Indian data of COVID-19 cases from 30th January 2020 to 30th May 2020 including population index state wise. Furthermore, the results are commensurately equal with both the implemented algorithms. Bayes point machine algorithm and logistic regression algorithm are cross validated with 10 cross folds over the data and the maximum accuracy achieved is 99.6% and 99.4% respectively. At the same time, 7th cross fold of Bayes point machine manifested the worst accuracy with lowest precision, recall and f-score while logistic regression seems to be clement with all the 10 cross folds. Prediction of future diegesis for COVID-19 can serve the medical decision making especially when it is evoking attention immediately. © 2021 Taru Publications.