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3rd Doctoral Symposium on Computational Intelligence, DoSCI 2022 ; 479:37-55, 2023.
Article in English | Scopus | ID: covidwho-2148650


The COVID-19 pandemic has effectively shut down the whole planet. Most countries have now suspended lockdowns or semi-lockdowns, although lockdowns still exist in many countries. The coronavirus epidemic has disrupted people's daily lives. People from all across the globe have flocked to social media to voice their thoughts and feelings on the phenomenon that has gone viral. In a very short period of time, the social networking site Twitter saw an extraordinary rise in tweets pertaining to the novel coronavirus. With the discovery of several vaccines for the virus, the new year of 2021 brought with it new hope. A global vaccine campaign is under way, and we anticipate that the world will quickly recover from this pandemic and return to normalcy. This paper is devoted to the vaccination drive's tweets. This is used to predict the attitude of tweets on vaccinations. We have taken note of how sentiment changes over time, with respect to vaccination, through the general people who tweeted. For analysis, VADER and LSTM, Z-score, have been used. Additionally, with vaccine data visualization, the most common positive and negative, all hashtags, and the source of the data have been analyzed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Indian Journal of Clinical and Experimental Ophthalmology ; 8(1):51-54, 2022.
Article in English | Scopus | ID: covidwho-2145766


Background: Due to the surge of post COVID mucormycosis in India there has been a significant patient load seen in hospitals we have observed that mucor is not the only culprit and there has been other fungi like aspergillus and candida, who have led to increased morbidity and mortality. Aim: To conduct a retrospective analysis on laboratory reports of specimens sent after surgical intervention of patients admitted with mucormycosis and to identify the weightage of different fungal infections in the post COVID era. Materials and Methods: It is a hospital based retrospective review of mycology and histopathology reports of post COVID rhino orbital mucormycosis patients referred from ophthalmology, ear nose throat surgery, oral maxillofacial surgery, neurosurgery department of mahatma gandhi memorial medical college, Indore, Madhya Pradesh from 1 June to 7 July 2021. Result: Out of 240 samples sent for histopathology examination, 1.6% samples showed mucormycosis with secondary aspergillosis while 98.33% samples showed primarily mucormycosis likewise 270 KOH mount reported around 8.51% mucormycosis with secondary aspergillosis, 4.81% reported primary aspergillosis, 72.15% reported primarily mucormycoses. Conclusion: We acknowledge that aspergillus and candida has contributed significantly in post covid mycoses and that mucor is not the only culprit. © 2022 Innovative Publication, All rights reserved.

2021 International Conference on Advancement in Computation and Computer Technologies, ICACCT 2021 ; 2555, 2022.
Article in English | Scopus | ID: covidwho-2133892


It is crucial that Breast Cancer should be detected early. Breast cancer time series forecasting is a novel data - driven approach to breast cancer diagnosis. Instead of looking at static images of the medical records, it analyses the dynamics in the tumour's growth rate, especially in its early stages. It uses machine learning models to find patterns that are not readily observable in static images, but are predictive of later outcomes. During COVID-19 it is necessary to monitor patient from home and IOT devices can be used that give moment forecast to the client and doctor during their typical day by day routine. Various Machine learning models are reviewed for classification of Breast Cancer symptoms. It is observed that data visualization and feature engineering play a crucial role in the classification before applying any model on data set. For human protection during COVID-19 it is better to depend on IoT enabled wearable device for automatic detection and appointment. The IoT enabled devices can use power of cloud computing and machine learning models to complete the framework of getting treated at home. Security of the data is another aspect to be taken into consideration. Solutions are available for the whole process and their aggregation will result in generating the desired model. In this paper, model is proposed to diagnose breast cancer at home using IoT, Blockchain, Machine learning and Cloud Computing. © 2022 American Institute of Physics Inc.. All rights reserved.