District Level Covid Case Prediction using LSTM Models

Covid-19 has had an adverse effect on the world, with more than 440 million cases recorded so far. The outbreak has hampered the country's healthcare and economy. This calls for an accurate prediction model for the prediction of Covid Cases, so that it gives some time to the hospitals and administration, to make the necessary arrangement. For population-dense countries like India, the covid case dynamics of every district is different, hence this requires a district-wise case prediction of Covid Cases. In this paper, we perform prediction of covid cases across all districts of India using different architectures of Long short-term memory (LSTM) and performed a comparative analysis between them. To the best of our knowledge, this is the first such attempt at the district level. Bidirectional LSTM encoder-decoder outperformed other LSTM-based models and, gave a test set MAPE of 15.44, followed by LSTM Encoder Decoder, giving a MAPE of 19.72. © 2022 IEEE.

COVID-19 Cases Prediction Based on LSTM and SIR Model Using Social Media

The novel coronavirus (COVID-19) epidemic, which broke out in Wuhan, was spread worldwide along with India. Nowadays, social media platforms are one of the primary sources of conveying information. This paper presents a susceptible-infected-removed (SIR)-based model to simulate the COVID-19 epidemic. We also investigate the impacts of prevention and control measures and preventive awareness using COVID-19 data from social media platforms such as Twitter (TW), Reddit (RD), and Google News (GN). The infection rates are then updated using long short-term memory (LSTM), and a modified SIR epidemic spreading model is proposed. The proposed model reflects the effectiveness of information disseminated through multiple social media platforms for predicting infection cases, and furthermore, compared to other standard epidemiological models, integrating language processing elements from online textual data significantly reduces the prediction errors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

Analysing Cyberbullying Using Natural Language Processing by Understanding Jargon in Social Media

Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, and cyberbullying amongst vulnerable groups is only growing over increased access to social platforms, especially post COVID-19. It is paramount to detect and ensure safety across social platforms so that any violence or hate-crime is automatically detected and strict action is taken against it. In our work, we explore binary classification by using a combination of datasets from various social media platforms that cover a wide range of cyberbullying such as sexism, racism, abusive, and hate-speech. We experiment through multiple models such as Bi-LSTM, GloVe, state-of-the-art models like BERT, and apply a unique preprocessing technique by introducing a slang-abusive corpus, achieving a higher precision in comparison to models without slang preprocessing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploring Bioinspired Feature Engineering Technique for Online Hate Speech Detection

The spreading of hate speech and toxicity on social media and other online platforms has increased severely in the past decade. In the current scenario also, when the whole world is suffering with outspread of COVID-19 online hate speech spreading more than before. The spread of such hate can jeopardize the mental and physical health of many people and is thus necessary to stop its spread on online social media. This paper aims to explore bioinspired algorithms like PSO and GA to detect online hate speech on social media and other online platforms. We explore the hybrid feature selection approach to select valuable and meaningful features from the hate speech dataset to classify between hate and not hate posts efficiently. Our experiments indicate the random behavior of Particle Swarm Optimization and Genetic Algorithm and the decrease in accuracy when applied individually to the experiments. The proposed hybrid approach gives the comparative results as TF-IDF when applied with the baseline machine learning models. © 2022 IEEE.

Human Mobility based Pandemic Prediction Model

In December 2019, the coronavirus disease 2019 (COVID-19) outbreak was first reported in Wuhan, China, and later has expanded all over India and across the world. The dynamics of pandemic spatial spread have gotten a lot of interest among researchers. Human mobility on a huge scale has increased the transmission of the epidemic. This paper offers a prediction model for coronavirus cases based on epidemic data and population mobility data. The study integrates the human mobility data with the historical cases to predict future COVID-19 cases. The findings indicate that population mobility may adequately explain the spread of coronavirus. The effectiveness of our proposed model is demonstrated by the coefficient of determination-R2 with the prediction value of 0.962. The proposed model can be used as a resource for epidemic prevention and control decision support. © 2021 IEEE.

Impact of Meteorological Parameters on COVID-19 Outbreak Using Machine Learning Techniques

The world has been stand-still by COVID-19, a respiratory syndrome. Its variable nature of outspread motivated us to study the impact of weather parameters. We emphasized our study on Delhi region. Applied the Pearson's correlation test and obtained the relation between weather conditions and COVID-19 cases then analyzed it for 5 incubation periods. We determined spread, growth, recovery, and transmission rates using popular regression-based machine learning (ML) techniques. The correlation resulted in achieving the best incubation period of 12 days in the city. We found that wind speed and precipitation levels were insignificant, whereas the notable association between Minimum temperature (Min. Temp) and Infra-Red (IR) radiative flux was determined. On stacking the regression-based models, we achieved the best accuracy for predicting the outcome of the pandemic. Our results proposed that certain prime environmental conditions are favourable for the growth of the virus and increase the chances of getting infected. The brief study is beneficial for concerned authorities to adopt adequate measures to flatten the curve. © 2021 IEEE.

A Study on Pulmonary Image Screening for the Detection of COVID-19 Using Convolutional Neural Networks

The coronavirus, also known as COVID-19, has now spread to almost all parts of the world causing widespread illness and deaths. Not only control but also testing individuals for this virus has become a challenge in a variety of sections of the society. The rising number of cases, and the shortage of testing kits for the virus has motivated us to explore other methods of testing for the virus. This study has tried to explore various research papers and studies, to come up with an efficient method for the detection of the virus, using datasets based on the chest X-ray and computed tomography (CT) scans of individuals. This study has found that visual inspection of the CT scan and X-rays of the chest of the individuals has led to a time-consuming process, but using convolutional neural network models, there exists a strong possibility of coming up with a data-driven deep learning model, which would act as a classifier between infected and healthy individual. This study has showed that convolutional neural network-based models have a capability of providing an alternative to the current methods of testing for the virus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Parameter Based Literature Survey of COVID-19 Mortality Dynamics Using Machine Learning Techniques

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.

A Review of Various Mathematical and Deep Learning based Forecasting Methods for COVID-19 Pandemic

With over a hundred million cases worldwide and thousands coming daily, the outbreak of COVID-19 has seriously affected many countries' healthcare and economic situations. A precise and efficient model for predicting new COVID-19 cases and the pandemic's future dynamics can be highly beneficial in such distressing conditions. These predictions might help the hospitals and the concerned authorities to devise necessary and preliminary arrangements for the patients in advance. This will be able to positively prevent the second or third wave of the pandemic spread. In the following study, we have composed a brief analysis of the appropriate and recent tools used for forecasting COVID-19. In this study, we have categorized these forecasting techniques into two broad classes, viz. Mathematical modeling based and Deep Learning-based. These predictions prepare us against any future threat and consequence that may occur in the future. © 2021 IEEE.

Understanding the Applications of Natural Language Processing on COVID-19 Data

In the digital age, the Internet has enabled the circulation of ideas and information and, in turn, has increased awareness among people. However, this does not come with its drawbacks. With the proliferation of online platforms, hoaxers can easily lure people towards their propagandist views or false news. The need to root out such false information and hate speech during this COVID-19 pandemic has never been more essential. The following study presents a survey of various papers that attempt to tackle similar problem statements with fake news, sentiment classification, and topic extraction. The paper focuses on how existing quality research can help improve the current state of research on COVID-19 related datasets by guiding researchers towards valuable procedures to help governmental authorities combat the rise in the spread of false news and malicious and hate comments. © 2020 IEEE.