A Machine Learning Approach for COVID-19 Tweet Classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making.This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutral for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective post.We're going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot and symptom detection is also playing a important part to arrest the spread of disease. © 2022 Copyright for this paper by its authors.

Covid -19 Social Distance Analysis Using Machine Learning

According to the Ministry of Global Health, social distance is one of the most effective defenses against COVID-19 and helps to prevent its spread. Governments have imposed many safety orders on citizens and facilities to limit social distancing and slow the spread of the virus. As a result, there has been an increase in interest in technologies to research and control the spread of COVID-19 in various settings. This research aims to investigate the results of several machine learning approaches to find cases when the physical distance between people has been violated. The method first identifies the instance of the human in the video frame, tracks the movements, computes the distance with other humans on the same frame and thus estimates the number of people who violate the social distance. Compares the approach to performing the performance using Yolo, SSD and Faster R- CNN. Videos that are used in this approach are collected from the wild, considering different camera settings, indoor and outdoor scenes, and recorded from various angles. Comparing the three methods Yolo, SSD and Faster RNN, the results show Yolo has a better performance in detecting humans from the current videos and thus in determining the violation of the distance between humans. © 2023 IEEE.

A Machine Learning Approach to Predict Customer Churn of a Delivery Platform

The use of delivery platforms has become widespread due to the impact of the Covid-19 and the O2O industry. However, the ELEME delivery platform, a subsidiary of Alibaba Group, which represents China, has recently been losing market share. This means that companies need to constantly look at strategies to attract new customers and maintain existing ones. In general, it costs at least five times more to attract new customers than it does to manage existing customers. This paper attempts to predict customer churn using the ELEME customer dataset to develop strategies to identify and prevent churn in advance. The results of the analysis using machine learning approach found that the most influential feature that can predict churn is the number of clicks made by the user. This paper presents the process and explanation of applying various algorithms for predicting customer churn on a distribution platform. It also proposes strategies for dealing with customer churn. © 2023 IEEE.

Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting

The problem of identification of unknown epidemiological parameters (contagiosity, the initial number of infected individuals, probability of being tested) of an agent-based model of COVID-19 spread in Novosibirsk region is solved and analyzed. The first stage of modeling involves data analysis based on the machine learning approach that allows one to determine correlated datasets of performed PCR tests and number of daily diagnoses and detect some features (seasonality, stationarity, data correlation) to be used for COVID-19 spread modeling. At the second stage, the unknown model parameters that depend on the date of introducing of containment measures are calibrated with the usage of additional measurements such as the number of daily diagnosed and tested people using PCR, their daily mortality rate and other statistical information about the disease. The calibration is based on minimization of the misfit function for daily diagnosed data. The OPTUNA optimization framework with tree-structured Parzen estimator and covariance matrix adaptation evolution strategy is used to minimize the misfit function. Due to ill-posedness of identification problem, the identifiability analysis is carried out to construct the regularization algorithm. At the third stage, the identified parameters of COVID-19 for Novosibirsk region and different scenarios of COVID-19 spread are analyzed in relation to introduced quarantine measures. This kind of modeling can be used to select effective anti-pandemic programs. © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023.

Pandemic Outbreak Time: Evaluation of Public Tweet Opinion by Machine Learning

In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time. © 2022 IEEE.

Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

Survival Analysis and Its Application of Discharge Time Likelihood Prediction Using Clinical Data on COVID-19 Patients-Machine-Learning Approaches

COVID-19 has significant fatality rate since its appearance in December 2019 as a respiratory ailment that is extremely contagious. As the number of cases in reduction zones rises, highly health officials are control that authorized treatment centers may become overrun with corona virus patients. Artificial neural networks (ANNs) are machine coding that can be used to find complicate relationships between datasets. They enable the detection of category in complicated biological datasets that would be impossible to identify with traditional linear statistical analysis. To study the survival characteristics of patients, several computational techniques are used. Men and older age groups had greater mortality rates than women, according to this study. COVID-19 patients discharge times were predicted;also, utilizing various machine learning and statistical tools applied technically. In medical research, survival analysis is a regularly used technique for identifying relevant predictors of adverse outcomes and developing therapy guidelines for patients. Historically, demographic statistics have been used to predict outcomes in such patients. These projections, on the other hand, have little meaning for the individual patient. We present the training of neural networks to predict outcomes for individual patients at one institution, as well as their predictive performance using data from another institution in a different region. The research output show that the Gradient boosting longevity model beats the all other different models, also in this research study for predicting patient longevity. This study aims to assist health officials in making more informed decisions during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Machine Learning Approach to Predict the Personalized Next Payment Date of An Online Payment Platform

Use of digital payments has risen exponentially in the recent past especially due to the COVID-19 pandemic. This is because online payment methods offer many benefits in performing their day-to-day transactions and paying utility bills such as electricity bills, water bills, telephone bills and etc. Knowing when a consumer will perform a specific online transaction, or bill payment is beneficial t o a n o nline payment platform to plan marketing campaigns since targeted marketing has become very prevalent nowadays. However, predicting this is not an easy task since thousands of transactions are happening in each and every minute of an online payment platform. This paper presents the results of a study that investigated predicting the customer personalized, utility bill payment type wise next payment date of a financial c ompany i n S ri Lanka by using machine learning techniques. This is accomplished by analyzing not only online transaction history but also customer characteristics and a holiday calendar which is specific t o Sri Lanka. At the end of the study, it was identified t hat XGBoost Regressor is the most suitable machine learning algorithm, etc deal with this scenario which provided 91.02% accuracy. These predictions will be used for sending personalized reminders and discount offers to customers without sending general common notifications w hen t hey a re p lanning t o d o a n o nline payment. Such reminders and offers will be notified o n t he m obile devices of the customers and, ultimately both customers and the business owners will be benefited by this. © 2022 IEEE.

Smart doors - A Machine Learning Approach to monitor body temperature and detect face mask

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

The Data Leakage Sentiment Analysis Using Naive Bayes Algorithm Based on Machine Learning Approach

Data leakage is a case that often occurs anywhere. Indonesia is one of the countries with the most population that is currently having data leakage cases. The leak of data on the COVID-19 PeduliLindung tracking application, triggered a public reaction because it was considered dangerous. Based on this, the aim of the study is to predict the sentiment pattern using Naïve Bayes. This study is important to do sentiment analysis to find out the public's reaction, it can become a recommendation in developing applications that are safer in data storage. The experiment in this study used data from Twitter which was taken for 14 days, between 16-21 May 2022. The data was processed using Google Collab and the Naïve Bayes approach. The experimental results are that negative sentiment is greater than positive sentiment and neutral sentiment, which is 93%. While the accuracy of positive sentiment is 81% and Neutra sentiment is 90%. This means the leak of public data from a COVID-19 tracing application in Indonesia has a greater negative sentiment. The difference in the study is the data testing process was carried out five times to get good accuracy from the model. And the results show that Naïve Bayes is a model that is quite recommended for prediction of sentiment patterns. © 2022 IEEE.

To Mask or Not To Mask? A Machine Learning Approach to Covid News Coverage Attitude Prediction Based on Time Series and Text Content

In the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language processing (NLP) has been one of the most popular areas for data analysis and pattern recognition. A significantly large amount of data is obtained in text format due to the ease of access nowadays. Most modern techniques focus on exploring large sets of textual data to build forecasting models;they tend to ignore the importance of temporal information which is often the main ingredient to determine the performance of analysis, especially in the public policy view. The contribution of this paper is two-fold. First, a dataset called COVID-News is collected from three news agencies, which consists of article segments related to wearing masks during the COVID-19 pandemic. Second, we propose a long-short term memory (LSTM)-based learning model to predict the attitude of the articles from the three news agencies towards wearing a mask with both temporal and textural information. Experimental results on COVID-News dataset show the effectiveness of the proposed LSTM-based algorithm. © 2022 IEEE.

An Improved Machine Learning Approach to Detect Real Time Face Mask

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly. © 2023 IEEE.

Exploring the Impact of the Quality of Social Media Early Adopters on Vaccine Adoption

As social media such as Twitter has become an important medium for disseminating information, it is essential to understand how the information diffusion on social media influences public adoption of vaccines. Based on the innovation diffusion theory, we construct a user and information quality indicator system for early adopters of COVID-19 vaccination by identifying their creation of user-generated content on social media. Machine learning approaches and text analysis methods are used to perform topic clustering and sentiment analysis on vaccination-related tweets on Twitter. Based on each country's vaccination data in January 2021, the study examines the relationship between the quality of social media early adopters, and the quality of the information they publish with vaccine adoption by using the OSL regression model. The empirical results show that the total number of tests, the number of new COVID-19 cases, and the human development index have a significantly positive influence on vaccine adoption. Neutral emotions and offensive language of early adopters on social media have a significantly negative relationship with vaccine adoption. These interesting findings can help governments and public health officials understand early adopters' perceptions of vaccines and play an important role in targeted policy interventions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine Learning Approach: Consumer Buying Behavior Analysis

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

Prediction of Unemployment using Machine Learning Approach

Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people. © 2022 IEEE.

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

A Machine Learning Approach for Diagnosing and Identifying Symptoms of COVID-19

Coronavirus 2019 (COVID-19) is a pandemic that hit the world and was responsible for the death of millions and the life disruption of billions of people. One of the most critical challenges faced during the earlier breakthrough of the diseases was identifying symptoms confused with colds, flu, and other common infections. Nevertheless, despite all the effort and research conducted for this purpose, this challenge continues as more strains, variants, and mutations appear. This work presents a solution for this problem based on machine learning classification and variable importance algorithms. A public dataset of 274,957 cases has been classified into typical and COVID-19 cases based on the reported symptoms and other variables. The dataset was used for classifying the reported cases using K-nearest neighbor (KNN), Naïve Bayes, and Decision Trees (DT) algorithms and identifying the significant symptoms that were decisive in classifying the patients using Gini, Information Gain, and Information Gain Ratio algorithms. Naïve Bayes and Decision Trees performed best with a Classification Accuracy (CA) score of 95.2% and 96.3%, respectively. The Naïve Bayes classifier scored an Area Under the Curve (AUC) of 88.75%. In addition, the applied variable importance algorithms identified headache, fever, and sore throat as the most important symptoms. © 2022 IEEE.

Public Sentiment Analysis of Indonesian Tweets About COVID-19 Vaccination Using Different Machine Learning Approaches

More than two years after the start of the coronavirus disease (COVID-19) pandemic, the whole world continues to be impacted by this global crisis. Indonesians use the social media platform Twitter to share information and opinions about coronavirus disease (COVID-19) vaccination. This study was conducted to determine the views of Indonesians toward the government's COVID-19 vaccination program and to test the capability of several machine learning techniques to classify sentiments expressed on Twitter. The performance of four machine learning algorithms was tested: the Naïve Bayes, k-Nearest Neighbors (kNN), Decision Tree, and Support Vector Machine (SVM) algorithms. The findings show that the SVM algorithm exhibited the best performance in terms of accuracy (92%) compared to the Naïve Bayes, kNN, and Decision Tree algorithms. A grid search technique was also used to optimize performance based on parameter settings in the algorithm used. © 2022 IEEE.

COVIDGuardian: A Machine Learning approach for detecting the Three Cs

On January 30, 2020, WHO officially declared the outbreak of COVID-19 a Public Health Emergency of International Concern. Japan announced the state of emergency and implemented safety protocols the "Three Cs", a warning guideline addressing to voluntarily avoid potentially COVID-19 hazardous situations such as confined and closed spaces, crowded places and close-contact settings that lead to occurrence of serious clusters. The primary goal of this research is to identify the factors which help to estimate whether the user is in the Three Cs. We propose COVIDGuardian, a system that detects the Three Cs based on data such as CO2, temperature, humidity, and wireless packet log. The results show that estimation of closed space had the highest accuracy followed by close-contact settings and crowded places. The ensemble Random Forest (RF) classifier demonstrates the highest accuracy and F score in detecting closed spaces and crowded spaces. The findings indicated that integrated loudness value, average CO2, average humidity, probe request log, and average RSSI are of critical importance. In addition, when the probe request logs were filtered at three RSSI cutoff points (1m, 3m, and 5m), 1m cut-off points had the highest accuracy and F Score among the Three C models. © 2022 Copyright held by the owner/author(s).