A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID19: INTELLIGENT CHATBOT AND FINANCIAL AID APPLICATION

Covid19 has devastated all continents causing disasters not only on the health sector but also at social, economic, and at political levels. The world is still trying to eradicate the virus. One of the measures taken is to inform citizens about the virus in order to avoid contamination as much as possible. Several people lost their jobs, and found themselves without any income. The whole world is confined, and the poor can no longer endure this critical situation. Financial assistance is therefore necessary in order to reduce the impact. This paper aims to propose an intelligent financial support application that computes the eligibility for a citizen to get a support during the pandemic;and to explain steps for chatbot using DialogFlow. The training realized using a machine learning algorithm was chosen after making a comparison between some other algorithms. Gradient Boosting Classifier algorithm was the accurate and most efficient for the application. It is possible to train the system again using other data set to make any adaptive results or computations. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.

Indonesia's Economic Recovery Post Covid-19 Pandemic Sentiment Analysis

Since the Covid-19 pandemic was first confirmed on March 2, 2020, Indonesia has faced many crises, one of which is the economic crisis. Many companies lose profits and impose layoffs for their workers. The Indonesian government in its efforts to restore the economy in Indonesia carried out several maneuvers such as eliminating the PCR/SWAB requirement for public transportation users and increasing tourism enthusiasm in Indonesia by organizing the Mandalika MotoGP. However, this is considered insufficient by some groups of people because the prices of primary needs continue to increase. This study aims to find out public sentiment towards the government for efforts to restore the economy in Indonesia. The results of this study indicate that the Indonesian government is considered successful and has taken the right steps in efforts to recover the economy in Indonesia. This is evidenced by the high percentage margin between positive and negative sentiments of 37%. © 2022 IEEE.

Sentiment Analysis of Covid-19 on Indonesian Twitter by Implementing the Naïve Bayes Method

Twitter is one of the social media used in Indonesia to express opinions/opinions. One of them is the opinion about Covid-19 which is taking the world by storm. The government's provisions regarding Covid-19 itself reap many pros and cons on social media, one of which is Twitter. In this study, 'Covid-19' will be used as a keyword to conduct sentiment analysis. Sentiment analysis is the process of understanding, extracting and processing textual data automatically to obtain information contained in an opinion sentence. The Naive Bayes Classifier method is used to classify and calculate the total accuracy of the class that has been obtained. Based on the results from the kaggle dataset, there are a total of 2269 tweet documents with the keyword 'Covid-19' on March 23-May 14, 2020 which can be trusted because the data has been labeled by experts. The Naive Bayes Classifier method has 2269 data sets, then divides it into 1815 training data, and 453 data testing data and produces an accuracy of 0.674. © 2022 IEEE.

Sentiment Analysis on the Perception and Mindset of the People of Indonesia on the Use of Vaccines to Deal with the Covid-19 Pandemic using the Text Mining Method

Right now, the world is busy with the COVID-19 pandemic. Coronavirus disease (COVID-19) itself is an infectious caused by a new variant of the newly discovered coronavirus. One way to deal with the virus is to get vaccinated against COVID-19. The government through the Indonesian Ministry of Health is also promoting the procurement of this COVID-19 vaccine by bringing various types of this COVID-19 vaccine. This research was conducted to know the sentiment and perception of the Indonesian people about the COVID-19 vaccination program. To find out, this research uses the Text Mining technique using Twitter as a data source. Data processing and analysis in this research used the Naive Bayes Classifier method using Python software. The results of this study show that the sentiment and perception of the Indonesian people to vaccination against COVID-19 is positive, as evidenced by the Confession Matrix value leaning towards True Positive. © 2022 IEEE.

HCMUS MediaEval 2021: Multi-Model Decision Method Applied on Data Augmentation for COVID-19 Conspiracy Theories Classification

Corona Virus and Conspiracies Multimedia Analysis Task is the task in MediaEval 2021 Challenge that concentrates on conspiracy theories that assume some kind of nefarious actions related to COVID-19. Our HCMUS team performs different approaches based on multiple pretrained models and many techniques to deal with 2 subtasks. Based on our experiments, we submit 5 runs for subtask 1 and 1 run for subtask 2. Run 1 and 2 both introduces BERT[5] pretrained model but the difference between them is that we add a sentimental analysis to extract semantic feature before training in the first run. In run 3 and 4, we propose a naive bayes classifier[4] and a LSTM[8] model to diversify our methods. Run 5 ultilize an ensemble of machine learning and deep learning models - multimodal approach for text-based analysis[3]. Finally, in the only run in subtask 2, we conduct a simple naive bayes algorithm to classify those theories. In the final result, our method achieves 0.5987 in task 1, 0.3136 in task 2. Copyright 2021 for this paper by its authors.

Analysis of Google Play Store's Sentiment Review on Indonesia's P2P Fintech Platform

Nowadays there are so many mobile phone-based investment applications, ranging from mutual funds, stocks, and P2P lending. While these investment applications are gaining huge attraction among the general masses, sometimes selecting the right platform still becomes a hot issue. This research aimed to analyze the sentiment on P2P lending applications and to determine the user's response due to the increase in the number of funds distribution during the COVID-19 pandemic. By doing so, this research could give some insight into the new and existing user. Data was obtained through assessment reviews on the Play store platform for the P2P A, P2P B, and P2P C applications. Assessment reviews were classified by using a data mining approach, TF-IDF feature extraction, and Naïve-Bayes classification method. This research showed that P2P A got 77% positive sentiment and 23% negative sentiment, P2P B got 36% positive sentiment and 64% negative sentiment, and P2P C got 68% positive sentiment and 32% negative sentiment. From the results of the study, it was found that P2P A got better results than both P2P B and P2P C. those were 77% positive sentiment with 23% negative sentiment in finance topic, 56% positive sentiment with 44 % negative sentiment in account verification topic, 79% positive sentiment with 21% negative sentiment in apps review, and 99% positive sentiment with 1% negative sentiment in referral topic. © 2022 IEEE.

A Comparison of Support Vector Machine and Naïve Bayes Classifier in Binary Sentiment Reviews for PeduliLindungi Application

COVID-19 statistics in Indonesia show more than 4.2 million active confirmed cases with more than 140 thousand deaths. The Indonesian government has made several policies to reduce the number of COVID-19 cases, one of them is by implementing the PeduliLindungi application. The government has socialized and recommended this application as an effort to fulfill the tracking, tracing, and fencing program. Various kinds of responses appear in the community to this application, therefore sentiment analysis is needed to find out public trends so that the government can evaluate the policies that have been made. This study aims to determine the best model from the comparison of the Naïve Bayes algorithm and the Support Vector Machine, besides that this study will also see whether a simpler model such as Naive Bayes is still good in handling binary sentiment for PeduliLindungi data reviews. The data was obtained by web scraping from the PeduliLindungi application review on the Google Play Store. The Naïve Bayes accuracy value is 81%, smaller than the Support Vector Machine which has an accuracy of 84%, although the Support Vector Machine is the best model we have, Naive Bayes itself can still be used to handle binary sentiment data because the difference in accuracy values is not too far. © 2021 IEEE.

Comparison of Adolescent Vaccination Data Accuracy by Urban Village in DKI Jakarta Province in July 2021 Using Several Data Mining Methods

Since 2020, the outbreak of the Coronavirus disease has begun to enter the territory of Indonesia. For a year and a half, various efforts have been made to reduce the number of deaths caused by this pandemic. One of the efforts made by the government is the provision of vaccinations for the community, especially for adolescents. This is one way to attract people's interest to vaccinate and also make it easier for the government and the system to process vaccination data, especially for youth vaccination. The purpose of this study is to determine the accuracy of the data on adolescents who have been vaccinated in the DKI Jakarta province in July 2021 by using several methods of data mining. Of the three data mining methods used in this study, the JRip method produces the highest percentage of accuracy, which is 100%. © 2021 IEEE.

FAKE NEWS PREDICTION ON COVID DATASET USING MACHINE LEARNING

Fake news is false information, nowadays these are big challenges in all types of media, especially social media. In this covid-19 pandemic situation, people are facing more problems and struggling every day. One among those problems, is fake news or false information about covid. To tackle this, we have made an attempt and created a dataset with 4200 records from social media. We analyze the outbreak of covid information and visualize them using charts and graphs and predict the fake news using three classifier machine learning models. They are passive aggressive classifiers, Naïve Bayes classifiers and Support Vector Machines. © 2021 IEEE.

Performance Analysis of Logistic Regression, KNN, SVM, Naïve Bayes Classifier for Healthcare Application During COVID-19

Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36 s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address these challenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Indonesian Twitter Sentiment Analysis Application on the Covid l9 Vaccine Using Naive Bayes Classifier

It's even one year since the COVID-19 pandemic hit Indonesia, to anticipate it, the government brought in a COVID-19 vaccine. Various types of COVID-19 vaccine have been introduced to Indonesia, including which ones will be considered the best according to the community through the Twitter platform. One of the venues that creates the most public sentiment is Twitter. It can be determined whether the public fully approves or rejects the existence of vaccination in Indonesia by analyzing public sentiment surrounding the COVID-19 vaccine. Data acquisition using a crawling procedure by connecting the Twitter API, pre-processing, sentiment categorization, and sentiment analysis outcomes are the stages of the sentiment analysis process to become a sentiment analysis application. The PHP and MySQL programming languages are used to create the database for the sentiment analysis application. After the application has been fully implemented, it can do sentiment analysis from each dictionary probability using the Naive Bayes Classifier approach. The study of the two keywords "vaksin covid"and "vaksin corona"yielded the following results. It has 93% positive sentiment results, 72% negative sentiment results, and 35% neutral sentiment outcomes, with an accuracy of 94.74% and 75.47% per keyword. Meanwhile, the Sinopharm vaccine, which has the most positive attitude with the terms "vaksin sinovac,""vaksin astrazeneca,""vaksin sinopharm,"and "vaksin nusantara,"has 84 percent tweets with a 74.23% accuracy rate. © 2021 IEEE.

Malay Tweets: Discovering Mental Health Situation during COVID-19 Pandemic in Malaysia

During the unprecedented of COVID-19 pandemic, numbers of research had been conducted on mental health in social media worldwide. Past research has shown interest in Twitter sentiment analysis by using keywords, geographical area, and range of ages. Up to the authors' analysis, there is no research conducted on mental health using keyword in the case of Malaysia. A Malay Tweet dataset was built for analysing mental health tweets during the first Movement Control Order period using unique keywords. Machine learning algorithms namely, Naïve Bayes classifier and Support Vector Machine were used to predict the sentiment of tweets. The classifiers were evaluated using 10-fold cross-validation, accuracy, precision, and F1-score. The data then visualized in charts and WordCloud. The results shows that Support Vector Machine performed better than Naïve Bayes classifier for both test set and 10-fold cross-validation in terms of performances in n-gram TF-IDF. The visualized data could provide insights to the authority pertaining the mental health issues, in which it relates to local news and situations during the periods. © 2021 IEEE.