CNN-Based Deep Features with Ensemble Learning for COVID-19 Classification

In this paper, we propose a hybrid system that can automatically detect coronavirus disease and speed up medical image analysis processes by using artificial intelligence technique. Our system consists of two parts: First, to perform feature extraction, we used a deep convolutional network that is based on the transfer learning technique, in this step, we include eight well-known convolutional neural networks for comparison purposes. In the second part, a voting classifier is considered, combining three classifiers, including random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN), to classify radiological images into three classes: COVID-19, normal, and pneumonia, collected from two public medical repositories. The results show that deep learning and radiological images are able to retrieve relevant COVID-19 features with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine

Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community's interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated. © Published under licence by IOP Publishing Ltd.

Flipbook in Learning: Bibliometric Analysis using Scopus Data and Google Scholar

This study aims to map articles that examine flipbooks as the variables. Scopus and Google Scholar are used to gather the data for this study. The selected articles are those published in international journals and proceedings indexed by Scopus and/or Google Scholar. The data collection applied is a documentation technique. The procedure for data documentation begins with Scopus data containing the article keyword "flipbook in learning". The data from Google Scholar is then cataloged in the Mendeley software. Furthermore, each data is mapped using the VOSviewer software. The data is limited according to the needs of the discussion of the results of the study, namely articles that discuss flipbooks in learning. The data analysis process uses a bibliometric approach. The results show that research on flipbooks in learning published in international journals and proceedings is increasingly in demand, particularly in 2020 and 2021. However, in terms of quantity, flipbook articles still need to be improved. This is following the need for teaching materials, especially teaching materials that can be used during the distance learning system during the COVID-19 pandemic. © 2021 IEEE.

Detecting COVID-19 Implication on Education and Economic in Arab World Using Sentiment Analysis Techniques of Twitter Data

Coronavirus (COVID-19) is a contagious and dangerous infection that initially surfaced in Wuhan, China, in December of 2019 and has infected millions around the world. The rapid transmission of this disease leads to all governments' taking a lockdown decision. This decision has a negative effect on the two main sectors of any country: education and the economy. This study explores public opinion about the impact of COVID-19 on these two sectors by collecting Arabic tweets from Twitter from the MENA region. Then, we applied NLP and sentiment analysis process to the dataset for each sector after applying the pre-processing steps. We built state-of-the-art machine learning models to use them in the prediction process that are Random Forest (RF), Logistic Regression (LR), Na¨ive Bayes (NB), and Voting classifier (VC). The results showed that the NB and VC gave the best performance results in two datasets. The results showed that these models produced outcomes that were useful to many decision-makers in the government in taking important and suitable decisions related to daily life in the Arab World. © 2022 IEEE.

Long-term Analysis and Comparison Electricity Consumption of Retrofitted Lighting Systems

This paper examines and evaluates the long-term electrical energy usage of retrofitting lighting systems with varying degrees of control intelligence. One of the most important components of confirming the appropriateness of the application in practice is the long-term energy measurement of these systems. The data analysis and measurement process are described in this article. An intelligent measurement system was used to collect data on electricity consumption. On the basis of these data, an analysis and comparison of various systems were conducted, and the overall electricity consumption for a certain time period was calculated. Finally, a scenario was created to demonstrate the impact of the COVID-19 pandemic on a retrofitted lighting system with various levels of intelligence. © 2022 IEEE.

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.