scholarly journals Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh.

2021 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Argho Das ◽  
Suben Kumer Saha ◽  
Mahfujur Rahman ◽  
Khandaker Tabin Hasan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Optimal Result ◽  
Machine Learning Methods ◽  
The People ◽  
Different Types ◽  
Dataset Size ◽  
Gated Recurrent Units ◽  
Survey Responses ◽  
General Opinion

Abstract Objectives: The COVID-19 pandemic is among the most serious global threats, and it is still a significant concern. The people of Bangladesh are undergoing one of the world's largest vaccination drive. With the recent launch and introduction of the COVID-19 vaccines, many of us are curious about the general opinion or view of the vaccine. While the vaccine has ignited new hope in the battle against COVID-19, it has also sparked militant anti-vaccine campaigns, so the need to analyze public opinion on the COVID-19 vaccine has emerged. Methods: Traditional machine learning methods were used to obtain a benchmark result for the experiment. The recurrent neural network (RNN) algorithm was used next. Several different types of recurrent neural networks were used, including simple RNNs, Gated Recurrent Units (GRUs), and LSTMs. Finally, to achieve a more optimal result, small BERT models (Bidirectional Encoder Representations from Transformers) were used. Results: Upon study and testing on several models and methods, it can be seen that BERT model was the most accurate of the bunch, which was 84%. On the other hand, Naive Bayes was able to obtain an accuracy of 81%. Naive Bayes and BERT produced similar results in F1- Score, but the performance of Naive Bayes can improve as the dataset size grows. Conclusion: Knowing about public opinions on the COVID-19 vaccine is critical, and action must be taken to ensure that everybody understands the value of vaccination and that everybody receives the COVID-19 vaccine. Vaccination may help to develop immunity, which lowers the likelihood of contracting the disease and its consequences.

scholarly journals To Enhance Phishing Emails Classification using Machine Learning Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 2240-2242
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
False Negative ◽  
Naïve Bayes ◽  
True Positive ◽  
Warning Messages ◽  
Dataset Size ◽  
Small Dataset

Phishing email becomes more dangers problem in online bank truncation processing problem as well as social networking sites like Facebook, twitter, Instagram. Normally phishing is carrying out by mocking of email or text embedded in email body, which will provoke users to enter their credential. Training on phishing approach is not so much effective because users are not permanently remember their training tricks, warning messages.it is totally depend on the user action which will be performed on certain time on warning messages given by software while operating any URL. In this paper, phishing email classification is enhanced using J48, Naïve Bayes and decision tree on Spam base dataset. J48 does best classification on spam base which is 97%for true positive and 0.025% false negative. Random forest work best on small dataset that is up to 5000 and number of feature are 34.but increase dataset size and reduce feature Naïve Bayes work faster.

scholarly journals Counteracting Deceptive Responding in the Finnish Investigative Instrument of Child Sexual Abuse (FICSA)

2020 ◽  
Author(s):  
Alessandro Tadei ◽  
Juulia Haajanen ◽  
Johan Pensar ◽  
Pekka Santtila ◽  
Jan Antfolk
Keyword(s):  
Sexual Abuse ◽  
Child Sexual Abuse ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Background Information ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
False Allegations ◽  
The People ◽  
Gender Specific

Unfounded child sexual abuse (CSA) allegations take investigative resources from real cases and have detrimental consequences for the people involved. The Finnish Investigative Instrument of Child Sexual Abuse (FICSA) supports investigators by estimating the probability of a CSA allegation being true based on the child’s background information. In the current study, we aimed at making FICSA resistant to deception. Two gender-specific questionnaires with FICSA questions and additional “trap” questions were constructed. The trap questions seemed statistically related to CSA but were not. We combined the answers of 278 real victims and 275 16–year-old students, instructed to simulate being CSA victims, to build a Naïve Bayes classifier able to separate the two groups (AUC = 0.91 for boys and AUC = 0.92 for girls). By identifying false allegations early in the investigation, authorities’ resources can be directed towards allegations that are probably true, effectively helping actual CSA victims.

scholarly journals How are Curiosity and Interest Different? Naïve Bayes Classification of People's Naïve Belief

2020 ◽  
Author(s):  
Ed Donnellan ◽  
Sumeyye Aslan ◽  
Greta M. Fastrich ◽  
Kou Murayama
Keyword(s):  
Information Seeking ◽  
Naive Bayes ◽  
Empirical Work ◽  
Naïve Bayes ◽  
Free Text ◽  
Machine Learning Methods ◽  
Conceptual Clarity ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are naïve concepts, we sought to determine a naïve consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a naïve consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (dependent n = 196; independent n = 218). In Study 2, we demonstrate that the naïve consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information-seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information-seeking towards information in domains where people already have knowledge.

Analysis on Ensemble Methods for the Prediction of Cardiovascular Disease

2021 ◽  
Vol 11 (10) ◽  
pp. 2529-2537
Author(s):  
C. Murale ◽  
M. Sundarambal ◽  
R. Nedunchezhian
Keyword(s):  
Cardiac Disease ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Ensemble Methods ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Reduced Dimensions ◽  
The People ◽  
Ensemble Algorithms ◽  
Better Than

Coronary Heart disease is one of the dominant sources of death and morbidity for the people worldwide. The identification of cardiac disease in the clinical review is considered one of the main problems. As the amount of data grows increasingly, interpretation and retrieval become even more complex. In addition, the Ensemble learning prediction model seems to be an important fact in this area of study. The prime aim of this paper is also to forecast CHD accurately. This paper is intended to offer a modern paradigm for prediction of cardiovascular diseases with the use of such processes such as pre-processing, detection of features, feature selection and classification. The pre-processing will initially be performed using the ordinal encoding technique, and the statistical and the features of higher order are extracted using the Fisher algorithm. Later, the minimization of record and attribute is performed, in which principle component analysis performs its extensive part in figuring out the “curse of dimensionality.” Lastly, the process of prediction is carried out by the different Ensemble models (SVM, Gaussian Naïve Bayes, Random forest, K-nearest neighbor, Logistic regression, decision tree and Multilayer perceptron that intake the features with reduced dimensions. Finally, in comparison to such success metrics the reliability of the proposal work is compared and its superiority has been confirmed. From the analysis, Naïve bayes with regards to accuracy is 98.4% better than other Ensemble algorithms.

scholarly journals THE DESIGN OF EXPERT SYSTEM FOR DETERMINING THE INITIAL DIAGNOSIS OF TROPICAL INFECTIOUS DISEASES IN INDONESIA WITH NAIVE BAYES METHOD-BASED ANDROID

2019 ◽  
Vol 2 (2) ◽  
pp. 35-45
Author(s):  
Andrew Dwi Permana ◽  
I Made Arsa Suyadnya ◽  
Duman Care Khrisne
Keyword(s):  
Infectious Diseases ◽  
Mobile Application ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Bayes Method ◽  
The People ◽  
Bayes Formula ◽  
System Usability Scale ◽  
Tropical Infectious Diseases

Tropical infectious diseases are frequent, serious and concerning for the people in Indonesia. Tropical infectious diseases can be fatal and cause death. But if we diagnose them earlier and get proper treatment, the story can be changed. In this research will make a mobile application using Naive Bayes and Forward Chaining methods for early diagnosing tropical infectious diseases including typhoid fever, dengue fever, tuberculosis, malaria, and measles. The process of this application will start with input of the symptoms felt by users, after the data collected, system will calculate the data with Naive Bayes formula. This application using 147 data training from interviewed with the experts. Based on the tests by System Usability Scale method shows above average users rating 73.875 %, which means the results of the application are acceptable. And Confusion Matrix method shows performance of the application as high as 76.74 %.

scholarly journals Preparing Annotated Data on Covid -19 by Employing Naïve Bayes

2021 ◽  
Author(s):  
Dipankar Das ◽  
Akash Ghosh ◽  
AdityaR Rayala ◽  
Dibyajyoti Dhar ◽  
Vidit Sarkar ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
The Past ◽  
Positive Side ◽  
The People ◽  
The World ◽  
Bayes Algorithm ◽  
Opening Up

The on-going pandemic has opened the pandora’s box of the plethora of hidden problems which the society has been hiding for years. But the positive side to the present scenario is the opening up of opportunities to solve these problems on the global stage. One such area which was being flooded with all kinds of different emotions, and reaction from the people all over the world, is twitter, which is a micro blogging platform. Coronavirus related hash tags have been trending all over for many days unlikeany other event in the past. Our experiment mainly deals with the collection, tagging and classification of these tweets based on the different keywords that they may belong to, using the Naive Bayes algorithm atthe core.

scholarly journals Penerapan Algoritma Naive Bayes Untuk Klasifikasi Penerima Bantuan Surat Keterangan Tidak Mampu

2021 ◽  
Vol 2 (4) ◽  
pp. 206-213
Author(s):  
Nurulfah Riyanah ◽  
Fatmawati Fatmawati
Keyword(s):  
Data Processing ◽  
Naive Bayes ◽  
Community Services ◽  
Naïve Bayes ◽  
Birth Certificates ◽  
Vehicle Ownership ◽  
Literature Study ◽  
The People ◽  
Bayes Algorithm ◽  
Processing Techniques

Rukun Warga 002 Kelurahan Meruya Selatan runs a government program, namely assistance for recipients of a certificate of being unable to meet the community's needs and aims to improve the community's welfare. Rukun Warga 002 has community services, namely death certificate services, making ID cards, disability certificates (SKTM), birth certificates, and many more. In carrying out assistance, most of the community complained that they did not get help, while some people were considered capable of getting this assistance. The researcher carried out data processing techniques with observation, literature study, and questionnaires based on this background. In contrast, the data processing used data mining to determine the incapable recipient's proper or inappropriate status, namely by using the Naïve Bayes algorithm while using the Rapidminer application, aiming to test the dataset's accuracy. In the dataset of incapacitated recipients used in this study, there are 35 records with eight attributes: name, occupation, age, status, income, vehicle, ownership, and roof of the building, while this research aims to predict and produce level values. Accuracy in providing assistance letters of incapacity to the people of 002 sub-district of Meruya Selatan using the naïve Bayes method. The trial results show that the system accuracy rate is 62.86%, a recall of 78.57%, and 52.38% precision.

scholarly journals Implementation of Sentiment Analysis on Twitter Using Naïve Bayes Algorithm to Know the People Responses to Debate of DKI Jakarta Governor Election

2019 ◽  
Vol 1175 ◽  
pp. 012102 ◽  
Author(s):  
Yohanssen Pratama ◽  
Anthon Roberto Tampubolon ◽  
Liana Diantri Sianturi ◽  
Rifka Diana Manalu ◽  
David Frietz Pangaribuan
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The People ◽  
Bayes Algorithm

scholarly journals Evaluating Machine Learning Methods for Predicting Diabetes among Female Patients in Bangladesh

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 374
Author(s):  
Badiuzzaman Pranto ◽  
Sk. Maliha Mehnaz ◽  
Esha Bintee Mahid ◽  
Imran Mahmud Sadman ◽  
Ahsanur Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Machine Learning Methods ◽  
Learning Techniques

Machine Learning has a significant impact on different aspects of science and technology including that of medical researches and life sciences. Diabetes Mellitus, more commonly known as diabetes, is a chronic disease that involves abnormally high levels of glucose sugar in blood cells and the usage of insulin in the human body. This article has focused on analyzing diabetes patients as well as detection of diabetes using different Machine Learning techniques to build up a model with a few dependencies based on the PIMA dataset. The model has been tested on an unseen portion of PIMA and also on the dataset collected from Kurmitola General Hospital, Dhaka, Bangladesh. The research is conducted to demonstrate the performance of several classifiers trained on a particular country’s diabetes dataset and tested on patients from a different country. We have evaluated decision tree, K-nearest neighbor, random forest, and Naïve Bayes in this research and the results show that both random forest and Naïve Bayes classifier performed well on both datasets.

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