scholarly journals Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1353
Author(s):  
Jingfang Liu ◽  
Shuangjinhua Lu ◽  
Caiying Lu
Keyword(s):  
Qualitative Analysis ◽  
Herd Immunity ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Vaccine Hesitancy ◽  
Support Vector ◽  
Text Data ◽  
News Reports ◽  
Public Health Institutions ◽  
Health Institutions

(1) Background: The COVID-19 pandemic is globally rampant, and it is the common goal of all countries to eliminate hesitation in taking the COVID-19 vaccine and achieve herd immunity as soon as possible. However, people are generally more hesitant about the COVID-19 vaccine than about other conventional vaccines, and exploring the specific reasons for hesitation with the COVID-19 vaccine is crucial. (2) Methods: this paper selected text data from a social platform to conduct qualitative analysis of the text to structure COVID-19 vaccine hesitancy reasons, and then conducted semiautomatic quantitative content analysis of the text through a supervised machine-learning method to classify them. (3) Results: on the basis of a large number of studies and news reports on vaccine hesitancy, we structured 12 types of the COVID-19 vaccine hesitancy reasons. Then, in the experiment, we conducted comparative analysis of three classifiers: support vector machine (SVM), logistic regression (LR), and naive Bayes classifier (NBC). Results show that the SVM classification model with TF-IDF and SMOTE had the best performance. (4) Conclusions: our study structured 12 types of COVID-19 vaccine hesitancy reasons through qualitative analysis, filling in the gaps of previous studies. At the same time, this work provides public health institutions with a monitoring tool to support efforts to mitigate and eliminate COVID-19 vaccine hesitancy.

scholarly journals The First Few Hundred Cases for Coronavirus Disease 2019 (COVID-19) in Colombia

2020 ◽  
Author(s):  
Julián Felipe PORRAS-VILLAMIL ◽  
Mario Javier OLIVERA ◽  
Nadia Katherine RÍOS-CAMARGO
Keyword(s):  
Public Health ◽  
Observational Study ◽  
Geographical Distribution ◽  
Causative Agent ◽  
Age Group ◽  
News Reports ◽  
The World ◽  
Public Health Institutions ◽  
Health Institutions ◽  
Epidemiological Characteristics

Background: SARS-CoV-2 virus is the causative agent of COVID-19 disease. It is essential to understand the epidemiological characteristics of the first few cases in each country. This study aimed to describe the geographical distribution, and temporal appearance of the first few hundred cases in Colombia. Methods: This observational study was conducted to review the literature and key documentary information from public health institutions, websites and news reports were examined. Results: The first few 100 cases for COVID-19 were confirmed in Colombia. According to sex, men with 54% predominate, the most affected age group was 20 to 29 yr old (26%), 9% of the cases required hospitalization and no deaths were reported. Most of the confirmed subjects were from the departments of Cundinamarca. To date, most cases are imported (63%), especially from Spain. Conclusion: The COVID-19 pandemic puts in evidence the lack of understanding, prevention and contention power of the different countries around the world is not as good as it could be. Politics must not affect the different proposed measures.

Breast cancer prediction using an optimal machine learning technique for next generation sequences

2021 ◽  
pp. 1063293X2199180
Author(s):  
Babymol Kurian ◽  
VL Jyothi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Next Generation ◽  
Machine Learning Technique ◽  
Cancer Prediction ◽  
Learning Technique

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

scholarly journals Text classification based on gated recurrent unit combines with support vector machine

2020 ◽  
Vol 10 (4) ◽  
pp. 3734
Author(s):  
Muhammad Zulqarnain ◽  
Rozaida Ghazali ◽  
Yana Mazwin Mohmad Hassim ◽  
Muhammad Rehan
Keyword(s):  
Support Vector Machine ◽  
Language Processing ◽  
Text Classification ◽  
Classification Model ◽  
Support Vector ◽  
Text Data ◽  
Intelligent Technique ◽  
Learning Tasks ◽  
The One ◽  
Gated Recurrent Unit

As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.

Galvanic skin response to recognizing human behaviour

2021 ◽  
pp. 330-342
Author(s):  
Nilima Gautam ◽  
Jagdish Lal Raheja ◽  
Rajesh Bhadada
Keyword(s):  
Decision Tree ◽  
Emotion Recognition ◽  
Research Work ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Beings ◽  
Emotion Classification ◽  
Window Method ◽  
Human Emotions

Human beings' health is affected by physical ventures and emotional states endured by regular activities, which frequently develop attitudes and substantially affect health outcomes. Human Emotions play a vital role in deciding perception, cognition, memory, attention, reasoning, and decision-making. Several approaches have been used for automatically recognizing users' sentiment through images, speech, text, video, and physiological signals. Truthful detection of human emotions and personality behaviours can be advantageous for many situations, like interviews, group discussions, polygraphs, depressed persons, paralytic patients, blind people, shooters, etc. So, there is a need for an emotion recognizer. Though researchers tried several methods for emotion recognition, the accuracy of detection is always a question. The main aim is to develop a precise classification model for better accuracy of the emotion recognition system. Therefore, an emotion detector using GSR (Grove – GSR Sensor V1.2) sensor is proposed in the current research work. Twenty pupil groups were subjected to under observation for six different human activities, viz., happy, relax, stress, pain, reading, and math calculation. This research work was carried out in the lab of machine vision (CEERI) Central Electronics Engineering Research Institute Pilani, Jhunjhunu India. Moving average window method was used for data pre-processing. Supervised machine learning models viz., k-nearest neighbours (KNN), support vector machine (SVM), and decision tree (DT) was used for emotion classification. The decision tree model gives the best results with an average accuracy of 97.61%. Pain activity is most correctly recognized with greater than 99% accuracy.

scholarly journals Fault Diagnosis of Belt Conveyor Based on Support Vector Machine and Grey Wolf Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiangong Li ◽  
Yu Li ◽  
Yuzhi Zhang ◽  
Feng Liu ◽  
Yu Fang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Diagnostic Model ◽  
Grey Wolf ◽  
Belt Conveyor

Belt conveyor is widely used for material transportation over both short and long distances nowadays while the failure of a single component may cause fateful consequences. Accordingly, the use of machine learning in timely fault diagnosis is an efficient way to ensure the safe operation of belt conveyors. The support vector machine is a powerful supervised machine learning algorithm for classification in fault diagnosis. Before the classification, the principal component analysis is used for data reduction according to the varieties of features. To optimize the parameters of the support vector machine, this paper presents a grey wolf optimizer approach. The diagnostic model is applied to an underground mine belt conveyor transportation system fault diagnosis on the basis of monitoring data collected by sensors of mine internet of things. The results show that the recognition accuracy of the fault is up to 97.22% according to the mine site dataset. It is proved that the combined classification model has a better performance in fault intelligent diagnosis.

scholarly journals An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 347
Author(s):  
Linchao Yang ◽  
Guozhu Jia ◽  
Ke Zheng ◽  
Fajie Wei ◽  
Xing Pan ◽  
...  
Keyword(s):  
Information Fusion ◽  
Mode Selection ◽  
Selection Method ◽  
Fault Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Text Data ◽  
Text Feature ◽  
Diagnosis And Classification ◽  
Modeling Data

At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

scholarly journals Sentiment analysis of Twitter posts related to the COVID-19 vaccines

2021 ◽  
Vol 24 (3) ◽  
pp. 1727
Author(s):  
Noralhuda N. Alabid ◽  
Zainab Dalaf Katheeth
Keyword(s):  
Public Health ◽  
Support Vector Machine ◽  
Financial Support ◽  
Support Vector ◽  
Classification Models ◽  
The People ◽  
Framework Model ◽  
The World ◽  
Public Health Institutions ◽  
Health Institutions

A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.

scholarly journals Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning

2019 ◽  
Vol 143 (8) ◽  
pp. 990-998 ◽  
Author(s):  
Min Yu ◽  
Lindsay A. L. Bazydlo ◽  
David E. Bruns ◽  
James H. Harrison
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Turnaround Time ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Test Dataset ◽  
Manual Review

Context.— Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results. Objective.— To determine whether a classification model created by using standard machine learning algorithms can verify analytically acceptable MS results and thereby reduce manual review requirements. Design.— We obtained retrospective data from gas chromatography–MS analyses of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) in 1267 urine samples. The data for each sample had been labeled previously as either analytically unacceptable or acceptable by manual review. The dataset was randomly split into training and test sets (848 and 419 samples, respectively), maintaining equal proportions of acceptable (90%) and unacceptable (10%) results in each set. We used stratified 10-fold cross-validation in assessing the abilities of 6 supervised machine learning algorithms to distinguish unacceptable from acceptable assay results in the training dataset. The classifier with the highest recall was used to build a final model, and its performance was evaluated against the test dataset. Results.— In comparison testing of the 6 classifiers, a model based on the Support Vector Machines algorithm yielded the highest recall and acceptable precision. After optimization, this model correctly identified all unacceptable results in the test dataset (100% recall) with a precision of 81%. Conclusions.— Automated data review identified all analytically unacceptable assays in the test dataset, while reducing the manual review requirement by about 87%. This automation strategy can focus manual review only on assays likely to be problematic, allowing improved throughput and turnaround time without reducing quality.

scholarly journals A Support Vector Machine and Decision Tree Based Breast Cancer Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 2972-2976
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Misclassification Rate ◽  
Support Vector

The first step in diagnosis of a breast cancer is the identification of the disease. Early detection of the breast cancer is significant to reduce the mortality rate due to breast cancer. Machine learning algorithms can be used in identification of the breast cancer. The supervised machine learning algorithms such as Support Vector Machine (SVM) and the Decision Tree are widely used in classification problems, such as the identification of breast cancer. In this study, a machine learning model is proposed by employing learning algorithms namely, the support vector machine and decision tree. The kaggle data repository consisting of 569 observations of malignant and benign observations is used to develop the proposed model. Finally, the model is evaluated using accuracy, confusion matrix precision and recall as metrics for evaluation of performance on the test set. The analysis result showed that, the support vector machine (SVM) has better accuracy and less number of misclassification rate and better precision than the decision tree algorithm. The average accuracy of the support vector machine (SVM) is 91.92 % and that of the decision tree classification model is 87.12 %.

Development of a Classification Model for CD4 Count of HIV Patients Using Supervised Machine Learning Algorithms

2019 ◽  
pp. 149-176
Author(s):  
Peter Adebayo Idowu ◽  
Jeremiah Ademola Balogun
Keyword(s):  
Predictive Model ◽  
Decision Trees ◽  
Machine Learning Algorithms ◽  
Cd4 Count ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Hiv Patients ◽  
The Relationship

This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform the DT algorithm used.

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