Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

Amin, Samina; Uddin, Imad; Zeb, Misbah; Alarood, Ala Abdulsalam; Mahmoud, Marwan; Alkinani, Monagi H.

doi:10.32604/cmc.2021.014733

Cited by 8 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the features of the attention and feature interweave modules are combined to create a more accurate In addition, to perform evaluation, the ROC curve [37,38], which plots the TP rate as a function of the FP rate, is used. e ROC graph is constructed with TP rate on the yaxis and FP rate on the x-axis.…”

Section: Experimental Results and Performance Evaluationmentioning

confidence: 99%

Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

Amin

Alouffi

Uddin

et al. 2022

Scientific Programming

Self Cite

View full text Add to dashboard Cite

The coronavirus disease (COVID-19) outbreak, which began in December 2019, has claimed numerous lives and impacted all aspects of human life. COVID-19 was deemed an outbreak by the World Health Organization (WHO) as time passed, putting a tremendous strain on substantially all countries, particularly those with poor health services and delayed reaction times. This recently identified virus is highly contagious. Controlling the rapid spread of this infection requires early detection of infected people through comprehensive screening. For COVID-19 viral diagnosis and follow-up, chest radiography imaging is an excellent tool. Deep learning (DL) has been used for a variety of healthcare purposes, including diabetic retinopathy detection, image classification, and thyroid diagnosis. DL is a useful strategy for combating the COVID-19 outbreak because there are so many streams of medical images (e.g., X-rays, CT, and MRI). In this study, we used the benchmark chest X-ray scan (CXRS) dataset for both COVID-19-infected and noninfected patients. We evaluate the results of DL-based convolutional neural network (CNN) models after preprocessing the scans and using data augmentation. Transfer learning (TL) is used to improve the algorithm’s classification performance for chest radiography imaging. Finally, features of the attention and feature interweave modules are combined to create a more accurate feature map. The architecture is trained for COVID-19 CXRS using CNN, and the newly generated feature layer is applied to TL architecture. The experimental results found that training enhances the CNN + TL algorithm’s ability to classify CXRS with an overall detection accuracy of 99.3%, precision (0.97), recall (0.98), f-measure (0.98), and receiver operating characteristic (ROC) curve (area = 0.97). The results show that further training improves the classification architecture’s performance by 99.3%.

show abstract

Section: Experimental Results and Performance Evaluationmentioning

confidence: 99%

Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

Amin

Alouffi

Uddin

et al. 2022

Scientific Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, using the imbalanced dataset, accuracy, precision, and recall metrics cannot generate an accurate picture of the model’s efficiency (Amin et al. 2021 ; Powers 2020 ). In comparison with the metrics mentioned above for performance evaluation, the F1-score is another helpful class imbalance measure.…”

Section: Methodsmentioning

confidence: 99%

“… 2020 ), public health monitoring (Amin et al. 2020 , 2021 ), and so forth. Twitter is an SM application that allows users to post text, images, and short videos (Gleason 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content

et al. 2022

Self Cite

View full text Add to dashboard Cite

The COVID-19 infection, which began in December 2019, has claimed many lives and impacted all aspects of human life. With time, COVID-19 was identified as a pandemic outbreak by the World Health Organization (WHO), putting massive pressure on global health. During this ongoing pandemic, the exponential growth of social media platforms has provided valuable resources for distributing information, as well as a source for self-reported disease symptoms in public discourse. Therefore, there is an urgent need for effective approaches to detect self-reported symptoms or cases in social media content. In this study, we scrapped public discourse on COVID-19 symptoms in Twitter content. For this, we developed a huge dataset of COVID-19 self-reported symptoms and gold-annotated the tweets into four categories: confirmed, death, suspected, and recovered. Then, we use a machine and deep machine learning models, each with its own set of features, such as feature representation. Furthermore, the experimentations were achieved with recurrent neural networks (RNNs) variants and compared their performance with traditional machine learning algorithms. Experimental results report that optimizing the area under the curve (AUC) enhances model performance, and the long short-term memory (LSTM) has the highest accuracy in detecting COVID-19 symptoms in real-time public messaging. Thus, the LSTM classifier in the proposed pipeline achieves a classification accuracy of 90.7%, outperforming existing state-of-the-art algorithms for multi-class classification.

show abstract

“…Millions of people worldwide currently use SNSs to share images and videos, update their current status, and post regular comments on various topics. SNSs can also provide massive data that can be a very influential source of information during pandemic outbreaks [1,2]. Early warning on outbreak detection can decrease the influence of epidemic outbreaks on public health.…”

Section: Introductionmentioning

confidence: 99%

“…Early warning on outbreak detection can decrease the influence of epidemic outbreaks on public health. SNSs can now be used for disease surveillance to monitor the rate of epidemic outbreaks quicker than health care specialists and health organizations [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for COVID-19 Detection on Twitter

Amin¹,

Uddin²,

Al-Baity³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.

show abstract

Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

Cited by 8 publications

References 17 publications

Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content

Machine Learning Approach for COVID-19 Detection on Twitter

Contact Info

Product

Resources

About