NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets

Jagadeesh, Madhu; Alphonse, P. J. A.

doi:10.18653/v1/2020.wnut-1.66

Cited by 6 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used RoBERTa [29] and XLNet [30] pre-trained networks. These models use Transformers [39,40] to capture bidirectional relationships and are suitable for flood-event classification. These models were selected based on their performance on similar tasks [40,41].…”

Section: Language Model Combination For Textual Datamentioning

confidence: 99%

See 1 more Smart Citation

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

et al. 2023

View full text Add to dashboard Cite

Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.

show abstract

Section: Language Model Combination For Textual Datamentioning

confidence: 99%

“…These models use Transformers [39,40] to capture bidirectional relationships and are suitable for flood-event classification. These models were selected based on their performance on similar tasks [40,41]. The implementation of both networks is performed with the help of the python-based Fastai [42] library.…”

Section: Language Model Combination For Textual Datamentioning

confidence: 99%

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

et al. 2023

View full text Add to dashboard Cite

show abstract

DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach

Demir

Şengür

2022

New Gener. Comput.

View full text Add to dashboard Cite

The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. The convolutional-autoencoder model, which had convolutional layers in encoder and decoder blocks, was trained by using the processed chest X-ray images from scratch for deep feature extraction. The distinctive features were selected with a novel and robust algorithm named SDAR from the deep feature set. In the classification stage, an SVM classifier with various kernel functions was used to evaluate the classification performance of the proposed method. Also, hyperparameters of the SVM classifier were optimized with the Bayesian algorithm for increasing classification accuracy. Specificity, sensitivity, precision, and F-score, were also used as performance metrics in addition to accuracy which was used as the main criterion. The proposed method with an accuracy of 99.75 outperformed the other approaches based on deep learning.

show abstract

Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN

Lella,

Jagadeesh,

Alphonse

2024

Health Inf Sci Syst

View full text Add to dashboard Cite

NIT_COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets

Cited by 6 publications

References 20 publications

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach

Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN

Contact Info

Product

Resources

About