Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm

Adel, Hadeer; Dahou, Abdelghani; Mabrouk, Alhassan; Elaziz, Mohamed Abd; Kayed, Mohammed; El-Henawy, Ibrahim M.; Alshathri, Samah; Ali, Abdelmgeid A.

doi:10.3390/math10030447

Cited by 39 publications

(22 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, many DCNN models have been suggested, which have been shown to enhance the productivity and effectiveness of machine learning (ML) [20,45]. Moreover, the DCNN models are among the most studied DL methods due to their capability to extract features automatically, and their adjustable structures, as in [15]. Many DL algorithms, such as MobileNet [21] and DenseNet [46], have incorporated the concept of depthwise separable convolutions to address the disadvantages of traditional operation.…”

Section: Deep Convolutional Neural Network (Dcnn) Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

et al. 2022

Self Cite

View full text Add to dashboard Cite

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models , which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database.

show abstract

Section: Deep Convolutional Neural Network (Dcnn) Modelsmentioning

confidence: 99%

“…To solve this problem, Transfer Learning (TL) has been developed. Due to its capacity to effectively solve the shortcomings of reinforcement learning and supervised learning, TL is becoming more widespread [14,15]. TL has the following types: Unsupervised, inductive, transductive, and negative learning.…”

Section: Introductionmentioning

confidence: 99%

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, the feature selection (FS) methods have been developed to improve the performance of the classification task [ 16 , 19 ]. For diagnosing medical images, in [ 20 ], they proposed a modified crow search algorithm as an FS technique to improve Parkinson's disease.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Elaziz

Mabrouk

Dahou

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset—both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.

show abstract

“…Many features, such as color, texture, and size, are used in standard medical image categorization methods. When controlling high-dimensional feature vectors through an optimizer algorithm, the selection of optimal features is offered in a way to improve classification efficiency [ 22 ]. The optimal representation of the specified subset of features creates additional issues for the researchers.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Mabrouk

Dahou

Elaziz

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

show abstract

Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm

Cited by 39 publications

References 44 publications

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Contact Info

Product

Resources

About