Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images

Barua, Koushick; Mahmud, Tanjim; Barua, Anik; Sharmen, Nahed; Basnin, Nanziba; Islam, Dilshad; Hossain, Mohammad Shahadat; Andersson, Karl; Hossain, Sazzad

doi:10.1109/iccit60459.2023.10441124

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The array of ML techniques includes Logistic Regression, Support Vector Machines, Multinomial Naive Bayes, Decision Trees, Random Forest, K-Nearest Neighbors, as well as ensemble methods like Bagging, Boosting, and Voting. Especially, deep learning has played a pivotal role in advancing many important problems in signal processing [19][20][21], image processing [22][23][24], and medical diagnosis [25][26][27]. The deep learning methods applied in this study include Multilayer Perceptron, Simple Recurrent Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, Convolutional Neural Networks, as well as hybrid models DL such as BiLSTM+GRU, CNN+LSTM, CNN+BiLSTM, CNN+GRU, and (CNN+LSTM)+BiLSTM, along with Transformer-based models like BERT, Bangla BERT, Bangla ELECTRA [28], Multilingual BERT, and XLM-Roberta.…”

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confidence: 99%

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Mahmud,

Ptaszynski,

Masui

2024

Electronics

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Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.

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