A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

Islam, Nahidul; Sulaiman, Norizam; Bari, Bifta Sama; Rashid, Mamunur; Mustafa, Mahfuzah

doi:10.1016/j.neuri.2021.100037

Cited by 4 publications

(3 citation statements)

References 68 publications

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“…To overcome the limitations, some recent studies showed that effectively combining two or more classifiers provides an effective solution for task classification. In [40] [41] [42] [43], the researchers have developed hybrid models that perform much better than a single classifier and, in some cases, effectively overcome the constraints of a single classifier. For example, even though the kNN method has some significant advantages, including a small number of parameters and a faster training procedure, it also has several drawbacks including, the KNN approach does not balance well with large datasets since it becomes difficult for the algorithm to calculate distances in each dimension [40].…”

Section: Introductionmentioning

confidence: 99%

“…In [40] [41] [42] [43], the researchers have developed hybrid models that perform much better than a single classifier and, in some cases, effectively overcome the constraints of a single classifier. For example, even though the kNN method has some significant advantages, including a small number of parameters and a faster training procedure, it also has several drawbacks including, the KNN approach does not balance well with large datasets since it becomes difficult for the algorithm to calculate distances in each dimension [40]. The calculation of distance between new and existing locations is prohibitively expensive, reducing the algorithm's speed.…”

Section: Introductionmentioning

confidence: 99%

“…This hybrid architecture achieved a maximum of 96.25 % classification accuracy which is better than the accuracy of the single KNN. Another recent article in [40] proposes a novel hybrid scheme based on the convolutional layer of CNN and KNN classifier to classify the EEG-based AEP signals. They use the convolutional layer to extract the more informative information from the data, whereas the KNN method is utilized to classify the extracted feature.…”

Section: Introductionmentioning

confidence: 99%

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New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

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Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. There is still a great deal of space for further research on this area before reaching a definite decision. This study introduced a novel hybrid framework based on a bidirectional recurrent neural network (BiRNN) with a multilayered dilated convolution neural network (CNN) for arrhythmia classification. Initially, the raw ECG signals are filtered using Chebyshev Type II method and the Daubechies wavelet method is used to solve fractal problems and signal discontinuities. Then, a synthetic signal is generated using a generative adversarial network (GAN) to handle imbalanced signal classes. The proposed Bidirectional RNN with Dilated CNN (BRDC) architecture takes advantage of multilayered dilated CNN and bidirectional RNN units (bidirectional gated recurrent Units, BiGRU, bidirectional long short-term memory, BiLSTM) to generate fusion features and then, fusion features are classified in the fully connected layer. The PhysioNet 2017 challenge (MIT-BIH) dataset is used to train and validate the proposed approach. By combining fusion features with dilated CNN, the proposed approach outperforms the existing model for arrhythmia detection with 99.90 % accuracy, 98.41 % F1-score, 97.96 % precision, and 99.90 % recall. Overall, our hybrid BRDC model provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. In the future, an automatic and cloud-based system with more arrhythmia data variance to test the model's robustness will be given the highest priority.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

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et al. 2022

IEEE Access

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Regularized Anisotropic Filtered Tanimoto Indexive Deep Multilayer Perceptive Neural Network learning for effective image classification

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Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

Barile,

Casavola,

Katamba Mpoyi

et al. 2024

International Journal of Damage Mechanics

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This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM.

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A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

Cited by 4 publications

References 68 publications

New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

Regularized Anisotropic Filtered Tanimoto Indexive Deep Multilayer Perceptive Neural Network learning for effective image classification

Enhancing the acoustic emission technique using fuzzy artificial bee colony-based deep learning for characterizing selective laser melted AlSi10Mg specimens

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