A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM

Islam, Shofiqul; Ghani, Ngahzaifa Ab

doi:10.1007/978-981-33-4597-3_37

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…The difference between the bidirectional method [43] [45] and the unidirectional method [46] [47] is the backwards operation of LSTM or BiGRU, information from the feature is preserved, and by combining the two hidden states, it can be executed at any point in bidirection [45]. Furthermore, when BiGRU and BiLSTM are combined, more independent and long-range characteristics are handled [48]. Dilated CNNs instead of normal convolution layer [49] perform better because of its larger receptive field (no loss of coverage), more computationally efficiency, robustness (provides wide coverage on the same computation cost) [50] and less memory consumption since it skips the pooling step [9] as well as does not degrade the output image's resolution (used dilated rather than using pooling) [50].…”

Section: Introductionmentioning

confidence: 99%

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

Islam

Hashim

et al. 2022

IEEE Access

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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%

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

Islam

Hashim

et al. 2022

IEEE Access

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“…In terms of accuracy and performance, deep attempting to learn procedures, on the other hand, outperform other similar techniques. In the classification of image [3][4], video [5][6], speech [7], and text [2][8][9] [10], IoT based analysis [11] [12], deep learning outperformed. Several neural network models have already been proved superior to others.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis

Islam¹,

Hasan²,

Rahim

et al. 2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The growth of the entertainment industry around the world may be seen in the creation of new genres and the influx of artists and musicians into this field. Every day, a large amount of music is generated and released. The classification of this music based on genres and the recommendation of music to users is a crucial task for various music streaming platforms. Many artificial intelligence methods have been created to overcome this. Inadequate data for training is one of the biggest issues when it comes to building machine learning algorithms. A certain dataset contains a large number of redundant features, which may lead the models to overfit. Data filtering could be used to solve this issue. On the GTZAN data for music genre classification, this article constructed numerous Artificial Intelligence (AI) models and used a data filtering strategy. This study does a comparative analysis and discusses the results. The models developed and evaluated are Naive Bayes

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Tweets Sentiment Analysis of Putin’s Participation at the G20 Summit in Indonesia

Hasanah

Firmansyah

Nugraha

et al. 2023

Computational Science and Its Applications – ICCSA 2023 Workshops

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A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM

Cited by 8 publications

References 19 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

Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis

Tweets Sentiment Analysis of Putin’s Participation at the G20 Summit in Indonesia

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