Research on intrusion detection method based on 1D-ICNN-BiGRU

Liu, Runjie; Ji, Chaoqiang; Niu, Jinle; Guo, Bingjie

doi:10.1088/1742-6596/2347/1/012001

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…This synthesizes new plausible examples in the minority class, but the new samples generated by SMOTE also have some limitations. 49 , 50 The newly generated samples have distinct boundary limitations. The Tomek Links algorithm was combined to achieve data balance 50 to avoid this problem caused by oversampling.…”

Section: Methodsmentioning

confidence: 99%

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Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Chopannejad,

Roshanpoor,

Sadoughi

2024

DIGITAL HEALTH

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Objectives Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman–Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)–Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE–Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

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Section: Methodsmentioning

confidence: 99%

“… 49 , 50 The newly generated samples have distinct boundary limitations. The Tomek Links algorithm was combined to achieve data balance 50 to avoid this problem caused by oversampling. The Tomek Links algorithm is an under-sampling algorithm for identifying pairs of nearest neighbors in a dataset that have different classes.…”

Section: Methodsmentioning

confidence: 99%

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Chopannejad,

Roshanpoor,

Sadoughi

2024

DIGITAL HEALTH

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“…The average of the features across timesteps is used in the paper [ 15 ], and their maximum value is reported in [ 16 ]. The final alternative involves merging features over time, though this approach may result in a proliferation of inputs to the subsequent layer and contribute to overfitting in the model [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor

Dirgová Luptáková,

Kubovčík,

Pospíchal

2024

Sensors

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A transformer neural network is employed in the present study to predict Q-values in a simulated environment using reinforcement learning techniques. The goal is to teach an agent to navigate and excel in the Flappy Bird game, which became a popular model for control in machine learning approaches. Unlike most top existing approaches that use the game’s rendered image as input, our main contribution lies in using sensory input from LIDAR, which is represented by the ray casting method. Specifically, we focus on understanding the temporal context of measurements from a ray casting perspective and optimizing potentially risky behavior by considering the degree of the approach to objects identified as obstacles. The agent learned to use the measurements from ray casting to avoid collisions with obstacles. Our model substantially outperforms related approaches. Going forward, we aim to apply this approach in real-world scenarios.

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Research on intrusion detection method based on 1D-ICNN-BiGRU

Cited by 2 publications

References 9 publications

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor

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