Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection

Ghazali, Seyed Morteza; Alizadeh, Mousa; Mazloum, Jalil; Baleghi, Yasser

doi:10.1016/j.bspc.2022.103858

Cited by 27 publications

(12 citation statements)

References 29 publications

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“…The proposed BESD-Net without ERF achieved improvements of 4.53% in precision, 1.59% in sensitivity, 4.34% in F1-score, 2.89% in accuracy, and 1.03% in specificity compared to DCVAE [22]. MBSSA [30] Proposed BESD-Net…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This was accomplished using a wide range of different values used for the bifurcation parameter. The modified binary salp swarm technique was suggested for use in the application of EEG data categorization for the purpose of identifying epileptic episodes by Ghazali et al [30]. The diagnosis of epileptic seizures might benefit from the use of this approach.…”

Section: Literature Surveymentioning

confidence: 99%

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Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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Brain Epilepsy seizure is a critical disorder, which is an uncontrolled burst of electrical activity of brain. The early detection of brain seizure can save the life of humans. The electroencephalogram (EEG) signals may be used to automatically identify brain seizures, which is one of the most prominent solutions for this issue. However, the conventional methods are failed to classify the brain seizure effectively. So, this work implemented the Brain Epilepsy Seizure-Detection-Network (BESD-Net) using deep learning, recurrent learning properties. Initially, the dataset pre-processing is performed, which eliminates the noise, unwanted data from EEG dataset. Then, the deep learning based customized convolution neural network (CCNN) is trained on pre-processed EEG data for precise extraction of disease correlated features. The machine learning based exhaustive random forest (ERF) feature selection is used to optimize features from the CCNN features, which are highly correlated with disease dependent properties. In conclusion, the recurrent neural network (RNN) based bi-directional long short-term memory (BLSTM) is used in order to detect brain seizures from chosen ERF features. Training and testing of suggested methodology had made use of CHB-MIT Scalp EEG Database. The aforementioned model has achieved the values of 98.36%, 97.54%, 97.91%, 98% and 95.08% respectively for precision, sensitivity, F1-Score, accuracy and specificity. The findings of the simulations demonstrate that the suggested BESD-Net led to superior performance when compared to the technologies that are already in use.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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“…In their study, authors 53 employed an optimized adaptive neuro‐fuzzy inference system (ANFIS) to predict two indices related to CO2 Trapping in deep saline aquifers. To enhance the performance of the ANFIS model and optimize its parameters, they utilized two recently developed optimization algorithms, the AO and the salp swarm algorithm (SSA) 54 . This implementation resulted in the utilization of the SSA search mechanism instead of the AO method, thereby improving the exploration process of AO.…”

Section: Related Workmentioning

confidence: 99%

“…To enhance the performance of the ANFIS model and optimize its parameters, they utilized two recently developed optimization algorithms, the AO and the salp swarm algorithm (SSA). 54 This implementation resulted in the utilization of the SSA search mechanism instead of the AO method, thereby improving the exploration process of AO. Furthermore, in another study, 55 a hybrid algorithm named AOSMA was introduced for hyperparameter optimization.…”

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

Self Cite

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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“…The cylindrical invisible coating with the resonator rings has an acceptable performance in the microwave frequency range [25,26]. Users cannot use this coating in the visible light frequency range.…”

Section: Related Workmentioning

confidence: 99%

Design and construct full invisible band metamaterial-based coating with layer-by-layer structure in the microwave range from 8 to 10 GHz

Talati¹,

Ghazali²,

SoltaniNia³

2023

J. Phys. D: Appl. Phys.

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This paper presents the design and construction of a carpet-based anti-radar coating for frequencies from 8 to 10 GHz. The basic idea of the proposed method is to use a coating to guide microwaves around an object so that what is inside the coating becomes invisible. The proposed method uses metamaterial structures with heterogeneous properties to create coatings that provide complete bounding and prevent loss. Using the theory of light transformation and Zhukovsky mapping for the physical dimensions of the bulge to be invisible, the profile of the refractive index distribution with respect to the radiation of waves is determined. The simulation results of the refractive index profile have shown that it is possible to create an ideal invisible coating for each bulge by using the proposed unit cells and a layer-by layer-structure. Finally, after photolithography of the design on the 5880 fibers using a network analyzer and a pair of corrugated wave antennas, the accuracy of the proposed coating performance is confirmed.

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Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection

Cited by 27 publications

References 29 publications

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Design and construct full invisible band metamaterial-based coating with layer-by-layer structure in the microwave range from 8 to 10 GHz

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