This paper presents a survey for optimization approaches that analyze and classify electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
Health electroencephalogram epileptic classification based on Hilbert probabi...IJECEIAES
This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Efficient electro encephelogram classification system using support vector ma...nooriasukmaningtyas
Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more effectively.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Effective electroencephalogram based epileptic seizure detection using suppo...IJECEIAES
Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.
Health electroencephalogram epileptic classification based on Hilbert probabi...IJECEIAES
This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.
Efficient electro encephelogram classification system using support vector ma...nooriasukmaningtyas
Complex modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more effectively.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
Effective electroencephalogram based epileptic seizure detection using suppo...IJECEIAES
Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Ontheclassificationof ee gsignalbyusingansvmbasedalgorythmKarthik S
Major depression, also termed as major depressive disorder (MDD),
unipolar depression, clinical depression, or even simply depression, is a
mental illness. According to the World Health Organization (WHO),
depression has been identified as a leading cause of functional disability, worldwide. About 300 million people have been reported suffering
from depression, globally.1 In addition to the functional disability caused
by depression, it may lead to suicide ideations. Moreover, the treatment
management for depression has been challenging due to multiple factors,
such as the suitable selection of medication for a patient being based on
the subjective experience of clinicians and which might not be appropriate for the patient and could result into unsuccessful treatment trials.
Another implication is that the patient may stop the treatment.
In this chapter, the topics covered in this book are introduced by providing a basic explanation of the relevant concepts which will be elaborated on in later chapters. More specifically, this chapter explores the
possibilities of utilizing electroencephalogram (EEG) as an objective
method for the diagnosis and treatment efficacy assessment for depression. Also, depression will be discussed from different perspectives
such as its subtypes, signs and symptoms, the challenges associated
with treating depression, an overview of the literature involving EEG
studies for depression, EEG as a modality, and the basics of an EEGbased machine learning (ML) approach
Drivers’ drowsiness detection based on an optimized random forest classificat...IJECEIAES
The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Classification of electroencephalography using cooperative learning based on...IJECEIAES
Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
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Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Ontheclassificationof ee gsignalbyusingansvmbasedalgorythmKarthik S
Major depression, also termed as major depressive disorder (MDD),
unipolar depression, clinical depression, or even simply depression, is a
mental illness. According to the World Health Organization (WHO),
depression has been identified as a leading cause of functional disability, worldwide. About 300 million people have been reported suffering
from depression, globally.1 In addition to the functional disability caused
by depression, it may lead to suicide ideations. Moreover, the treatment
management for depression has been challenging due to multiple factors,
such as the suitable selection of medication for a patient being based on
the subjective experience of clinicians and which might not be appropriate for the patient and could result into unsuccessful treatment trials.
Another implication is that the patient may stop the treatment.
In this chapter, the topics covered in this book are introduced by providing a basic explanation of the relevant concepts which will be elaborated on in later chapters. More specifically, this chapter explores the
possibilities of utilizing electroencephalogram (EEG) as an objective
method for the diagnosis and treatment efficacy assessment for depression. Also, depression will be discussed from different perspectives
such as its subtypes, signs and symptoms, the challenges associated
with treating depression, an overview of the literature involving EEG
studies for depression, EEG as a modality, and the basics of an EEGbased machine learning (ML) approach
Drivers’ drowsiness detection based on an optimized random forest classificat...IJECEIAES
The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds).
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A Comparative Study of Machine Learning Algorithms for EEG Signal Classificationsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Classification of electroencephalography using cooperative learning based on...IJECEIAES
Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Survey analysis for optimization algorithms applied to electroencephalogram
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6891~6903
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6891-6903 6891
Journal homepage: http://ijece.iaescore.com
Survey analysis for optimization algorithms applied to
electroencephalogram
Ekram Hakem, Dhiah Al-Shammary, Ahmed M. Mahdi
Department Computer Science, College of Computer Science and Information Technology, Universitas of Al-Qadisiyah, Diwaniyah, Iraq
Article Info ABSTRACT
Article history:
Received Nov 9, 2022
Revised May 15, 2023
Accepted May 23, 2023
This paper presents a survey for optimization approaches that analyze and
classify electroencephalogram (EEG) signals. The automatic analysis of EEG
presents a significant challenge due to the high-dimensional data volume.
Optimization algorithms seek to achieve better accuracy by selecting practical
features and reducing unwanted features. Forty-seven reputable research
papers are provided in this work, emphasizing the developed and executed
techniques divided into seven groups based on the applied optimization
algorithm particle swarm optimization (PSO), ant colony optimization
(ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly,
and other optimizer approaches). The main measures to analyze this paper are
accuracy, precision, recall, and F1-score assessment. Several datasets have
been utilized in the included papers like EEG Bonn University, CHB-MIT,
electrocardiography (ECG) dataset, and other datasets. The results have
proven that the PSO and GWO algorithms have achieved the highest accuracy
rate of around 99% compared with other techniques.
Keywords:
Classification
Electroencephalogram data
Machine learning algorithms
Optimization algorithms
Particle swarm optimization
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ekram Hakem
Department Computer Science, College of Computer Science and Information Technology, Universitas of
Al-Qadisiyah
Diwaniyah, Iraq
Email: com21.post1@qu.edu.iq
1. INTRODUCTION
An electroencephalogram (EEG) signal is the most common way to diagnose changes in brain cells
[1]. EEG signals contain a vast amount of data [2]–[4], and visual diagnosis of them by neurophysiologists is
more prone to error, time-consuming, and complex. An EEG records the brain's electromagnetic Activity,
which can reveal crucial details about various brain disorders like epilepsy and eye issues [5]. On the scalp,
electrodes are employed.to determine EEG data. It identifies and monitors neurological disorders like sleep
apnea and epilepsy [6]. The most prevalent neurological condition affecting people is epilepsy, marked by
recurrent seizures [7]. Many studies and research endeavors, including gaming, neuromarketing, and many
others, employ EEG signals [8]. As a result, many researchers have suggested various optimization and
machine learning techniques to analyze and classify the EEG signal with high accuracy to protect people's
health and early detection of brain diseases [9]. EEG analysis is crucial for identifying epileptic seizures and
monitoring sleep disorders [10]. These signals are complex, noisy, nonlinear, and nonstable [11]. As a result,
recognizing and discovering information relating to the brain is a difficult task [12]. Furthermore, the
Automated analysis of EEG signals faces many problems due to the high dimensional data volume [13].
Moreover, optimization algorithms seek to obtain better accuracy by reducing the number of features and
exploiting the excellent search space within appropriate time intervals [3], [14].
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6891-6903
6892
2. MOTIVATION
Recently, the number of researchers analyzing the EEG signal has increased due to its importance in
discovering and diagnosing brain diseases [15]. EEG is a complex network of billions of neurons whose data
are interconnected, producing thousands of features per second [16]. It constitutes a burden on machine
learning algorithms in the classification of EEG as they suffer greatly from high features rate and several
undesirable features [17]. Therefore, optimization algorithms strive to select the optimal features for better
exploration and exploitation [18]. However, efficient feature extraction and reduction of data dimensions can
improve Complexity, processing time, and memory storage. Optimization algorithms suffer from many
limitations, which can have summed up in two steps. First, the problem of optimal local stagnation results from
a need for more diversity to discover new solutions and extract essential features from previous iterations.
Second, it takes much processing time, and the convergence rate of many iterative processes is low [19].
3. SURVEY STRATEGY AND EVALUATION
The thirty proposed optimization techniques for EEG signal enhancement are divided into seven
groups (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), bat
algorithm (BA), grey wolf optimizer (GWO), firefly algorithm (FA), and other optimizer approaches). These
are the most common techniques applied in evolutionary algorithms (EA). This survey analyzes the problem
and proposed solutions, evaluates performance using some metrics, and analyzes the best results. A critical
statement is shown for each approach. These studies focus on using some measures to assess performance,
such as precision, recall, accuracy, and F1-score. Technically, compare and evaluate the best results within
their groups.
Furthermore, the best accuracy for all techniques within the same group has been calculated and
compared with other improvement groups. The PSO and GWO groups have reached the highest accuracy level.
Moreover, most researchers ignored the evaluation of processing time, so the system's performance in most
techniques could have been clearer [20].
3.1. Particle swarm optimization
Satapathy et al. [21] have focused on the problem of detecting epileptic seizures in patients based on
EEG analysis and classification. An accurate analysis of these signals may be critical for the early detection of
many diseases of the human brain. This proposal uses a radial basis function neural network (RBFNN) to
classify the EEG signal. Furthermore, the mean square error (MSE) is optimized using the modified PSO
algorithm. PSO modification aims to overcome the slow search problem and find the optimal solution. Some
measures are computed for evaluation, including accuracy, precision, recall (true positive (TP), false negative
(FN), false positive (FP), true negative (TN)), and F1-score. Two EEG datasets are utilized one is an epileptic
seizure for the EEG dataset determination, and the other is eye condition prediction for the EEG dataset. This
paper has achieved a high accuracy of 99% compared with other methods. The proposed model achieved high-
precision classification results but ignored the careful performance analysis regarding processing time and
complexity.
Jamali-Dinan et al. [22] have focused on detecting patients with temporal lobe epilepsy (TLE), the
prevalent form of focal epilepsy. This paper applies a new method for optimizing particle swarm, and
Minkowski weighted k-means to determine the aspect of temporal lobe epilepsy. K-means it suffered from
noisy features. To address this problem, weighted k-means of optimization using Minkowski distance.
Generally, it is sensitive to the initialization assembly, therefore. PSO allows the user to avoid local stagnation
and maintain the advantages of PSO and mobile water kit (MWK) methods. The proposed model the evaluated
by computing the accuracy metric only. Furthermore, Silhouette criteria are applied to scale the better cluster
number. They have been used as standard datasets with previously named sets selected from the UCI Machine
Learning Repository. This method can identify epilepsy with 82.3% and 93.6% accuracy. This research could
have evaluated the model's performance, as it adopted the accuracy measure only and ignored many criteria,
such as processing time and complexity.
Another study by Sun et al. [23] discussed the problem of EEG signals and how they can easily be
affected by noise that impacts the intelligent diagnosis of diseases. To reduce noise affecting EEG signals and
improve the accuracy of feature extraction. This paper proposes using a wide, deep echo state network with
several parallel reservoirs instead of a single reservoir. EEG signals are trained to distinguish between noisy
and noise-free EEG features. However, uniform search particle swarm optimization (UPSO) is used to optimize
the reservoir parameters of a wide deep echo state network. The proposed model is evaluated by comparing
the signal-to-noise ratio (SNR); root means square error (RMSE), and nonlinear features. The high the SNR
value, the better the filtering impact. The model takes relatively less time. The dataset used in this paper is
obtained from the physical website. Experiments are applied to the dataset electrocardiography (ECG) and
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electromyography (EMG). SNR results of ECG noise are one dB=23.14416873 and RMSE with one
dB=0.040621086. The results of this research were unclear and lacked many measures, such as accuracy.
Moreover, this method performed relatively poorly in removing noise in some data types, such as ECG.
Detecting brain malignancies has been the focus of Deepa et al. [24]. It is critical for protecting human
health, catching diseases, and treating certain cancers. This paper has proposed a solution for brain tumor
detection. Firstly, a filtering algorithm performs the preprocessing to select important features. Second, to
speed up the search process, modified PSO is used to segment the tumor portion of the image and then
categorize it by the k-nearest neighbor (KNN) algorithm. The obtained accuracy is compared with the
sensitivity, specificity, and error rate to evaluate this proposed approach. The dataset used in this paper is a set
of magnetic resonance images (MRI) brain images obtained from a brain web database. An accuracy achieved
by this method of 98.2 percent is the best result. Technically, the new model has outperformed other
approaches. This research achieved high accuracy results, but compared to other methods the percentage of
change in results was low. Moreover, this research should have addressed the calculation of processing time.
3.2. Ant colony optimization
Miao et al. [25] have discussed the high dimensional features pattern of the motor imagery EEG
(MI EEG) classification. The authors have proposed a framework for cooperative optimization using adaptive
multi-domain features to optimize the MI EEG feature pattern. The algorithms for random forests (RF) and
composite kernel support vector machines (CKSVM) account for the potential and diverse local temporal–
frequency and spatial channels. ACO is proposed to identify more channels and temporal frequency segments.
The performance of the classifier is measured using a variety of performance formulas. The accuracy metrics
evaluate the proposed model competition III dataset Iva, Hand MI dataset, and Finger MI dataset. The highest
average accuracy is 90.85%. Researchers have failed to show some metrics as an accuracy scale is used only.
Therefore, accurate performance metrics are considered to be insufficient.
Fernandez-Fraga et al. [26] have focused on reducing feature extraction by removing irrelevant data
from EEG-acquired brain electrical signals to enhance brain-computer interaction (BCI) system performance.
ACO algorithms have been proposed by the authors to obtain the main features of signals and detect events
based on visual stimuli. The steady-state visual evoked potentials (SSVEP)-based BCI systems have an
advantage over other BCI systems because they have a superior SNR and faster information transfer rate (ITR).
The dataset used in this proposal is the original O1 and O2 signals from the EEG signals and the end outcome
of combining them to create a signal reconstruction. Compared to unreconstructed calls, O1-ACO has an 82.76
more significant correlation in original O1-O2, obtaining an 86.2% better correlation of O2-ACO versus
original O1-O2. This research lacks essential measures such as accuracy, processing time, and performance
complexity, and the results of the proposal and dataset used must be clarified.
Alghawli and Taloba [27] has focused on diagnosing and detecting depression, considered one of the
most common mental illnesses. The authors have proposed an improved ACO (IACO) technique to reduce the
number of features by removing irrelevant or extraneous feature data. To differentiate between bipolar disorder
(BD) and major depressive disorder (MDD), the support vector machine (SVM) loads the selected features and
classification. Metrics are used to evaluate performance methods such as accuracy rate, Recall, and area under
the curve (AUC) classification levels using various feature selection (FS) approaches. The dataset used BD
Patients in comparison to MDD patients. This research has achieved an average accuracy of 80.18 compared
to other approaches. ACO still needs to improve the practice algorithm's basic parameters, such as the
possibility of falling into the Local optimum, the significant computational effort and system resources needed
for the optimal answer, and the challenge of inventing the suggestive approach to achieve high efficiency.
3.3. Artificial bee colony
Satapathy et al. [28] mainly focused on detecting and classifying epileptic seizures vs. non-seizure
patients. ABC and RBFNNs are proposed in this paper to detect epileptic seizure disorders in the human brain
using EEG signal analysis. To evaluate the proposed method's performance, metrics such as accuracy, Recall,
accuracy, MSE, and discrete wave transformation (DWT) technique were used to extract potential features
from the signal. From the University of Bonn’s publicly accessible sources, five sets of EEG data for epileptic
seizure identification have been gathered. EEG data can be classified using a modified ABC algorithm, with
the maximum degree of accuracy for epilepsy identification is 82.3. The proposed method did not achieve high
results compared with other methods. Moreover, evaluation metrics such as time calculation and system
complexity are lacking.
Alshamlan et al. [29] have discussed the issue of the high dimensionality of the microarray gene
selection and cancer classification method. This paper addressed the A method based on ABCs for correctly
identifying cancer microarray data. The SVM is used to evaluate the effectiveness of gene selection approaches
for classification. To assess the efficacy of the suggested method using two criteria: the number of predictive
genes utilized for cancer classification and the accuracy of the classification. The microarray dataset is used in
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leukemia and colon datasets in this paper. The best result of the colon dataset in terms of accuracy is 95.61,
and the number of genes is 20. The best result of the leukemia dataset is an accuracy of 95.83 and several genes
of 20. When using this algorithm with complex and high-dimensional data like the Microarray dataset, the
ABC algorithm faces several challenging problems, particularly in processing efficiency.
Another research by Satapathy et al. [30] focused on the EEG classification problem, one of the
Invasive techniques; they can discover numerous instances of brain diseases, including epileptic seizures and
sleep disturbance. The ABC approach, which is utilized to improve the parameters required in the RBF network
to categorize the EEG signal, was presented in this paper. Adaptive synthetic sampling (ADASYN) enhances
the learning method for addressing the category imbalance issue in the EEG dataset. To assess the effectiveness
of multi-quadric RBFNN classifiers on imbalanced and balanced EEG data using ADASYN. To evaluate the
efficacy of multi-quadric RBFNN classifiers on imbalanced and balanced EEG data using ADASYN metrics
such as accuracy, recall, and computing the mean square error. The Department of Epileptology at the
University of Bonn provided these data. The best results are achieved by this proposal, with a high accuracy
rate of 92%. The results and evaluations are limited and have shown accuracy only as a measure of assessment.
A recent study by Ahirwal et al. [31] focused on the problem of the noise that occurs in the
EEG/filtering of event-related potentials (ERP), which is caused by hand or eye movement. The authors have
proposed an adaptive noise-canceling (ANC) system that was developed using the ABC method tool to filter
the EEG from ERP signals. ANC is implemented using the recursive least square (RLS) algorithm and the least
mean square (LMS) algorithm. SNR in dB and mean value difference are used to measure the algorithms'
performance. The squared error of the adaptive coil is examined in adaption time analysis using the mean and
standard deviation (STD). The effectiveness of ERP, kurtosis(k), and skewness(s) values-statistical shape
measurements are calculated. The Physio Net web database provided the information used in this study. While
it is 2.2343 for the LMS approach, and 0.5565 for the RLS technique, the average SNR obtained with the ABC
technique is closer to zero at 0.3095.
The results of this research are ambiguous due to the lack of measures such as accuracy and processing
time. Miao et al. [32] have addressed the channel selection pattern by removing numerous channels and
minimizing the computing overhead for the common spatial pattern (CSP) method. The MI EEG classification
of optimum frequency bands and period channels. Technically, they are essential for extracting MI EEG
features. This research uses the ABC algorithm to determine the frequency and time domain combinations that
are globally optimal. Simultaneously, prior expertise in CSP feature extraction and classification is not
required.
Moreover, it finds relatively optimal channels. To evaluate the performance was measured objectively
using the cross-validation average classification accuracy. Moreover, fisher’s linear discriminant criteria (FDC)
channel reduction is calculated. This study has used three EEG datasets for evaluation; the BCI Competition
III dataset IIIa, the BCI Competition III dataset Iva, and the BCI Competition I dataset. The algorithm achieved
an average classification accuracy for the first dataset up to 89.45%, the second at 90.76%, and the third at
0.5336%. The scales are insufficient to assess performance as only the accuracy scale is used.
3.4. Firefly algorithm
He et al. [33] has discussed the high-dimensional features pattern and complexity of emotion.
However, the mechanism of pattern recognition used in EEG-based emotion recognition is a complex process.
Using a new Firefly integrated optimization algorithm (FIOA) to identify emotions. The best feature selection,
parameter setting, and classifier selection can all be accomplished simultaneously. Based on diverse emotion
datasets EEG-based. To evaluate this research, many classification measures, including Recall, Precision,
specificity, negative predictive value (NPV), and accuracy, are used the check the compared methods. Both
Labe data and database for emotion analysis using physiological signals (DEAP) datasets have verified the
FOIA. The best result accuracy (ACC) of Labe data is 92%, and DEAP datasets are 86% with less feature
number comparison with PSO binary and F.A. binary. This method has achieved high accuracy with fewer
features. However, the researchers still need to calculate the processing time.
The optimal selection of EEG electrodes and features (EFS) for efficient classification has been
addressed by Lahiri et al. [34]. This research has presented a self-adaptive variant of the Firefly algorithm
(SAFA) proposed to improve individual targets by effectively balancing computational accuracy and run-time
complexity. Some measures, such as recall, precision, and average error rate, are evaluated. Moreover, Mobility
gives a ratio of the standard deviation of the EEG signal's slope to the standard deviation of the initial signal
amplitude. At the same time, Activity measures the EEG signal's squared standard deviation or variance.
Complexity indicates the frequency shift. The suggested approach uses a training dataset Tc (used for
classifying different cognitive tasks). The best results in this proposed Recall are 83.9196, a precision of
94.6017, and an average error rate of 99.8625. There needs to be a clear dataset used in this paper. Therefore,
the results of this research need to be more accurate.
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Attia et al. [35] have discussed EEG signals to determine epilepsy and epileptic seizures, which is a
complex problem. This paper has presented a new method using the autoregressive model (AR) in the process
of feature extraction and FA to get the best model order (P). The FA algorithm's goal function is the Akaike
information criterion's (AIC) lowest residual variance. A SVM classifier is used to classify epileptic seizure
signals. Accuracy, recall, and precision have evaluated performance. Allergy describes the precision as a
correct ratio of positive and negative divided by the total number of cases. A true positive ratio is as specific
as a true negative ratio. The Bonn EEG dataset is applied. It has been widely used in epilepsy and is available
online. The suggested method can classify data with an average accuracy, sensitivity, and specificity of 98.0%,
100.00%, and 96.0%. In this publication, the researchers have produced excellent results. The processing time
scale, however, is separate from this article.
Sharaf et al. [36] have focused on using EEG signals to find epileptic seizures. By reducing the
original features and producing a reduced compressed package. The authors have proposed a Firefly
optimization algorithm due to a feature selection and the vast quantity of retrieved features. A random forest
classifier is developed to categorize and forecast seizures and seizure-free cases. To evaluate performance,
some criteria, such as precision, recall, accuracy (ACC), F-score, receiver operating characteristics (ROC), and
Matthew’s correlation coefficient (MCC), are used. The University of Bonn acquired the standard data
collection used in this inquiry. The method has achieved an accuracy of 99%, precision of 97%, Recall of 98%,
F-score of 98%, and MCC of 95%. Although high classification accuracy of 99% was attained using this
method, the results are not compared with those from other approaches.
3.5. Bat algorithm
Kumar et al. [37] have addressed the problem of autism spectrum disorder (ASD). It is a diverse
neurodevelopmental disturbance that impacts the enhancement curve in many attitude domains and involves
the weakness of social communication, perceptual, and language abilities. This paper analyzes the signals EEG
that can detect ASD in children. Firstly, EEG signals are investigated in the dataset. The Kalman filter's
processing has already exposed this signal. Finally, the variable mode decomposition is used to achieve signal
analysis. The hybrid Bat algorithm with ANFIS classifier (HBA-ANFIS) is used to classify data. The proposed
model is evaluated using a variable mode decomposition technique (VMD). Different characteristics are
extracted from the analysis signal, including wavelet entropy, approximation entropy, relative wavelet entropy,
root mean square (RMS), Hurst exponent, correlation dimension, principal component analysis (PCA),
kurtosis, and skewness. Calculations are made for F1-score, accuracy, precision, and recall. These features are
then classified by the HBA and ANFIS classifiers. The signal is then categorized as either a standard or an
autistic instance. The dataset used in the paper comprises only EEG signals. The accuracy obtained from the
proposed is 98%; the precision value is 97% for the proposed method. The Recall for the proposed technique
is 0.97, higher than all the other methods. Due to the absence of processing time, this paper's performance
cannot be easily assessed.
Bablani et al. [38] have proposed developing a system for identifying deception using a test known as
the “concealed information test” can be achieved by identifying relevant channels and removing irrelevant
channels from the EEG signal classification. The SVM parameters are improved, and the EEG channels are
determined to boost the performance of the deceit identification system. This proposal has applied the non-
performing channels eliminated using binary BAT. This paper uses performance measurement for some
metrics, such as accuracy, specificity, sensitivity, and G-measure. Citizenship Law dataset is applied in the
proposed model. The average accuracy of the system increased from 94.11% to 96.8%. It was unclear how
well the model performed because the evaluation missed the time measure for encoding and decoding.
Another research by Dodia et al. [39] detecting lies by acquiring and preprocessing EEG brain signals.
Furthermore, features are extracted, and the optimal feature set is selected from the EEG. This paper has
provided a system that combines the short-time Fourier transform (STFT) approach for feature extraction, the
binary bat algorithm for feature selection, and the extreme learning machine (ELM) for classification. The
performance measures accuracy, precision, and Recall are used to evaluate the lie detection system
performance. These performance measures are the primary metrics for the classifiers. 600 EEG recording
samples from the experiment were used, of which 540 were used for training and 60 for testing. The proposed
lie detection system yields an accuracy of 88.3%. Results from the system have been significant. Although this
research has achieved good results, the performance measures must be revised and compared with other
techniques. However, the performance of the system could be clearer.
Mujeeb et al. [40] have focused on the problem of big data, which includes a high amount of
information that needs to be organized and stored. The authors have proposed map reduce framework
MRF-based optimization is used to deal with extensive imbalanced data classified using deep belief network
(DBN). The adaptive E-Bat algorithm that has been suggested has been applied in the feature selection process.
To evaluate the effectiveness of the proposed E-Bat DBN adaptive approach using the accuracy and true
positive rate (TPR) metrics. The results of this research are analyzed using six standard datasets obtained from
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the standard datasets of the UCI device repository, including breast cancer, hepatitis, Indian diabetes Pima,
heart disease, Poker Hand, and SUSY data. This research has achieved a high accuracy of 89.98% and a higher
TPR of 0.9144. The measures used in this paper need to be revised, as accuracy is the only applied metric.
3.6. Grey wolf optimizer
Karasu and Saraç [41] have discussed the problem of power quality (PQ) disturbances in the modern
electric network, such as high and low voltage. Moreover, continuous control by measurement can be complex
and take a long time. The researchers have proposed a novel method to merge the two-dimensional Reisz
transform (2D-RT). The statistical and image-based feature collection representing P.Q. disturbances is
extracted in the signal processing stage, and 1D signals are converted into 2D signals. Each 2D signal is
transformed using the 2D-RT technique, yielding 12 2D matrices, which are then used to categorize PQ
disturbances using the multiobjective grey wolf optimizer (MOGWO) using the KNN method. To assess the
suggested study. The mathematical mean, harmonic mean, geometric mean, standard deviation, skewness, and
kurtosis values are extracted as statistical features. Image properties are removed, including contrast,
homogeneity, and total compromise-the best result of the proposed with 99.26% accuracy. When compared to
alternative alternatives, the suggested approach has produced excellent outcomes. However, past studies have
produced results with higher overall accuracy. Additionally, this article lacked time processing.
Jaffino et al. [42] have focused on discovering and analyzing epileptic seizure diseases by monitoring
people's brain activity using EEG signals to identify the usual signals and signal epileptic seizures. To
accurately identify epileptic seizures in brain waves, this is addressed by using GWO-based on a deep recurrent
neural network (RNN) technique. Statistical parameters are computed to evaluate the proposed approach, such
as precision, Recall, and accuracy, as performance measures for this paper. The research's application dataset
is taken from the University of Bonn. This method has achieved results with an accuracy of 93.4%. Although
this research has presented results with high accuracy compared with other methods, the researchers still need
to calculate the processing time and Complexity of the system, which leaves ambiguity in the efficiency of this
system.
Another recent study [43] has performed research focusing on taking EEG/ERP as the input signal for
accurate adaptive noise cancellation. It might be generated due to movements like eyeball, hand movement, or
heart signals. ERP is too Weak signals combined with EEG with a meager SNR. GWO and other gradient-
based approaches to reduce the noise of the EEG signal and swarm techniques with an adjustable filter are used
to cleanse the EEG signal. To evaluate the method, the analysis of ERPs is achieved. Ensemble averaging (EA)
is one technique for identifying and removing the noise component from EEG readings. Wavelet denoising is
an algorithm that also uses the discrete wavelet transform. Furthermore, adaptive filter and applied quality
metrics mean and SNR. This paper uses the EEG/ERP data signal to analyze and implement the proposed
method. The best results of this proposed approach are the Maximum SNR of 3.204 was attained, and the
lowest correlation value was 0.0689. Additionally, it is predicted that the typical mean value is 0.00093. This
research has achieved high results by reducing the noise emitted by the EEG. However, a large group of
different techniques is used for improvement, and this has increased the complexity of the system and taken
longer processing time.
3.7. Other optimizer approaches
Baldominos and Ramon-Lozano [44] have discussed the problem of epilepsy seizures as a vital
neurological condition that causes seizures with a severe probable impact on human health. This paper has
described a seizure detection system based on energy. That is applied over EEG signals. This method includes
various parameters that have an essential impact on the detection performance. Genetic algorithms (GAs) are
used to optimize these parameters. The proposed model is evaluated by computing the FP and FN rates per
hour and TP. The investigation has significant accuracy with a low false positive rate for some patients. A
public dataset accessible via Physio Net is the CHB-MIT Scalp EEG database. With an average of 0.39 false
positives every 24 hours, the approach is better than previous methods at detecting very few false positives.
One of the disadvantages of this research is that the results are ambiguous, and the measurements need to be
completed.
Shon et al. [45] focused on detecting emotional stress state and feature selection using EEG signals.
In this article, the KNN classifier and GA-based feature selection is used to assess stress based on
analyzing EEG signals. The proposed model is evaluated by computing the accuracy only. The DEAP, an open-
source EEG dataset, was used to assess this paper. The obtained classification accuracy with the EEG dataset
was up to 71.76%. This research needs metrics such as computing processing time, error rate, and complexity.
Another research by Abdi et al. [46] discussed the difficulty of choosing a channel for EEG-based
biometric person identification. This article has proposed an approach to a multiobjective binary using the
cuckoo search algorithm (MOBCS-KNN) to identify people by selecting the best EEG channels. Moreover,
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EEG-based biometric person identification using the KNN classifier. Five measures are used to evaluate their
proposed model: channels selected, accuracy ratio, precision, F-score, and recall. In this study, the performance
is assessed using a common EEG motor imagery dataset. With an accuracy of 93.86 percent, the best results
can be obtained. The time metric that made it unclear for the model's efficacy to be evaluated in this article
must be included.
Pratiwi et al. [47] have focused on discovering epileptic seizures, a mental illness that impacts the
brain. Epilepsy can be confirmed by EEG classification. Their proposed model analyzes EEG signals using a
hybrid cuckoo search and neural network for epilepsy classifications. Moreover, the multi-layered perceptual
(MLP) weight is the cuckoo search method optimizes. Technically, two measures are computed for evaluation:
MSE and accuracy. The Epilepsy Center at the University of Bonn provided the EEG data utilized in this work.
The proposed methods have resulted in an MSE of 0.001 and an accuracy of 90.0%. This research achieved
high-accuracy results. However, it needed some critical measures of processing time and complexity.
The problem of feature extraction and selection to process the tri-axial acceleration data loggers data
and resolve the issue of the imbalanced dataset and measurement noise has been discussed by Yang et al. [48].
This research describes the feature extraction, choice, and application of the K-NN method to categorize the
behavior of sharks using the data gathered by ADLs. In this paper, performance measurement is achieved by
applying metrics such as recall, precision, and F1-score and calculating training time. Tri-axial acceleration
data recorders were used to gather the dataset ADLs. This research has achieved a high precision of 94%. It
needs clarity of the system's performance in terms of complexity.
Mo and Zhao [49] have addressed classifying BCI using electroencephalography for motor
imagery. This article proposed a SVM that improved by crediting a new bioinspired magnetic bacteria
optimization algorithm. To produce a high-performance classifier for brain-computer interfaces using
electroencephalography for motor imaging BCI. Technically, in the evaluation of the performance of this
proposal, accuracy criteria were applied for classification. This research shows the efficacy of the suggested
strategy using the BCI competition IV dataset II-a. This paper has achieved an accuracy of 67.3611. This paper
needs more than many vital metrics to assess the system's performance.
4. ANALYSIS AND EVALUATION
Three important measures have optimization to assess the outcomes of the optimization techniques
achieved by researchers: accuracy, precision, recall, and F1-score [50]. Most research has focused on
measuring accuracy, an essential component of the optimization process. Typically, all publications are
grouped into their primary optimization method: PSO, ACO, ABC, GWO, Bat, Firefly, and others. The
accuracy result is displayed in seven tables. Table 1 shows the accuracy of the PSO group with their tools.
Technically, the accuracy of the results obtained by all publications grouped with PSO optimizer started from
93.6% to 99%. However, several classification algorithms and tools used in the preprocessing process were
used in this research.
Moreover, all studies that utilized an ACO optimizer have demonstrated potential results accuracy
ranging from 80.18% to 90.85%, as presented in Table 2. Furthermore, all ABC-based research has generated
high accuracy results ranging from 82% to 95%, as shown in Table 3. Moreover, studies based on Firefly
optimizer have demonstrably displayed a significant accuracy range from 92% to 99%, as presented in
Table 4. As shown in Table 5, the high accuracy results for the Bat optimizer group are 98%. For the GWO
optimizer group, accuracy best results are 99.26% shown in Table 6.
The accuracy result reported in Tables 1 to 7 is visually represented by bar chart figures for a precise
visual analysis in Figures 1 to 7. Figure 1 shows the accuracy result by particle swarm optimization. Figure 2
shows the accuracy result by ACO optimization. Figure 3 shows the accuracy result by ABC optimization.
Figure 4 shows the accuracy result of Firefly optimization. Figure 5 shows the accuracy result of Bat
optimization. Figure 6 shows result accuracies by GWO optimization. Finally, Figure 7 shows the resultant
accuracy of the other optimizer approaches group. To have a precise comparison for all the included optimizers,
the average of all the extracted accuracy is computed and compared as shown in Figure 8. PSO, Firefly, and
GWO have shown the highest accuracy, above 99%. Then, Bat has shown the second-best accuracy with 98%.
ACO has shown a demonstrated average accuracy of around 90%.
Table 1. Accuracy result for PSO optimizer group
References Methods Dataset Accuracy (%)
Satapathy et al. [21] PSO+RBFNN Epileptology, University of Bonn 99%
Jamali-Dinan et al. [22] PSO+MWK-means TLE, MEG, and DTI 93.6%
Deepa et al. [24] PSO+KNN MRI 98.2%
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Table 2. Accuracy result for ACO optimizer group
References Methods Dataset Accuracy (%)
Miao et al. [25] ACO+RF+CKSVM III dataset Iva, Hand MI and Finger MI 90.85%
Alghawli and Taloba [27] IACO+SVM MDD+BD 80.18%
Table 3. Accuracy result for ABC optimizer group
References Methods Dataset Accuracy (%)
Satapathy et al. [28] ABC+RBFNN Five sets of EEG signals 82.3%
Alshamlan et al. [29] ABC+SVM leukemia and colon 95.83 %
Satapathy et al. [30] ABC+ADASYN Epileptology, University of Bonn 92%
Miao et al. [32] ABC BCI Competition III Dataset Iva, BCI Competition III Dataset
IIIa, and BCI Competition I Dataset
89.45%
Table 4. Accuracy result for Firefly optimizer group
References Methods Dataset Accuracy (%)
He et al. [33] FIOA Labe data 92%
Lahiri et al. [34] SAFA training dataset Tc 94%
Attia et al. [35] FA+SVM The Bonn EEG 98.0%
Sharaf et al. [36] FA+RF University of Bonn 99%
Table 5. Accuracy result for Bat optimizer group
References Methods Dataset Accuracy (%)
Kumar et al. [37] HBA–ANFIS Only EEG signals 98%
Bablani et al. [38] Binary BAT+SVM CIT dataset 94.11%
96.8%
Dodia et al. [39] BBAT+ STFT 600 EEG recording samples 88.3%
Mujeeb et al. [40] E-Bat DBN Including breast cancer, hepatitis, Indian diabetes Pima,
heart disease, Poker Hand, and SUSY data
89.98%
Table 6. Accuracy result for GWO optimizer group
References Methods Dataset Accuracy (%)
Karasu and Saraç [41] MOGWO+KNN PQ disturbances 99.26%
Jaffino et al. [42] GWO+RNN 'University of Bonn 93.4%
Table 7. Accuracy result for other optimizer approaches group
References Methods Dataset Accuracy (%)
Baldominos and Ramon-Lozano [44] GA CHB-MIT Scalp EEG
Shon et al. [45] GA+KNN DEAP 71.76%
Abdi et al. [46] MOBCS-KNN standard EEG motor imagery 93.86%
Pratiwi et al. [47] Hybrid cuckoo research the University of Bonn 90.0 %
Yang et al. [48] KNN ADLs 94%
Mo and Zhao [49] Magnetic bacteria+SVM BCI Competition IV dataset II- a 67%
Figure 1. Accuracy result for PSO optimizer group
99%
93.60%
98.20%
90%
91%
92%
93%
94%
95%
96%
97%
98%
99%
100%
Satapathy et al. [21] Jamali-Dinan et al. [22] Deepa et al. [24]
Accuracy
papers
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Figure 2. Accuracy result for ACO optimizer group
Figure 3. Accuracy result for ABC optimizer group
Figure 4. Accuracy result for Firefly optimizer group
Figure 5. Accuracy result for Bat optimizer group
90.85%
80.18%
70.00%
75.00%
80.00%
85.00%
90.00%
95.00%
Miao et al. [25] Alghawli and Taloba [27]
Accuracy
papers
82.30%
95.83%
92%
89.45%
75.00%
80.00%
85.00%
90.00%
95.00%
100.00%
Satapathy et al. [28] Alshamlan et al.
[29]
Satapathy et al. [30] Miao et al. [32]
Accuracy
papers
92%
94%
98.00%
99%
88%
90%
92%
94%
96%
98%
100%
He et al. [33] Lahiri et al. [34] Attia et al. [35] Sharaf et al. [36]
Accuracy
papers
98%
94.11%
88.30%
89.98%
82%
84%
86%
88%
90%
92%
94%
96%
98%
100%
Kumar et al. [37] Bablani et al. [38] Dodia et al. [39] Mujeeb et al. [40]
Accuracy
papers
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Figure 6. Accuracy result for GWO optimizer group
Figure 7. Accuracy result for other optimizer approaches group
Figure 8. Average accuracy with several optimization algorithms
5. CONCLUSION
In conclusion, this research has provided the thirty most effective techniques for EEG signal
optimization that were discussed and analyzed. All approaches are divided into seven groups based on the
primary optimization strategies proposed (PSO, ACO, ABC, GWO, Bat, Firefly, and other optimizer
approaches). The main measures for this research analysis are the evaluation of accuracy, precision, recall,
99.26%
93.40%
90.00%
92.00%
94.00%
96.00%
98.00%
100.00%
Karasu and Saraç [41] Jaffino et al. [42]
Accuracy
Paper
71.76%
93.86%
90.00%
94%
67%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Shon et al. [46] Abdi et al. [47] Pratiwi et al. [48] Yang et al. [49] Mo and Zhao [50]
Accuracy
Papers
[45]
85%
90%
95%
100%
99%
90.85%
95.83%
99%
98%
99.26%
94%
Accuracy
Optimization Algorthims
[46] [47] [48] [49]
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and F1-score. However, the system performance analysis and the processing time of all papers still need to
be included. Generally, all optimization groups have shown extraordinary accuracy abilities. PSO and GWO
approaches have outperformed.
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BIOGRAPHIES OF AUTHORS
Ekram Hakem has received a B.Sc. degree in Computer Sciences from the
University of Al-Qadisiyah, Iraq in 2016. Currently, she is an M.Sc. student in College of
Computer Science and Information Technology, University of Al-Qadisiyah, Iraq. She is
interested in artificial intelligence and machine learning techniques. She can be contacted at
email: com21.post1@qu.edu.iq.
13. Int J Elec & Comp Eng ISSN: 2088-8708
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Dhiah Al-Shammary has received his Ph.D. in Computer Science in 2014 from
RMIT University, Melbourne, Australia. Dhiah is awarded as the best Ph.D. student and top
publication during his Ph.D. period. He has several years of experience in both education and
industry. His main industrial experience came from Silicon Valley-based companies working
on security projects, including non-traditional and quantum-scale encryption. Dhiah has
worked at several universities in Australia and Iraq like RMIT University and the University
of Al-Qadisiyah. His research interests include performance modeling, Web services,
compression, and encoding techniques, and distributed systems. Dhiah has several
publications in the areas of improving the performance of web services and encoding
techniques. He can be contacted at email: d.alshammary@qu.edu.iq.
Ahmed M. Mahdi has received his Ph.D. in applied mathematics and I.T. in 2020
from Szeged University, Hungary. Also, he received a Master's degree in number theory in
2013 from Szczecin, Poland. He has several years of experience in both education and
industry. His research interests include number theory, numerical analysis, web services,
compression and encoding techniques, and distributed systems. Ahmed has several
publications in the areas of applied mathematics and IT. He can be contacted at email:
ahmed.m.mahdi@qu.edu.iq and ahmediraqmath@gmail.com.