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.
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.
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
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.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
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.
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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
Study and analysis of motion artifacts for ambulatory electroencephalographyIJECEIAES
Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.
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.
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.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Impact of adaptive filtering-based component analysis method on steady-state ...IAESIJAI
The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
An Entropy-based Feature in Epileptic Seizure Prediction Algorithmiosrjce
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure
onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction
algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods.
The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure
periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female
less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate
starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific
time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task.
SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance
is evaluated and analysed
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
Similar to Health electroencephalogram epileptic classification based on Hilbert probability similarity
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.
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
Study and analysis of motion artifacts for ambulatory electroencephalographyIJECEIAES
Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.
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.
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.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Impact of adaptive filtering-based component analysis method on steady-state ...IAESIJAI
The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
A Novel Approach For Detection of Neurological Disorders through Electrical P...IJECEIAES
This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots
An Entropy-based Feature in Epileptic Seizure Prediction Algorithmiosrjce
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure
onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction
algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods.
The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure
periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female
less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate
starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific
time. The prediction time in order of 1min- 49min is achieved in 60 seizure periods under study in this task.
SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance
is evaluated and analysed
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.
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.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
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.
Health electroencephalogram epileptic classification based on Hilbert probability similarity
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 3339~3347
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp3339-3347 3339
Journal homepage: http://ijece.iaescore.com
Health electroencephalogram epileptic classification based on
Hilbert probability similarity
Abdulkareem A. Al-Hamzawi, Dhiah Al-Shammary, Alaa Hussein Hammadi
College of Computer Science and Information Technology, University of Al-Qadisiyah, Dewaniyah, Iraq
Article Info ABSTRACT
Article history:
Received Aug 19, 2022
Revised Sep 7, 2022
Accepted Oct 1, 2022
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.
Keywords:
Electroencephalogram
classification
Epileptic
Hilbert similarity
Probability similarity
Seizure detection
This is an open access article under the CC BY-SA license.
Corresponding Author:
Abdulkareem A. Al-Hamzawi
College of Computer Science and Information Technology, University of Al-Qadisiyah
Dewaniyah, Iraq
Email: abdelkarim.karmul@qu.edu.iq
1. INTRODUCTION
An electroencephalogram (EEG) is a recording of the brain's electric activity that can reveal
information about brain conditions such as epilepsy and eye conditions. The most common neurological illness
in humans is epilepsy, which is characterized by recurring seizures [1], [2]. Seizures are rapid changes in the
electrical activity of the brain that cause changed behaviors such as loss of consciousness, jerky movements,
temporary lack of breath, and memory loss [3].
Initially, EEG signal processing was purely visual, difficult, time-consuming, and required the
assistance of a physician [4]. Changing this outdated classification system proves the difficulty of the process
which requires a lot of time and effort. Several studies in biomedical signal processing have focused on the
development of classification systems for automatic analysis [5]. Moreover, they concentrated on EEG signals
in the detection of epileptic seizures and the identification of eye states EEG [6]. In fact, this can then be
integrated into implantable devices that detect the beginning of seizures and trigger a focal treatment to stop or
slow the progression of seizures and improve patients' living conditions [7].
EEG signals produce a vast amount of data and visual evaluation of this data achieved by specialists
or neurologists is time-consuming and prone to inaccuracy [8]. Consequently, various EEG analysis approaches
to computerize the processing of EEG data have been developed. In medical applications, such as epileptic
treatment, developers have concentrated on evaluating the most important forms of EEG signals [9]. This paper
tackles the issue of detecting epileptic seizures by using another technique for EEG signals classification
depending on Hilbert probability similarity (HPS). HPS classifier can detect epileptic seizures from EEG
signals within a reasonable time-consuming.
Many medical and service applications rely on the classification of health data [10]. The provision of
broad patient monitoring and early diagnosis is essential. Machine learning (ML) algorithms rely heavily on
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3339-3347
3340
classification methods. On the other hand, they are prone to stagnation, trapped with local optimums, have
large time requirements, and produce inconsistent results [11], [12]. In order to overcome traditional
classification challenges and drawbacks, potential classification models are technically required. EEG epileptic
signal classification is a useful technique for early diagnosis and monitoring of epilepsy seizures.
A novel technique has been developed that exploits the use of HPS to diagnose epileptic seizures
using EEG signals. This paper has proposed two models for epileptic seizure detection: the first model is
created based on HPS classifier, and the results are significant with achieved accuracy of up to 100% in some
classification cases. A second model is built by combining HPS classifier with particle swarm optimization
(PSO) as an optimization method. The proposed model HPS with PSO has outperformed the first model in
several classification cases according to the accuracy metric results.
In this paper, HPS model has been designed for EEG signal analysis and epileptic seizure detection.
Bonn EEG dataset has been utilized for experiment and evaluation. Technically, the proposed model has been
compared with other most significant ML algorithms in terms of performance metrics accuracy, precision,
Recall, and F1-Score. Another comparison is made with other related work that adopts the same EEG dataset.
The experiments are achieved with several EEG dataset testing sizes with the whole EEG signals. Other results
are obtained based on various signal lengths. All testing cases are performed with and without optimization.
Empirically, the proposed model has clearly outperformed most machine learning algorithms in the same
environment.
The rest of this paper is structured as follows. In section 2, some of the related works are explained.
Section 3 demonstrates the proposed system with its main concepts. System results are calculated and compared
to other EEG classification methods are explained in section 4. Finally, section 5 describes the conclusions and
future work.
2. RELATED WORK
EEG classification is essential in a number of EEG-based services and applications. ML methods have
been frequently employed for the analysis or prediction of epileptic seizures in raw EEG signals. Technically,
a large number of studies concentrate on the detection of epileptic seizures within the EEG signals.
Nkengfack et al. [13] discussed the detection and identification of seizure or seizure-free states by
using EEG signals for epileptic patients. Discrete Legendre transforms (DLT) and discrete Chebyshev
transform (DChT) have been suggested for extraction of beta and gamma rhythms of EEG signal that would
be fed as an input to the least square support vector machine (LS-SVM) that is used for the classification
process. Technically, accuracy, sensitivity, specificity, and area under the curve (AUC) have been calculated
in order to evaluate the proposed model. The evaluation has entirely based on the Bonn university EEG dataset.
The best-achieved accuracy result started from 88.75% to 100%.
Mandhouj et al. [14] discussed EEG signals classification for normal, pre-ictal, and ictal classes in
order to help the detection of epileptic seizure onset. Short-time Fourier transform (STFT) has been utilized
for extracting useful information from EEG signals and exploited as an input to the convolution neural network
(CNN) classification model. Technically, sensitivity, specificity, accuracy, and precision have been computed
to assess the performance of the suggested model. The EEG dataset of Bonn University has been employed for
evaluation. The best-achieved result of accuracy was an average rate of 98.22%. On the other hand, standard
criteria such have not been used for evaluation such as F1-score, Recall, and AUC. Moreover, processing time
has not been computed to investigate how the proposed model is practical.
In order to extract and select the most discriminative features of the EEG signal, Abdelhameed and
Bayoumi [8] proposed a variational autoencoder (VAE). VAE is a combination of probabilistic graphical and
neural network models that are utilized for EEG signal classification. Technically, accuracy, sensitivity,
specificity, precision, and F1-score have been computed in order to evaluate the proposed model. The
evaluation has been completely based on two datasets, the Bonn University EEG dataset and the Children’s
Hospital EEG dataset in Boston. The best accuracy result has been obtained for the Bonn EEG dataset starting
from 99% to 100%, and for the Children’s Hospital, the EEG dataset started from 96.8% to 99.45%.
Samiee et al. [15] discussed the detection and classification of epileptic seizures from EEG patient
records, and how is the distinction made between epileptic seizures and normal artifacts with a similar time-
frequency paradigm. Discrete short-time Fourier transform (DSTFT) has been generalized to extract features
from EEG records. Multilayer perceptron (MLP) architecture has been selected for classification tasks from
multiple classifiers been experimented with. MLP has been trained with a back-propagation algorithm.
Technically, sensitivity, accuracy, and specificity have been computed for the proposed model evaluation. The
evaluation has been completely based on the Bonn university EEG dataset. The best accuracy result has been
obtained for two classes starting from 94.9% to 99.8%.
3. Int J Elec & Comp Eng ISSN: 2088-8708
Health Electroencephalogram epileptic classification based on Hilbert … (Abdulkareem A. Al-Hamzawi)
3341
3. PROPOSED METHOD
This section illustrates the complete design for the proposed models including the EEG dataset and
HPS measurement. Empirically, cosine similarity measurement, convex set in Hilbert space measurement of
similarity, and Hilbert probability-based measure of similarity have been implemented in the proposed method
in order to classifying EEG signals and epileptic seizure detection. Hilbert's probability-based measure of
similarity has outperformed the other measurements of similarity for EEG signals analysis and classification
in terms of performance metrics.
3.1. Hilbert probability similarity measurement
In this paper, HPS measurement has been proposed for EEG signal classification. Mathematically, the
HPS is used to compute the similarity between two vectors. Similarity can be obtained using (1) which
describes the Hilbert probability formula with its parameters,
Similarity = 𝑐𝑜𝑠−1
( 𝑃1
1
2 . 𝑃2
1
2 + 𝑄1
1
2 . 𝑄2
1
2 ) (1)
where P1 represents the probability of the most frequency value in the first vector. Similar to P1, P2 represents
the probability of the most frequency value in the second vector. The complement probability of p1 is (1-P1)
has been depicted by Q1 which mean the probability of the rest values within the first vector. Similarly, to that,
the complement probability of P2 is (1-P2) has been described by Q2 [16], [17]. Technically, this similarity
function has never been used in machine learning algorithms. The idea of developing Hilbert probability-based
similarity in a classification method is the core of the paper. Parameters of this function are exploited to
represent the most significant features within the EEG signal.
3.2. EEG dataset
In this paper, the Bonn University EEG dataset has been employed for the experiments and results
[18]. It is the most extensively used dataset for identifying epileptic seizures within EEG signals containing
five sets of one hundred EEG signals. Each set specifies an identical condition for patients as illustrated in
Table 1. Each EEG signal has 23.6 second time duration with 4,097 features.
Table 1. Bonn University EEG dataset
Set Name Samples Epoch duration Length of Segment Sample frequency Patient stage Patient situation
A 100 23.6 s 4097 173.61 (Hz) Eye open (Normal) Healthy
B 100 23.6 s 4097 173.61 (Hz) Eye close (Normal) Healthy
C 100 23.6 s 4097 173.61 (Hz) Seizure free (Pre-Ictal) Epileptic
D 100 23.6 s 4097 173.61 (Hz) Seizure free (Post-Ictal) Epileptic
E 100 23.6 s 4097 173.61 (Hz) Seizure activity (Ictal) Epileptic
3.3. Hilbert probability similarity classifier
The proposed classification model is mainly based on HPS measurement that is utilized for computing
the similarity of two vectors. In our system, these vectors refer to the EEG test signal with each train EEG
signal. Each vector represents an EEG signal that is depicted by a large number of data point sampling from
the EEG signal. Firstly, the EEG dataset is split into a training set and a testing set. Then for each signal in the
testing part, the similarity list is computed using Hilbert similarity-probability-based similarity measure
between each testing signal and all training EEG signals. Then, the similarity list is sorted in descending order
and the first top five items are considered for the predicted class by computing the peak of the histogram of the
EEG class label. Evidently, the decision to pick the top five is based on several experiments using one, two,
three, four, five, six, seven, eight, and nine top EEG cases that have empirically proven the highest accuracy
can be obtained by the five-selection decision. Finally, the accuracy metric is calculated for all testing signals.
All steps of the proposed system are shown in Figure 1.
3.4. Hilbert probability similarity with optimization
In this model, PSO is used to decrease the number of selected features in the EEG signal. PSO
produces better results, more quickly and more affordably [19]. PSO is a population-based stochastic
optimization technique that mimics animal social behavior like flocks of birds or schools of fish [20]. It begins
by randomly selecting a population (swarm) of potential solutions (particles) Generations are updated in order
to find the optimal solution. PSO first randomized the velocity and weight of all particles depending on specific
parameters. The fitness value for each particle is then determined as the global best position which represents
the optimal solution and is iteratively updated. Then, the velocity and position equations are used to update
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3339-3347
3342
particle velocity and position. Next, the complete optimization process would be repeated as a new iteration
with the same steps in order to reach the optimal solution [21]. Figure 2 illustrates the design of the proposed
model combined with optimization using the PSO algorithm.
Figure 1. HPS classifier
Figure 2. HPS classifier with PSO feature selection
4. EXPERIMENTS AND RESULT
In this section, experiments and results for the proposed system are explained and evaluated based on
the suggested models with and without the PSO algorithm. EEG signals of various durations and different
testing sizes are performed. Several widely used machine learning algorithms for classification tasks are
compared under the same conditions. Other comparisons are achieved with a number of previous studies that
are similar to our proposed models using the same dataset. Technically, accuracy, precision, recall, and
F1-score have been computed for the experiments and evaluation. Finally, in order to display the results in a
comprehensible manner, many illustrative charts have been presented.
4.1. Evaluation strategy
The Bonn University EEG dataset has included five different types of classes. As shown in Table 2,
the experiments are concentrated on the class that describes epilepsy seizures. The evaluation and comparison
process are carried out across the entire wavelength of EEG signals. The testing results are compared with
machine learning classification techniques such as K-nearest neighbor (KNN) with K=3, support vector
machine (SVM), random forest (RF), decision tree (DT), and naive Bayes (NB). Finally, another comparison
is achieved with previous studies that detailed the same EEG dataset.
5. Int J Elec & Comp Eng ISSN: 2088-8708
Health Electroencephalogram epileptic classification based on Hilbert … (Abdulkareem A. Al-Hamzawi)
3343
Table 2. Evaluation strategy
Test number EEG signals sets Description
1 S-Z Epilepsy seizure and health open eyes
2 S-O Epilepsy seizure and health closed eyes
3 S-F Epilepsy seizure and post-ictal
4 S-N Epilepsy seizure and pre-ictal
5 S-Z-O Epilepsy seizure against two cases of healthy people
4.2. Performance metrics
The evaluation of ML algorithms is the most important aspect of any machine learning project. Several
measures have been computed for the proposed model evaluation such as accuracy, precision, recall, and
F1-score. As a predictive metric, accuracy is the most often used parameter in the classification process. As
shown in Figure 3, a confusion matrix is a relationship between the actual class labels and the predicted class
labels [22].
Figure 3. Confusion matrix
The accuracy measure is the most commonly used performance metric. It is obtained by dividing the
total number of calculated results by the number of correct predictions. Accuracy is achieved based on the
confusion matrix using (2) [23].
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃+𝑇𝑁
𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
(2)
4.3. Results and analysis
The training set and testing set are obviously the two sets into which machine learning divides the
entire dataset. A testing set is used to evaluate the model performance using a number of ML efficiency
measures once the proposed model has been built and trained on a training set. Two scenarios have been
employed in this experiment, different testing sizes, and various signal lengths.
Scenario 1: Different test sizes
Different testing sizes have been employed in the evaluation strategy in order to properly construct
system performance evaluation and also to show how the size of the training set affects the patterns that have
developed. Empirically, 10%, 20%, 30%, and 40% of EEG signals testing size are performed during the
evaluation process. All computations are carried out without using any preprocessing for the signals with the
whole length of the EEG signal (23.6 seconds). Table 3 shows accuracy results for HPS classifier with and
without PSO compared to ML algorithms.
The results are obviously more accurate with a larger training size. Technically, this has resulted in a
wide range of patterns that assist the system in classifying the different signals of EEG data. Evidently, by
applying a 10% testing size without PSO, the proposed model has achieved high rates of accuracy up to 87%
in the case of three-class classification and up to 100% in the case of two-class classification. Moreover, the
proposed HPS classifier is combined with PSO and the obtained accuracy of two classes has reached up to
100% and 96.7% for three classes of EEG signals. On the other hand, the lowest obtained efficiency is the
result of using a 60% training set and a 40% testing set. The lowest obtained accuracy is 91.6 percent for two-
class without optimization and 97 percent for two-class with optimization. All resultant accuracy reported in
Table 3 is graphically displayed by a bar chart in Figure 4 that depicts the average accuracy for the HPS
classifier with and without PSO as well as for various ML techniques for different testing sizes.
Scenario 2: Different signal lengths
EEG signals are having a temporal period of up to 23.6 seconds (available in the Bonn dataset). Our
proposed model is tested with different signal lengths to check how the prediction results would vary. The test
has included signal lengths of 1, 5, 10, and 15 seconds. Furthermore, the electroencephalogram for the whole
signal length provided in Table 3 is subjected to a performance test.
In order to evaluate the performance of the Hilbert classifier based on the probability-similarity model,
the accuracy metric has been calculated and reported in Table 4. All results are compared against the most
widely used machine learning algorithms for classification, such as NB, SVM, DT, RF, and KNN.
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3339-3347
3344
Table 3. Accuracy results for HPS classifier with and without PSO for different testing size
Testing Type EEG signal duration Testing size HPS classifier HPS with PSO NB SVM RF DT KNN
S-Z 23.6 s 10% 100 100 100 50.0 100 85.0 65.0
S-O 23.6 s 10% 95.5 100 95.0 50.0 90.0 90.0 55.0
S-F 23.6 s 10% 95.5 100 90.0 50.0 95.0 80.0 55.0
S-N 23.6 s 10% 100 100 95.0 50.0 100 80.0 55.0
S-Z-O 23.6 s 10% 87.0 96.7 73.3 20.0 70.0 70.0 50.0
S-Z 23.6 s 20% 100 100 100 45 100 77.5 60
S-O 23.6 s 20% 92.5 100 97.5 45.0 95.0 72.5 47.5
S-F 23.6 s 20% 95.0 100 92.5 45.0 97.5 75.0 47.5
S-N 23.6 s 20% 95.0 97.5 95.0 45.0 95.0 87.5 47.5
S-Z-O 23.6 s 20% 80.0 88.3 76.7 33.3 73.3 58.3 38.3
S-Z 23.6 s 30% 100 100 100 45.0 100 76.6 61.6
S-O 23.6 s 30% 93.3 95.0 98.3 48.3 96.6 76.6 50.0
S-F 23.6 s 30% 91.6 95.0 95.0 48.3 95.0 81.6 50.0
S-N 23.6 s 30% 95.0 95.0 96.6 48.3 100 90.0 50.0
S-Z-O 23.6 s 30% 75.6 82.2 78.8 30.3 75.5 67.7 48.3
S-Z 23.6 s 40% 100 100 100 48.8 97.5 70.0 58.8
S-O 23.6 s 40% 95.0 97.5 97.5 48.8 97.5 72.5 51.2
S-F 23.6 s 40% 96.3 98.8 93.8 48.8 96.3 81.3 51.2
S-N 23.6 s 40% 96.3 98.8 97.5 48.8 98.8 73.8 51.2
S-Z-O 23.6 s 40% 80.8 86.2 80.0 29.2 75.8 52.5 42.5
Figure 4. Average accuracy for HPS model with and without PSO compared to machine learning algorithms
for different testing sizes
Table 4. Accuracy results for HPS classifier with and without PSO for different signal length
Testing Type EEG signal duration Testing size HPS classifier HPS with PSO NB SVM RF DT KNN
S-Z 1 sec 30% 91.67 93.33 98.33 48.33 93.33 90.00 81.67
S-O 1 sec 30% 75.00 80.00 95.00 48.33 90.00 70.00 66.67
S-F 1 sec 30% 85.00 90.00 86.67 48.33 91.67 83.33 65.00
S-N 1 sec 30% 83.33 86.67 95.00 48.33 95.00 76.67 63.33
S-Z-O 1 sec 30% 57.78 65.56 76.67 30.00 77.78 63.33 57.78
S-Z 5 sec 30% 95.00 100.00 100 48.33 100 76.67 71.67
S-O 5 sec 30% 83.33 90.00 96.67 48.33 93.33 71.67 55.00
S-F 5 sec 30% 86.67 95.00 93.33 48.33 93.33 86.67 53.33
S-N 5 sec 30% 90.00 96.67 95.00 48.33 98.33 81.67 55.00
S-Z-O 5 sec 30% 76.67 80.00 77.78 30.00 81.11 65.56 46.67
S-Z 10 s 30% 98.33 100 100 48.33 100 86.67 63.33
S-O 10 s 30% 83.33 93.00 96.67 48.33 96.67 71.67 53.33
S-F 10 s 30% 90.00 96.00 93.33 48.33 93.33 81.67 51.67
S-N 10 s 30% 91.67 98.33 96.67 48.33 100 81.67 53.33
S-Z-O 10 s 30% 78.89 84.44 78.89 30.00 78.89 53.33 43.33
S-Z 15 s 30% 100.00 100 100 48.33 100 83.33 61.67
S-O 15 s 30% 91.67 95.00 96.67 48.33 95.00 68.33 51.67
S-F 15 s 30% 90.00 96.67 93.33 48.33 91.67 75.00 51.67
S-N 15 s 30% 91.67 96.67 96.67 48.33 98.33 85.00 51.67
S-Z-O 15 s 30% 76.67 82.22 78.89 30.00 76.67 60.00 43.33
Clearly, HPS classifier produced a high accuracy of 91.7% without PSO and up to 93.3% with PSO
using an EEG signal with a length of one second. With and without PSO, the proposed models have produced
100
94.075
94.6
96.575
80.975
100
98.125
98.25
97.625
88.35
100
97.075
92.825
96.025
77.2
47.2
48.025
48.025
48.025
28.2
99.375
94.775
95.95
98.45
73.65
77.275
77.9
79.475
82.825
62.125
61.35
50.925
50.925
50.925
44.775
0
20
40
60
80
100
120
S,Z S,O S,F S,N S,Z,O
Accuracy
Test Mode
Hilbert Hilbert & Pso NB SVM RF DT KNN
7. Int J Elec & Comp Eng ISSN: 2088-8708
Health Electroencephalogram epileptic classification based on Hilbert … (Abdulkareem A. Al-Hamzawi)
3345
better results with an accuracy of up to 100% when the whole EEG signal length (23.6 seconds) has been
applied. The minimal accuracy result was reached by using three classes of EEG signals with only 177 features
at a one-second signal duration. It has reached 57.8% without PSO and 65.6% with PSO.
The average accuracy metric for 1 sec, 5 sec, 10 sec, 15 sec, and the whole EEG signal length is
calculated in order to present the results and performance in such a precise manner for all classification classes.
In order to show a clear view of these results Figure 5 depicts the results of the average accuracy metric for
different time durations for HPS classifier with and without PSO compared to ML algorithms.
4.4. Comparison with other models
Technically, a high number of alternative techniques have been developed for detecting epileptic
seizures. The proposed approach is compared to various previously developed approaches using accuracy
measures. This comparison only includes techniques that were implemented within the same dataset, allowing
comparisons of outcomes between groups belonging to the same classes.
The results of the comparison in Table 5 show that HPS models have outperformed most of the
previous methods. In order to assess the effectiveness of their classifiers, the majority of prior approaches to
classifying EEG data have only used two-class classifications. We have categorized various EEG signals and
recognized various states of EEG signals as opposed to the methods used by others.
Figure 5. Results of average accuracy metric for HPS classifier with and without PSO of different time durations
Table 5. A comparison of HPS classifier with other previous methods based on best accuracy metric
Author Methods Classification case Best Accuracy
Nkengfack et al. [13] LS-SVM A – E 100
Samiee et al. [15] DSTFT & MLP A – E 99.5
Peng et al. [24] Stein kernel-based sparse representation A – E 99
Liu et al. [1] Energy, ApEn with LPP, LS-SVM S – Z 98
Attia et al. [3] Burg + SVM A – E 98
Ech-choudany et al. [25] ANN A – E 100
Proposed model HPS classifier with PSO S - Z 100
Nkengfack et al. [13] LS-SVM B – E 100
Samiee et al. [15] DSTFT & MLP B – E 99.3
Peng et al. [24] Stein kernel-based sparse representation B – E 98.3
Liu et al. [1] Energy, ApEn with LPP, LS-SVM B – E ----
Attia et al. [3] Burg + SVM B – E 99
Ech-choudany et al. [25] ANN B – E 100
Proposed model HPS classifier with PSO S – O 100
Nkengfack et al. [13] LS-SVM C – E 100
Samiee et al. [15] DSTFT & MLP C – E 98.5
Peng et al. [24] Stein kernel-based sparse representation C – E 98.3
Liu et al. [1] Energy, ApEn with LPP, LS-SVM C – E 99.5
Attia et al. [3] Burg + SVM C – E 99
Ech-choudany et al. [25] ANN C – E 100
Proposed model HPS classifier with PSO S – N 100
Nkengfack et al. [13] LS-SVM D – E 100
Samiee et al. [15] DSTFT & MLP D – E 94.9
Peng et al. [24] Stein kernel-based sparse representation D – E 96.7
Liu et al. [1] Energy, ApEn with LPP, LS-SVM D – E 98
Attia et al. [3] Burg + SVM D – E 95
Ech-choudany et al. [25] ANN D – E 99.5
Proposed model HPS classifier with PSO S – F 100
97.0
98.7
99.7
47.7
98.7
82.7
68.0
85.3
90.6
96.7
48.3
94.3
71.7
55.3
88.7
94.5
92.3
48.3
93.0
81.7
54.3
90.3
94.7
96.0
48.3
98.3
83.0
54.7
73.5
78.9
78.2
30.1
78.0
62.0
47.9
0.0
20.0
40.0
60.0
80.0
100.0
120.0
Hilbert
Classifier
Hilbert & PSO NB SVM RF DT KNN
Accuracy
Classification methods
S-Z S-O S-F S-N S-Z-O
8. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 3, June 2023: 3339-3347
3346
4.5. Time consuming
The proposed classification models (Hilbert classifier based on probability similarity) have
outperformed most ML methods for several classification cases within the same environment that is been used
for implementation. Python programming language within Windows 10 operating system has been utilized for
designing and implementing our models. A personal computer running the system has a 2.30 GHz Intel Core
i7-11800H processor and 16 gigabytes of RAM.
The suggested model has an average processing time of 0.05447 ms, a maximum execution time of
0.07720 ms, and a minimum execution time of 0.03505 ms without PSO feature selection. HPS classifier with
PSO has an average processing time of 0.02535 ms, minimum execution time of 0.01139 ms, and maximum
execution time of 0.054212 ms.
5. CONCLUSION
In this paper, an automated approach for classifying and detecting epileptic seizures from EEG signals
is proposed based on HPS as a classifier model and PSO as a feature selection. The Bonn University EEG
dataset has been employed for experiments and results. Several performance metrics have been computed for
the evaluation such as precision, accuracy, recall, and F1-score. The proposed method is capable of achieving
high classification accuracy for two classes reached 100%. In order to properly handle more than two classes,
more model development would be performed for future work like adding signals clustering preceding HPS
classification.
REFERENCES
[1] Y. Liu, B. Jiang, J. Feng, J. Hu, and H. Zhang, “Classification of EEG signals for epileptic seizures using feature dimension reduction
algorithm based on LPP,” Multimedia Tools and Applications, vol. 80, no. 20, pp. 30261–30282, Aug. 2021, doi: 10.1007/s11042-
020-09135-7.
[2] M. Tosun and Ö. Kasım, “Novel eye‐blink artefact detection algorithm from raw EEG signals using FCN‐based semantic
segmentation method,” IET Signal Processing, vol. 14, no. 8, pp. 489–494, Oct. 2020, doi: 10.1049/iet-spr.2019.0602.
[3] A. Attia, A. Moussaoui, and Y. Chahir, “Epileptic seizures identification with autoregressive model and firefly optimization based
classification,” Evolving Systems, vol. 12, no. 3, pp. 827–836, Sep. 2021, doi: 10.1007/s12530-019-09319-z.
[4] Ö. Kasim and M. Tosun, “Biometric authentication from photic stimulated EEG records,” Applied Artificial Intelligence, vol. 35,
no. 15, pp. 1407–1419, Dec. 2021, doi: 10.1080/08839514.2021.1981660.
[5] A. A. Alsakaa, M. H. Hussein, Z. H. Nasralla, H. Alsaqaa, K. Nermend, and A. Borawska, “Effective electroencephalogram based
epileptic seizure detection using support vector machine and statistical moment’s features,” International Journal of Electrical and
Computer Engineering (IJECE), vol. 12, no. 5, pp. 5204–5213, Oct. 2022, doi: 10.11591/ijece.v12i5.pp5204-5213.
[6] J. Rabcan, V. Levashenko, E. Zaitseva, and M. Kvassay, “EEG signal classification based on fuzzy classifiers,” IEEE Transactions
on Industrial Informatics, vol. 18, no. 2, pp. 757–766, Feb. 2022, doi: 10.1109/TII.2021.3084352.
[7] Y. Jiang, W. Chen, M. Li, T. Zhang, and Y. You, “Synchroextracting chirplet transform-based epileptic seizures detection using
EEG,” Biomedical Signal Processing and Control, vol. 68, Jul. 2021, doi: 10.1016/j.bspc.2021.102699.
[8] A. M. Abdelhameed and M. Bayoumi, “Semi-supervised EEG signals classification system for epileptic seizure detection,” IEEE
Signal Processing Letters, vol. 26, no. 12, pp. 1922–1926, Dec. 2019, doi: 10.1109/LSP.2019.2953870.
[9] M. Radman, M. Moradi, A. Chaibakhsh, M. Kordestani, and M. Saif, “Multi-feature fusion approach for epileptic seizure detection
from EEG signals,” IEEE Sensors Journal, vol. 21, no. 3, pp. 3533–3543, Feb. 2021, doi: 10.1109/JSEN.2020.3026032.
[10] T. Dissanayake, T. Fernando, S. Denman, S. Sridharan, and C. Fookes, “Deep learning for patient-independent epileptic seizure
prediction using scalp EEG signals,” IEEE Sensors Journal, vol. 21, no. 7, pp. 9377–9388, Apr. 2021, doi:
10.1109/JSEN.2021.3057076.
[11] X. Lu, J. Zhang, S. Huang, J. Lu, M. Ye, and M. Wang, “Detection and classification of epileptic EEG signals by the methods of
nonlinear dynamics,” Chaos, Solitons & Fractals, vol. 151, Oct. 2021, doi: 10.1016/j.chaos.2021.111032.
[12] A. A. Al-Hamzawi, D. Al-Shammary, and A. H. Hammadi, “A survey on healthcare EEG classification-based ML methods,” in
Mobile Computing and Sustainable Informatics, 2022, pp. 923–936, doi: 10.1007/978-981-19-2069-1_64.
[13] L. C. Djoufack Nkengfack, D. Tchiotsop, R. Atangana, V. Louis-Door, and D. Wolf, “Classification of EEG signals for epileptic
seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square
support vector machine,” Informatics in Medicine Unlocked, vol. 23, 2021, doi: 10.1016/j.imu.2021.100536.
[14] B. Mandhouj, M. A. Cherni, and M. Sayadi, “An automated classification of EEG signals based on spectrogram and CNN for
epilepsy diagnosis,” Analog Integrated Circuits and Signal Processing, vol. 108, no. 1, pp. 101–110, Jul. 2021, doi:
10.1007/s10470-021-01805-2.
[15] K. Samiee, P. Kovacs, and M. Gabbouj, “Epileptic seizure classification of EEG time-series using rational discrete short-time
Fourier transform,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 2, pp. 541–552, Feb. 2015, doi:
10.1109/TBME.2014.2360101.
[16] Y. C. Sagala, S. Hariyanto, Y. D. Sumanto, and T. Udjiani, “The distance between two convex sets in Hilbert space,” in AIP
Conference Proceedings, 2021, doi: 10.1063/5.0041684.
[17] W. K. Wootters, “Statistical distance and Hilbert space,” Physical Review D, vol. 23, no. 2, pp. 357–362, Jan. 1981, doi:
10.1103/PhysRevD.23.357.
[18] S. S. Al-Fraiji and D. Al-Shammary, “EEG signals classification based on mathematical selection and cosine similarity,” Journal
of Al-Qadisiyah for Computer Science and Mathematics, vol. 13, no. 3, pp. 57–67, 2021, doi: 10.29304/jqcm.2021.13.3.837.
[19] T. M. Shin, A. Adam, and A. F. Z. Abidin, “A comparative study of PSO, GSA and SCA in parameters optimization of surface
grinding process,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 3, pp. 1117–1127, Sep. 2019, doi:
10.11591/eei.v8i3.1586.
9. Int J Elec & Comp Eng ISSN: 2088-8708
Health Electroencephalogram epileptic classification based on Hilbert … (Abdulkareem A. Al-Hamzawi)
3347
[20] R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” Proceedings of the International Symposium on Micro
Machine and Human Science, pp. 39–43, 1995, doi: 10.1109/mhs.1995.494215.
[21] A. P. Yoganandini and G. S. Anitha, “A modified particle swarm optimization algorithm to enhance MPPT in the PV array,”
International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 5, pp. 5001–5008, Oct. 2020, doi:
10.11591/ijece.v10i5.pp5001-5008.
[22] S. S. Al-Fraiji and D. Al-Shammary, “Survey for electroencephalography EEG signal classification approaches,” in Mobile
Computing and Sustainable Informatics, 2022, pp. 199–214, doi: 10.1007/978-981-16-1866-6_14.
[23] S. A. Shams, A. Hekal Omar, A. S. Desuky, M. T. Abou-Kreisha, and G. A. Elsharawy, “Even-odd crossover: a new crossover
operator for improving the accuracy of students’ performance prediction,” Bulletin of Electrical Engineering and Informatics, vol.
11, no. 4, pp. 2292–2302, Aug. 2022, doi: 10.11591/eei.v11i4.3841.
[24] H. Peng et al., “Automatic epileptic seizure detection via Stein kernel-based sparse representation,” Computers in Biology and
Medicine, vol. 132, May 2021, doi: 10.1016/j.compbiomed.2021.104338.
[25] Y. Ech-Choudany, D. Scida, M. Assarar, J. Landré, B. Bellach, and F. Morain-Nicolier, “Dissimilarity-based time–frequency
distributions as features for epileptic EEG signal classification,” Biomedical Signal Processing and Control, vol. 64, Feb. 2021,
doi: 10.1016/j.bspc.2020.102268.
BIOGRAPHIES OF AUTHORS
Abdulkareem A. Al-Hamzawi received a B.Sc. degree in computer sciences from
the University of Al-Qadisiyah, Iraq in 2005. Currently, he is an M.Sc. student at the College
of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq. He is
interested in artificial intelligence and machine learning techniques, and he has several
publications in these fields. He can be contacted at abdelkarim.karmul@qu.edu.iq.
Dhiah Al-Shammary 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 both 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 d.alshammary@qu.edu.iq.
Alaa Hussein Hammadi received his Ph.D. from Udmurt State University in
Russia. He is an assistant professor in applied mathematics. He is an academic at the College
of Computer Science and Information Technology at the University of Al-Qadisiyah, Iraq. He
has several publications in the area of applied mathematics and modeling systems in
Information Technology. He can be contacted at alaa.hammadi@qu.edu.iq.