Thaer Thaher - Academia.edu (original) (raw)

Papers by Thaer Thaher

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)

Expert Systems with Applications

Diagnostics

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of ma... more Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characterist...

JUCS - Journal of Universal Computer Science

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious th... more The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their populat...

Communications in computer and information science, 2022

Applied Intelligence

Software Fault Prediction (SFP) is an important process to detect the faulty components of the so... more Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

JUCS - Journal of Universal Computer Science

Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-inf... more Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Sev...

Neural Computing and Applications

Real-time tracking of pedestrians has attracted tremendous attention for many purposes, especiall... more Real-time tracking of pedestrians has attracted tremendous attention for many purposes, especially studying their behavior. The perfect tracking is still a challenging open research. In this paper, a novel approach was introduced by employing a multi-agent vision-based system to achieve an accurate real-time tracker. The proposed approach is recommended to be used in open cultural places where there may be some obstacles including buildings, columns, and many others. The proposed model efficiency was assessed on four real Multiple Object Tracking (MOT) challenge benchmarks in terms of Multi-Object Tracking Accuracy (MOTA). Simulation results reveal a high efficacy of the proposed model compared with the state-of-the-art techniques.

Future Generation Computer Systems, 2020

Abstract Smart connected appliances expand the boundaries of the conventional Internet into the n... more Abstract Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented Whale Optimization Algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset; wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems.

Algorithms for Intelligent Systems, 2019

Feature selection is a preprocessing step that aims to eliminate the features that may negatively... more Feature selection is a preprocessing step that aims to eliminate the features that may negatively influence the performance of the machine learning techniques. The negative influence is due to the possibility of having many irrelevant and/or redundant features. In this chapter, a binary variant of recent Harris hawks optimizer (HHO) is proposed to boost the efficacy of wrapper-based feature selection techniques. HHO is a new fast and efficient swarm-based optimizer with various simple but effective exploratory and exploitative mechanisms (Levy flight, greedy selection, etc.) and a dynamic structure for solving continuous problems. However, it was originally designed for continuous search spaces. To deal with binary feature spaces, we propose a new binary HHO in this chapter. The binary HHO is validated based on special types of feature selection datasets. These hard datasets are high dimensional, which means that there is a huge number of features. Simultaneously, we should deal with a low number of samples. Various experiments and comparisons reveal the improved stability of HHO in dealing with this type of datasets.

Expert Systems with Applications, 2022

Traffic congestion is one of the most important problems with respect to people daily lives. As a... more Traffic congestion is one of the most important problems with respect to people daily lives. As a consequence, a lot of environmental and economic problems were emerged. Several works were proposed to participate in problem solving. Traffic signal management introduced a promising solution by minimizing vehicles average travel times and hence decreasing traffic congestion. Studying vehicles’ activities on roads is non-deterministic by nature and contains several continuously changing parameters which makes it hard to find an optimal solution for the mentioned problem. Therefore, optimization techniques were intensively exploited with respect to Traffic Signal Scheduling (TSS) systems. In this work, we propose TSS control methodology based on Whale Optimization Algorithm (WOA) in order to minimize Average Travel Time (ATT). Experimental results show the superiority of WOA over other related algorithms specially with the case of large-scale benchmarks.

2020 International Conference on Promising Electronic Technologies (ICPET), 2020

Water plays a significant role in the economic development of countries. The agriculture sector i... more Water plays a significant role in the economic development of countries. The agriculture sector is the most water-consuming; this sector consumes 69% of the freshwater. However, farmers often use traditional irrigation systems to water their crops. These systems are ineffective and consume a IoT of time and effort, mainly when several fields are distributed in different geographical regions. Therefore, employing smart irrigation techniques will significantly overcome these problems. This paper proposes an intelligent irrigation framework based on Wireless Sensor Network (WSN) and Internet of Things (IoT) cloud services. The proposed system consists of three main components; the WSN, the control unit, and cloud services. Arduino Uno and XBee ZigBee modules are combined to gather sensors data and send them wirelessly to the control unit. YL-69 sensor is used to monitor the soil moisture. Raspberry Pi is utilized to gather data, process them, provide the proper decision, and transfer t...

In the software development process, the testing phase plays a vital role in assessing software q... more In the software development process, the testing phase plays a vital role in assessing software quality. Limited resources pose a challenge in achieving this purpose efficiently. Therefore, early stage procedures such as software fault prediction (SFP) are utilized to facilitate the testing process in an optimal way. SFP aims to predict fault-prone components early based on some software metrics (features). Machine learning (ML) techniques have proven superior performance in tackling this problem. However, there is no best classifier to handle all possible classification problems. Thus, building a reliable SFP model is still a research challenge. The purpose of this paper is to introduce an efficient classification framework to improve the performance of the SFP. For this purpose, an ensemble of multi-layer perceptron (MLP) deep learning algorithm boosted with synthetic minority oversampling technique (SMOTE) is proposed. The proposed model is benchmarked and assessed using sixteen ...

2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT)

2019 2nd International Conference on new Trends in Computing Sciences (ICTCS)

The non-uniform distribution of classes (imbalanced data) and the presence of irrelevant and/or r... more The non-uniform distribution of classes (imbalanced data) and the presence of irrelevant and/or redundant information are considered as challenging aspects encountered in most real-world domains. In this paper, we propose an efficient software fault prediction (SFP) model based on a wrapper feature selection method combined with Synthetic Minority Oversampling Technique (SMOTE) with the aim of maximizing the prediction accuracy of the learning model. A binary variant of recent optimization algorithm; Queuing Search Algorithm (QSA), is introduced as a search strategy in wrapper FS method. The performance of the proposed model is assessed on 14 real-world benchmarks from the PROMISE repository in terms of three evaluation measures; sensitivity, specificity, and area under the curve (AUC). Experimental results reveal a positive impact of the SMOTE technique in improving the prediction performing in a highly imbalanced data. Moreover, the binary QSA (BQSA) show a superior efficacy on 64.28% of datasets compared with other state-of-the-art algorithms in handling the problem of FS. The combination of BQSA and SMOTE achieved an acceptable AUC results (66.47-87.12%).

The Artificial Bee Colony (ABC) is a novel nature-inspired metaheuristic optimization algorithm t... more The Artificial Bee Colony (ABC) is a novel nature-inspired metaheuristic optimization algorithm that mimics the behavior of honey bees searching for food sources. The main drawback of ABC, similar to the most of metaheuristics, is the premature convergence (i.e., the earlier stuck into local optima). Recently, the structured population approach, in which the individuals are distributed into multiple sub-populations (called islands), has been widely exploited to maintain the required diversity during the search process and thus reducing the prematurity problem. In this paper, the island model, which is a common structured population approach, is incorporated with the ABC to introduce a parallel variant called (iABC). Besides, an experimental design approach is proposed to analyze the sensitivity of iABC to the parameters of the island model as well as the main specific parameters. The linear regression model and the Analysis of variance (ANOVA) are utilized to estimate the effect of ...

Applied Sciences

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortag... more Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ab...

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)

Expert Systems with Applications

Diagnostics

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of ma... more Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characterist...

JUCS - Journal of Universal Computer Science

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious th... more The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their populat...

Communications in computer and information science, 2022

Applied Intelligence

Software Fault Prediction (SFP) is an important process to detect the faulty components of the so... more Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

JUCS - Journal of Universal Computer Science

Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-inf... more Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Sev...

Neural Computing and Applications

Real-time tracking of pedestrians has attracted tremendous attention for many purposes, especiall... more Real-time tracking of pedestrians has attracted tremendous attention for many purposes, especially studying their behavior. The perfect tracking is still a challenging open research. In this paper, a novel approach was introduced by employing a multi-agent vision-based system to achieve an accurate real-time tracker. The proposed approach is recommended to be used in open cultural places where there may be some obstacles including buildings, columns, and many others. The proposed model efficiency was assessed on four real Multiple Object Tracking (MOT) challenge benchmarks in terms of Multi-Object Tracking Accuracy (MOTA). Simulation results reveal a high efficacy of the proposed model compared with the state-of-the-art techniques.

Future Generation Computer Systems, 2020

Abstract Smart connected appliances expand the boundaries of the conventional Internet into the n... more Abstract Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented Whale Optimization Algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset; wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems.

Algorithms for Intelligent Systems, 2019

Feature selection is a preprocessing step that aims to eliminate the features that may negatively... more Feature selection is a preprocessing step that aims to eliminate the features that may negatively influence the performance of the machine learning techniques. The negative influence is due to the possibility of having many irrelevant and/or redundant features. In this chapter, a binary variant of recent Harris hawks optimizer (HHO) is proposed to boost the efficacy of wrapper-based feature selection techniques. HHO is a new fast and efficient swarm-based optimizer with various simple but effective exploratory and exploitative mechanisms (Levy flight, greedy selection, etc.) and a dynamic structure for solving continuous problems. However, it was originally designed for continuous search spaces. To deal with binary feature spaces, we propose a new binary HHO in this chapter. The binary HHO is validated based on special types of feature selection datasets. These hard datasets are high dimensional, which means that there is a huge number of features. Simultaneously, we should deal with a low number of samples. Various experiments and comparisons reveal the improved stability of HHO in dealing with this type of datasets.

Expert Systems with Applications, 2022

Traffic congestion is one of the most important problems with respect to people daily lives. As a... more Traffic congestion is one of the most important problems with respect to people daily lives. As a consequence, a lot of environmental and economic problems were emerged. Several works were proposed to participate in problem solving. Traffic signal management introduced a promising solution by minimizing vehicles average travel times and hence decreasing traffic congestion. Studying vehicles’ activities on roads is non-deterministic by nature and contains several continuously changing parameters which makes it hard to find an optimal solution for the mentioned problem. Therefore, optimization techniques were intensively exploited with respect to Traffic Signal Scheduling (TSS) systems. In this work, we propose TSS control methodology based on Whale Optimization Algorithm (WOA) in order to minimize Average Travel Time (ATT). Experimental results show the superiority of WOA over other related algorithms specially with the case of large-scale benchmarks.

2020 International Conference on Promising Electronic Technologies (ICPET), 2020

Water plays a significant role in the economic development of countries. The agriculture sector i... more Water plays a significant role in the economic development of countries. The agriculture sector is the most water-consuming; this sector consumes 69% of the freshwater. However, farmers often use traditional irrigation systems to water their crops. These systems are ineffective and consume a IoT of time and effort, mainly when several fields are distributed in different geographical regions. Therefore, employing smart irrigation techniques will significantly overcome these problems. This paper proposes an intelligent irrigation framework based on Wireless Sensor Network (WSN) and Internet of Things (IoT) cloud services. The proposed system consists of three main components; the WSN, the control unit, and cloud services. Arduino Uno and XBee ZigBee modules are combined to gather sensors data and send them wirelessly to the control unit. YL-69 sensor is used to monitor the soil moisture. Raspberry Pi is utilized to gather data, process them, provide the proper decision, and transfer t...

In the software development process, the testing phase plays a vital role in assessing software q... more In the software development process, the testing phase plays a vital role in assessing software quality. Limited resources pose a challenge in achieving this purpose efficiently. Therefore, early stage procedures such as software fault prediction (SFP) are utilized to facilitate the testing process in an optimal way. SFP aims to predict fault-prone components early based on some software metrics (features). Machine learning (ML) techniques have proven superior performance in tackling this problem. However, there is no best classifier to handle all possible classification problems. Thus, building a reliable SFP model is still a research challenge. The purpose of this paper is to introduce an efficient classification framework to improve the performance of the SFP. For this purpose, an ensemble of multi-layer perceptron (MLP) deep learning algorithm boosted with synthetic minority oversampling technique (SMOTE) is proposed. The proposed model is benchmarked and assessed using sixteen ...

2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT)

2019 2nd International Conference on new Trends in Computing Sciences (ICTCS)

The non-uniform distribution of classes (imbalanced data) and the presence of irrelevant and/or r... more The non-uniform distribution of classes (imbalanced data) and the presence of irrelevant and/or redundant information are considered as challenging aspects encountered in most real-world domains. In this paper, we propose an efficient software fault prediction (SFP) model based on a wrapper feature selection method combined with Synthetic Minority Oversampling Technique (SMOTE) with the aim of maximizing the prediction accuracy of the learning model. A binary variant of recent optimization algorithm; Queuing Search Algorithm (QSA), is introduced as a search strategy in wrapper FS method. The performance of the proposed model is assessed on 14 real-world benchmarks from the PROMISE repository in terms of three evaluation measures; sensitivity, specificity, and area under the curve (AUC). Experimental results reveal a positive impact of the SMOTE technique in improving the prediction performing in a highly imbalanced data. Moreover, the binary QSA (BQSA) show a superior efficacy on 64.28% of datasets compared with other state-of-the-art algorithms in handling the problem of FS. The combination of BQSA and SMOTE achieved an acceptable AUC results (66.47-87.12%).

The Artificial Bee Colony (ABC) is a novel nature-inspired metaheuristic optimization algorithm t... more The Artificial Bee Colony (ABC) is a novel nature-inspired metaheuristic optimization algorithm that mimics the behavior of honey bees searching for food sources. The main drawback of ABC, similar to the most of metaheuristics, is the premature convergence (i.e., the earlier stuck into local optima). Recently, the structured population approach, in which the individuals are distributed into multiple sub-populations (called islands), has been widely exploited to maintain the required diversity during the search process and thus reducing the prematurity problem. In this paper, the island model, which is a common structured population approach, is incorporated with the ABC to introduce a parallel variant called (iABC). Besides, an experimental design approach is proposed to analyze the sensitivity of iABC to the parameters of the island model as well as the main specific parameters. The linear regression model and the Analysis of variance (ANOVA) are utilized to estimate the effect of ...

Applied Sciences

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortag... more Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ab...