Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collectio...Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.展开更多
The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy...The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).展开更多
The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an e...The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.展开更多
In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According t...In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.展开更多
Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange ...Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.展开更多
Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies use...Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.展开更多
Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it ...Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.展开更多
In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features ...In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.展开更多
Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is ...Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).展开更多
In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data tran...In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.展开更多
Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms...Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.展开更多
Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainab...Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar power.To achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base models.These regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)regressor.In addition,data cleaning and data preprocessing were performed to the data.The dataset used in this study includes 4 features and 50530 instances.To accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM network.Five evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.展开更多
Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the seco...Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.展开更多
Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solution...Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.展开更多
Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especia...Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especially those in the agricultural sector,rely on rain forecasts.Forecasting rainfall is challenging because of the changing nature of the weather.The area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting model.To estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),respectively.Moreover,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed approach.On the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.展开更多
Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task cl...Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.展开更多
In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regres...In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria.Long-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two years.Five computational algorithms were considered and analyzed for the suitability of estimation.Further two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach.The accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA test.Among the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good performances.However,the newly proposed ensemble algorithms exhibited even better performance.The proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than 0.999.This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms.It is concluded that the proposed algorithms are far superior to the commonly adopted ones.展开更多
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiv...Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.展开更多
Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area wit...Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.展开更多
As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infect...As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach.展开更多
文摘Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.
文摘The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.
文摘In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.
文摘Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.
文摘Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R323)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.
文摘In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.
文摘Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).
文摘In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.
文摘Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.
文摘Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar power.To achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base models.These regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)regressor.In addition,data cleaning and data preprocessing were performed to the data.The dataset used in this study includes 4 features and 50530 instances.To accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM network.Five evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.
文摘Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.
文摘Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.
文摘Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especially those in the agricultural sector,rely on rain forecasts.Forecasting rainfall is challenging because of the changing nature of the weather.The area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting model.To estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),respectively.Moreover,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed approach.On the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.
基金Funding for this study is received from Taif University Researchers Supporting Project No.(Project No.TURSP-2020/150)Taif University,Taif,Saudi Arabia。
文摘Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.
文摘In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria.Long-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two years.Five computational algorithms were considered and analyzed for the suitability of estimation.Further two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach.The accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA test.Among the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good performances.However,the newly proposed ensemble algorithms exhibited even better performance.The proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than 0.999.This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms.It is concluded that the proposed algorithms are far superior to the commonly adopted ones.
文摘Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance.Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas.Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas.Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s technology.The accuracy of the forecast is mostly determined by the model used.The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna.Support Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic models.The Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the datasets.The suggested model is compared to models based on five variables and to the average ensemble model.The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for gain.This is superior to other models and can accurately predict antenna bandwidth and gain.
文摘Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.
文摘As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach.
