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 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
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
Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The go...Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.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
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
Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining ...Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining popularity as a way to improve solutions in a range of fields.Machine learning approaches are currently a big part of current research,and they’re likely to be huge in the future.The model utilized determines the accuracy of the prediction in large part.The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain.The basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)algorithm.To choose the best features from the dataset,the binary(bDTO)algorithm is exploited.The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically.In addition,two other ensembles are incorporated in the conducted experiments for comparison.These ensembles are average ensemble and K-nearest neighbors(KNN)-based ensemble.The comparison is performed in terms of eleven evaluation criteria.The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.展开更多
The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in ma...The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.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
Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the ...Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the relationship between pore throat structure and crude oil mobility characteristics of full particle sequence reservoirs in the Lower Permian Fengcheng Formation of Mahu Sag,Junggar Basin,are revealed.(1)With the decrease of reservoir particle size,the volume of pores connected by large throats and the volume of large pores show a decreasing trend,and the distribution and peak ranges of throat and pore radius shift to smaller size in an orderly manner.The upper limits of throat radius,porosity and permeability of unconventional reservoirs in Fengcheng Formation are approximately 0.7μm,8%and 0.1×10^(−3)μm^(2),respectively.(2)As the reservoir particle size decreases,the distribution and peak ranges of pores hosting retained oil and movable oil are shifted to a smaller size in an orderly manner.With the increase of driving pressure,the amount of retained and movable oil of the larger particle reservoir samples shows a more obvious trend of decreasing and increasing,respectively.(3)With the increase of throat radius,the driving pressure of reservoir with different particle levels presents three stages,namely rapid decrease,slow decrease and stabilization.The oil driving pressures of various reservoirs and the differences of them decrease with the increase of temperature and obviously decrease with the increase of throat radius.According to the above experimental analysis,it is concluded that the deep shale oil of Fengcheng Formation in Mahu Sag has great potential for production under geological conditions.展开更多
Jet pumps often suffer from efficiency losses due to the intense mixing of power and suction fluids,which leads to significant kinetic energy dissipation.Enhancing the efficiency of such pumps requires careful optimiz...Jet pumps often suffer from efficiency losses due to the intense mixing of power and suction fluids,which leads to significant kinetic energy dissipation.Enhancing the efficiency of such pumps requires careful optimization of their structural parameters.In this study,a computational fluid dynamics(CFD)model of a hydraulic jet sand-flushing pump is developed to investigate the effects of throat-to-nozzle distance,area ratio,and throat length on the pump’s sand-carrying performance.An orthogonal experimental design is employed to optimize the structural parameters,while the influence of sand characteristics on pumping performance is systematically evaluated.Complementary indoor experiments are used to validate the numerical results,yielding an optimized configuration with a throat-to-nozzle cross-sectional area ratio of 4,a throat length five times the throat diameter,and a throat-to-nozzle distance equal to the nozzle diameter.Under a power fluid flow rate of 1.7 m3/h with this area ratio,the sand-carrying efficiency reaches its peak,achieving a sand transport rate of 290 g/min(6.7 L/h).Comparison of CFD predictions with experimental data across different area ratios demonstrates excellent agreement,with an average relative error of 2.44%and an average absolute error of 3.56%,confirming the reliability of the simulation approach.展开更多
Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant...Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant lithofacies distribution,particularly the favorable areas for high-quality reservoirs,remains to be determined.In this paper,the Huoshiling Formation in the Dehui faulted depression,Songliao Basin is taken as an example to carry out the multi-scale joint characterization of its pore throat structure,establish a reservoir evaluation standard that considers both the gas content and seepage capacity,and perform reservoir evaluation and play fairway mapping under facies control.The results show that the storage space types of the gas-bearing reservoirs in the faulted depression can be ascribed into three categories and six subcategories according to the pore throat and pore characteristics.In terms of pore sizes,volcaniclastic lava rank the first,followed by volcaniclastic rocks,sedimentary volcaniclastic rocks and volcanic lava.The comprehensive evaluation parameter(Φ·K·Sg,whereΦis porosity,K permeability,and Sggas saturation)of high-quality reservoirs are all greater than 0.1.The volcanic reservoirs in the Stage-III strata are the highest in quality and largest in area of play fairways.The thermal debris flow sub-facies developed at Stage III are mainly seen along the western strike-slip fault zone in the Debei sub-sag and the southwest Nong'an tectonic belt,while those developed at Stage I are distributed along the central and eastern fault zones in the southeastern Baojia sub-sag.The favorable layer evaluation and favorable area delineation under facies control will be of certain reference significance for subsequent exploration and development of volcanic gas reservoirs.展开更多
The impact of cross-sectional topographic variability on the kinetic properties of granular flows has been underexplored,which hinders the understanding of the kinematics of rock avalanches.In this study,the throat co...The impact of cross-sectional topographic variability on the kinetic properties of granular flows has been underexplored,which hinders the understanding of the kinematics of rock avalanches.In this study,the throat contraction index(T)is introduced to quantify variations in throat topography,and 96 numerical simulation experiments with varying T and slope angles(δ)are conducted.The findings indicate that granular flows experience transient obstructions when traversing throat topographies,primarily due to the periodic formation and breaking of the arch structure.Observations suggest that the acceleration of velocity in the tails of granular flows is restrained by the throat region,potentially altering the dynamics of related geohazards.In this study,the impact of throat topography is quantitatively assessed,demonstrating a reduction in peak flowrates of granular materials by 20%-80% and extending the flowduration up to six times.The present study proposes the throat-induced hazard index(Φ)to evaluate the influenceof throat topography on the risk of rockslides and avalanches characterized by granular flows,which may provide insights for the design of mitigation structures in topographic regions.展开更多
Tight oil is the most viable target for unconventional oil and gas exploration, but the complexity of micro-/nanopore throat systems significantly affects the oil content of reservoirs. To investigate the causes of he...Tight oil is the most viable target for unconventional oil and gas exploration, but the complexity of micro-/nanopore throat systems significantly affects the oil content of reservoirs. To investigate the causes of heterogeneity in oil-bearing reservoirs, a high-pressure mercury injection experiment combined with fractal theory was conducted to analyze the micro pore throat structure characteristics of the tight sandstone of Chang 7 Member reservoirs in the Ordos Basin. The factors controlling the variations in oil content among tight sandstone samples were identified based on mineral composition characteristics. The results indicate that the pore throat radius distribution is mainly unimodal an bimodal. In oil-bearing samples, the pore throat distributions align well with the corresponding permeability contribution curves, while in oil-free samples, there is a clear deviation from these curves. Mesopore throats exert the greatest influence on seepage capacity. Differences in fractal characteristics are primarily reflected in D1 values, with oil-free samples exhibiting D1 values close to 3, indicating an extremely nonuniform pore throat structure at this scale. The content of quartz, plagioclase, and chlorite is significantly higher in oil-bearing samples than in oil-free samples, whereas calcite content is lower in oil-bearing samples. There is a positive correlation between the contents of quartz, plagioclase, and chlorite with D1;their increased presence contributes to a more favorable pore throat structure.Conversely, the calcite contents show an inverse relationship with D1. Cementation increases the complexity of pore throat structures, while multiple diagenetic processes simultaneously control these characteristics, leading to variations in oil content.展开更多
The Lucaogou Formation in Jimsar has a significant development potential due to its massive shale oil resources.Nevertheless,the complex and heterogeneous lithology,coupled with unclear flow mechanisms,poses a challen...The Lucaogou Formation in Jimsar has a significant development potential due to its massive shale oil resources.Nevertheless,the complex and heterogeneous lithology,coupled with unclear flow mechanisms,poses a challenge in effectively predicting its development potential.Therefore,it is crucial to clarify the flow characteristics of shale oil and its controlling factors.In this study,we used a flow simulation experiment to investigate the flow characteristics of different samples under various temperatures and confining stresses and quantitatively evaluated flow characteristics using threshold pressure gradient and total loss of flow rate.Additionally,by combining scanning electron microscopy and mercury intrusion capillary pressure techniques for pore structure characterization,and the relationship between microscopic pore structure and flow parameters was discussed.The findings indicate that rock composition and pore throat structure collaboratively control shale oil flow.Mesopores and macropores primarily develop between dolomite or albite,leading to well-developed pore throat structure with larger average throat radius,lower displacement pressure,and better reservoir quality,enhancing shale oil flowability.Dolomitic siltstone often exhibits these characteristics,making it a favorable lithology for shale oil flow.This study reveals the flow mechanism of shale oil under the action of reservoir physical properties,material compositions,temperatures and confining stresses,summarizes the geological characteristics of advantageous reservoirs.It provides theoretical support for layer selection and efficient development of shale oil reservoirs in the Lucaogou Formation of the Jimsar.展开更多
KHOOMEI is a traditional throat-singing art created by the Mongolian ethnic group and is regarded as one of the oldest forms of this low rumbling vocal technique in the world.In 2006,Khoomei was included in China’s f...KHOOMEI is a traditional throat-singing art created by the Mongolian ethnic group and is regarded as one of the oldest forms of this low rumbling vocal technique in the world.In 2006,Khoomei was included in China’s first national list of intangible cultural heritage items,followed in 2009 by its inclusion on UNESCO’s Representative List of the Intangible Cultural Heritage of Humanity.展开更多
One of the most common kinds of cancer is breast cancer.The early detection of it may help lower its overall rates of mortality.In this paper,we robustly propose a novel approach for detecting and classifying breast c...One of the most common kinds of cancer is breast cancer.The early detection of it may help lower its overall rates of mortality.In this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images.The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest.In addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling ratios.On the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are employed.The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy.The selected features are used to train a neural network to finally classify the thermal images of breast cancer.To achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm.Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature.Moreover,several experiments were conducted to compare the performance of the proposed approach with the other approaches.The results of these experiments emphasized the superiority of the proposed approach.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
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
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
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.
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 ProjectNumber (PNURSP2022R308), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
文摘Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.
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Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance.The bandwidth restriction associated with small antennas can be solved using metamaterial antennas.Machine learning is gaining popularity as a way to improve solutions in a range of fields.Machine learning approaches are currently a big part of current research,and they’re likely to be huge in the future.The model utilized determines the accuracy of the prediction in large part.The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain.The basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)algorithm.To choose the best features from the dataset,the binary(bDTO)algorithm is exploited.The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically.In addition,two other ensembles are incorporated in the conducted experiments for comparison.These ensembles are average ensemble and K-nearest neighbors(KNN)-based ensemble.The comparison is performed in terms of eleven evaluation criteria.The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.
基金funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,Grant No (43-PRFA-P-52).
文摘The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.
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%.
基金Supported by Leading Talent Program of Autonomous Region(2022TSYCLJ0070)PetroChina Prospective and Basic Technological Project(2021DJ0108)Natural Science Foundation for Outstanding Young People in Shandong Province(ZR2022YQ30).
文摘Based on the experimental results of casting thin section,low temperature nitrogen adsorption,high pressure mercury injection,nuclear magnetic resonance T2 spectrum,contact angle and oil-water interfacial tension,the relationship between pore throat structure and crude oil mobility characteristics of full particle sequence reservoirs in the Lower Permian Fengcheng Formation of Mahu Sag,Junggar Basin,are revealed.(1)With the decrease of reservoir particle size,the volume of pores connected by large throats and the volume of large pores show a decreasing trend,and the distribution and peak ranges of throat and pore radius shift to smaller size in an orderly manner.The upper limits of throat radius,porosity and permeability of unconventional reservoirs in Fengcheng Formation are approximately 0.7μm,8%and 0.1×10^(−3)μm^(2),respectively.(2)As the reservoir particle size decreases,the distribution and peak ranges of pores hosting retained oil and movable oil are shifted to a smaller size in an orderly manner.With the increase of driving pressure,the amount of retained and movable oil of the larger particle reservoir samples shows a more obvious trend of decreasing and increasing,respectively.(3)With the increase of throat radius,the driving pressure of reservoir with different particle levels presents three stages,namely rapid decrease,slow decrease and stabilization.The oil driving pressures of various reservoirs and the differences of them decrease with the increase of temperature and obviously decrease with the increase of throat radius.According to the above experimental analysis,it is concluded that the deep shale oil of Fengcheng Formation in Mahu Sag has great potential for production under geological conditions.
基金funded by China Oilfield Services Limited,grant number YXB24YF003.
文摘Jet pumps often suffer from efficiency losses due to the intense mixing of power and suction fluids,which leads to significant kinetic energy dissipation.Enhancing the efficiency of such pumps requires careful optimization of their structural parameters.In this study,a computational fluid dynamics(CFD)model of a hydraulic jet sand-flushing pump is developed to investigate the effects of throat-to-nozzle distance,area ratio,and throat length on the pump’s sand-carrying performance.An orthogonal experimental design is employed to optimize the structural parameters,while the influence of sand characteristics on pumping performance is systematically evaluated.Complementary indoor experiments are used to validate the numerical results,yielding an optimized configuration with a throat-to-nozzle cross-sectional area ratio of 4,a throat length five times the throat diameter,and a throat-to-nozzle distance equal to the nozzle diameter.Under a power fluid flow rate of 1.7 m3/h with this area ratio,the sand-carrying efficiency reaches its peak,achieving a sand transport rate of 290 g/min(6.7 L/h).Comparison of CFD predictions with experimental data across different area ratios demonstrates excellent agreement,with an average relative error of 2.44%and an average absolute error of 3.56%,confirming the reliability of the simulation approach.
基金funded by the National Natural Science Foundation of China(No.42172145)Natural Science Foundation of Heilongjiang Provincial(No.LH2022D014)。
文摘Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant lithofacies distribution,particularly the favorable areas for high-quality reservoirs,remains to be determined.In this paper,the Huoshiling Formation in the Dehui faulted depression,Songliao Basin is taken as an example to carry out the multi-scale joint characterization of its pore throat structure,establish a reservoir evaluation standard that considers both the gas content and seepage capacity,and perform reservoir evaluation and play fairway mapping under facies control.The results show that the storage space types of the gas-bearing reservoirs in the faulted depression can be ascribed into three categories and six subcategories according to the pore throat and pore characteristics.In terms of pore sizes,volcaniclastic lava rank the first,followed by volcaniclastic rocks,sedimentary volcaniclastic rocks and volcanic lava.The comprehensive evaluation parameter(Φ·K·Sg,whereΦis porosity,K permeability,and Sggas saturation)of high-quality reservoirs are all greater than 0.1.The volcanic reservoirs in the Stage-III strata are the highest in quality and largest in area of play fairways.The thermal debris flow sub-facies developed at Stage III are mainly seen along the western strike-slip fault zone in the Debei sub-sag and the southwest Nong'an tectonic belt,while those developed at Stage I are distributed along the central and eastern fault zones in the southeastern Baojia sub-sag.The favorable layer evaluation and favorable area delineation under facies control will be of certain reference significance for subsequent exploration and development of volcanic gas reservoirs.
基金supported by the National Natural Science Foundation of China(Grant Nos.42307194 and 42120104002)the Young Elite Scientists Sponsorship Program by CAST(Grant No.2024QNRC001).
文摘The impact of cross-sectional topographic variability on the kinetic properties of granular flows has been underexplored,which hinders the understanding of the kinematics of rock avalanches.In this study,the throat contraction index(T)is introduced to quantify variations in throat topography,and 96 numerical simulation experiments with varying T and slope angles(δ)are conducted.The findings indicate that granular flows experience transient obstructions when traversing throat topographies,primarily due to the periodic formation and breaking of the arch structure.Observations suggest that the acceleration of velocity in the tails of granular flows is restrained by the throat region,potentially altering the dynamics of related geohazards.In this study,the impact of throat topography is quantitatively assessed,demonstrating a reduction in peak flowrates of granular materials by 20%-80% and extending the flowduration up to six times.The present study proposes the throat-induced hazard index(Φ)to evaluate the influenceof throat topography on the risk of rockslides and avalanches characterized by granular flows,which may provide insights for the design of mitigation structures in topographic regions.
基金supported by the National Natural Science Foundation of China (Grant No. 42002139)the Basic Prospective Project of SINOPEC (Grant No. P23240-3)。
文摘Tight oil is the most viable target for unconventional oil and gas exploration, but the complexity of micro-/nanopore throat systems significantly affects the oil content of reservoirs. To investigate the causes of heterogeneity in oil-bearing reservoirs, a high-pressure mercury injection experiment combined with fractal theory was conducted to analyze the micro pore throat structure characteristics of the tight sandstone of Chang 7 Member reservoirs in the Ordos Basin. The factors controlling the variations in oil content among tight sandstone samples were identified based on mineral composition characteristics. The results indicate that the pore throat radius distribution is mainly unimodal an bimodal. In oil-bearing samples, the pore throat distributions align well with the corresponding permeability contribution curves, while in oil-free samples, there is a clear deviation from these curves. Mesopore throats exert the greatest influence on seepage capacity. Differences in fractal characteristics are primarily reflected in D1 values, with oil-free samples exhibiting D1 values close to 3, indicating an extremely nonuniform pore throat structure at this scale. The content of quartz, plagioclase, and chlorite is significantly higher in oil-bearing samples than in oil-free samples, whereas calcite content is lower in oil-bearing samples. There is a positive correlation between the contents of quartz, plagioclase, and chlorite with D1;their increased presence contributes to a more favorable pore throat structure.Conversely, the calcite contents show an inverse relationship with D1. Cementation increases the complexity of pore throat structures, while multiple diagenetic processes simultaneously control these characteristics, leading to variations in oil content.
基金financially supported by the National Natural Science Foundation of China(No.41972123)the Support Plan for Outstanding Youth Innovation Team in Shandong Higher Education Institutions(No.2022KJ060)。
文摘The Lucaogou Formation in Jimsar has a significant development potential due to its massive shale oil resources.Nevertheless,the complex and heterogeneous lithology,coupled with unclear flow mechanisms,poses a challenge in effectively predicting its development potential.Therefore,it is crucial to clarify the flow characteristics of shale oil and its controlling factors.In this study,we used a flow simulation experiment to investigate the flow characteristics of different samples under various temperatures and confining stresses and quantitatively evaluated flow characteristics using threshold pressure gradient and total loss of flow rate.Additionally,by combining scanning electron microscopy and mercury intrusion capillary pressure techniques for pore structure characterization,and the relationship between microscopic pore structure and flow parameters was discussed.The findings indicate that rock composition and pore throat structure collaboratively control shale oil flow.Mesopores and macropores primarily develop between dolomite or albite,leading to well-developed pore throat structure with larger average throat radius,lower displacement pressure,and better reservoir quality,enhancing shale oil flowability.Dolomitic siltstone often exhibits these characteristics,making it a favorable lithology for shale oil flow.This study reveals the flow mechanism of shale oil under the action of reservoir physical properties,material compositions,temperatures and confining stresses,summarizes the geological characteristics of advantageous reservoirs.It provides theoretical support for layer selection and efficient development of shale oil reservoirs in the Lucaogou Formation of the Jimsar.
文摘KHOOMEI is a traditional throat-singing art created by the Mongolian ethnic group and is regarded as one of the oldest forms of this low rumbling vocal technique in the world.In 2006,Khoomei was included in China’s first national list of intangible cultural heritage items,followed in 2009 by its inclusion on UNESCO’s Representative List of the Intangible Cultural Heritage of Humanity.
文摘One of the most common kinds of cancer is breast cancer.The early detection of it may help lower its overall rates of mortality.In this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images.The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest.In addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling ratios.On the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are employed.The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy.The selected features are used to train a neural network to finally classify the thermal images of breast cancer.To achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm.Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature.Moreover,several experiments were conducted to compare the performance of the proposed approach with the other approaches.The results of these experiments emphasized the superiority of the proposed approach.
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.
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.
