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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** 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 *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** 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.

关键词： metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

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Computers, Materials & Continua 2022年第7期72卷 181-195页

作者： Mesfer Al Duhayyim Majdy M.Eltahir Imène Issaoui Fahd N.Al-Wesabi Anwer Mustafa Hilal Fuad Ali Mohammed Al-Yarimi Manar Ahmed Hamza Abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityAl-Bukairiyah52571Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different *** attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is ***,detection of an appropriate resource for the presented task is a difficult *** scientists have presented algorithms for mapping tasks to the *** of them focus on fault tolerance,user fulfillment,and load *** this motivation,this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm(DHOA),called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid *** IGSS-DHOA technique is mainly based on the hunting nature of humans toward *** also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate *** simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

关键词： Grid services grid scheduling resources makespan np hard problem metaheuristics

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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3087-3102页

作者： Manal Abdullah Alohali Muna Elsadig Fahd N.Al-Wesabi Mesfer Al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share *** the IoT model is prone to security concerns as several attackers try to hit the network and make it *** such scenarios,security concern is the most *** models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security *** malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim *** article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT *** the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT *** SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection ***,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC *** comparison study reported the enhancement of the SCADL-RWDC technique over other models.

关键词： Security IoT network ransomware attack deep learning metaheuristics

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Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

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Computer Systems Science & Engineering 2023年第5期45卷 1641-1654页

作者： K.Karthikeyan Liyakathunisa Eman Aljohani Thavavel Vaiyapuri Department of Computer Applications Anna UniversityRegional CampusMaduraiMaduraiIndia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Cloud data centers consume high volume of energy for processing and switching the servers among different *** Machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and *** the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the *** VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the *** this view,the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization(IMFP-VMMO)model in cloud *** major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure *** accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM *** the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy *** performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed *** comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

关键词： Cloud computing energy efficiency virtual machine migration failure prediction energy optimization metaheuristics

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Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第3期35卷 2925-2939页

作者： H.Manikandan D.Narasimhan Department of CSE Srinivasa Ramanujan CentreSASTRA Deemed UniversityThanjavurTamil Nadu 613401India Department of Mathematics Srinivasa Ramanujan CentreSASTRA Deemed UniversityThanjavurTamil Nadu 613401India

Wireless sensor networks(WSNs)encompass a massive set of sensor nodes,which are self-conﬁgurable,inexpensive,and *** sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt *** reducing energy consumption and lengthen lifetime of WSN,multihop routing protocols can be *** study develops an improved rat swarm optimization based energy aware multi-hop routing(IRSO-EAMHR)protocol for *** important intention of the IRSO-EAMHR method is for determining optimal routes to base station(BS)in the clustered ***,a weighted clustering process is performed to group the nodes into clusters and select cluster heads(CHs).Next,the IRSO-EAMHR approach derives aﬁtness function containing three input parameters(residual energy,dis-tance,and node degree)for routing *** IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO *** experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various *** simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

关键词： Wireless sensor networks multihop routing clustering metaheuristics lifetime rat swarm optimizer

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Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

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Intelligent Automation & Soft Computing 2023年第6期36卷 3621-3636页

作者： Ierin Babu R.MathuSoothana S.Kumar Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationKanyakumari629180India Department of Information Technology Noorul Islam Centre for Higher EducationKanyakumari629180India

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive *** education institutions have started to utilize analytical tools to improve students’grades and *** of students’performance is a difficult process owing to the massive quantity of educational ***,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data *** the same time,in EDM,the feature selection process becomes necessary in creation of feature *** the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection *** this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’*** addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of ***,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high ***,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational *** MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different *** to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

关键词： Students’performance analysis educational data mining feature selection deep learning metaheuristics outlier detection

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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Computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** 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 *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** 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 *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer Systems Science & Engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Multiple learning neural network algorithm for parameter estimation of proton exchange membrane fuel cell models

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Green Energy and Intelligent Transportation 2023年第1期2卷 1-15页

作者： Yiying Zhang Chao Huang Hailong Huang Jingda Wu School of Electrical and Information Engineering Jiangsu UniversityChina Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore

Extracting the unknown parameters of proton exchange membrane fuel cell(PEMFC)models accurately is vital to design,control,and simulate the actual *** order to extract the unknown parameters of PEMFC models precisely,this work presents an improved version of neural network algorithm(NNA),namely the multiple learning neural network algorithm(MLNNA).In MLNNA,six learning strategies are designed based on the created local elite archive and global elite archive to balance exploration and exploitation of *** evaluate the performance of MLNNA,MLNNA is first employed to solve the well-known CEC 2015 test *** results demonstrate that MLNNA outperforms NNA on most test ***,MLNNA is used to extract the parameters of two PEMFC models including the BCS 500 W PEMFC model and the NedStack SP6 PEMFC *** results support the superiority of MLNNA in the parameter estimation of PEMFC models by comparing it with 10 powerful optimization algorithms.

关键词： Neural network algorithm Parameter extraction Proton exchange membrane fuel cell metaheuristics

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