Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcared...Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.展开更多
The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different clients.To attain the benefits of collaborative resource sharing in Grid computing,a ...The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different clients.To attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is essential.Therefore,detection of an appropriate resource for the presented task is a difficult task.Several scientists have presented algorithms for mapping tasks to the resource.Few of them focus on fault tolerance,user fulfillment,and load balancing.With 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 platform.The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer.It also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate solution.The 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.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP2/42/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R136)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.
文摘The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different clients.To attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is essential.Therefore,detection of an appropriate resource for the presented task is a difficult task.Several scientists have presented algorithms for mapping tasks to the resource.Few of them focus on fault tolerance,user fulfillment,and load balancing.With 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 platform.The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer.It also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate solution.The 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.
