固执点好 发表于 2025-3-28 15:24:59
http://reply.papertrans.cn/29/2819/281837/281837_41.pngAntimicrobial 发表于 2025-3-28 20:29:28
K-means and Wordnet Based Feature Selection Combined with Extreme Learning Machines for Text Classifpurpose, 20-Newsgroups and DMOZ datasets have been used. The empirical results on these two benchmark datasets demonstrate the applicability, efficiency and effectiveness of our approach using ELM and ML-ELM as the classifiers over state-of-the-art classifiers.喧闹 发表于 2025-3-29 01:24:57
http://reply.papertrans.cn/29/2819/281837/281837_43.pngTIA742 发表于 2025-3-29 06:36:23
Storage Load Control Through Meta-Scheduler Using Predictive Analyticsynthetic and industry specific I/O intensive jobs have shown to have superior total completion time and total flow time compared to traditional approaches like FCFS and Backfilling. Proposed scheme prevented any down time when implemented with a live NetApp storage system.RAGE 发表于 2025-3-29 09:00:03
http://reply.papertrans.cn/29/2819/281837/281837_45.pngCANON 发表于 2025-3-29 14:41:23
HGASA: An Efficient Hybrid Technique for Optimizing Data Access in Dynamic Data Grid the performance of the grid. GridSim simulator is used for evaluating the performance of the proposed algorithm. The results show that the proposed algorithm, HGASA, outperforms Genetic Algorithms (GA) by 9 % and Simulated Annealing (SA) by 21 % and Ant Colony Optimization (ACO) by 50 %.许可 发表于 2025-3-29 16:57:55
http://reply.papertrans.cn/29/2819/281837/281837_47.png随意 发表于 2025-3-29 23:29:04
Antonino Pennisi,Alessandra Falzoneynthetic and industry specific I/O intensive jobs have shown to have superior total completion time and total flow time compared to traditional approaches like FCFS and Backfilling. Proposed scheme prevented any down time when implemented with a live NetApp storage system.adequate-intake 发表于 2025-3-30 01:44:36
http://reply.papertrans.cn/29/2819/281837/281837_49.png黑豹 发表于 2025-3-30 07:01:17
On the Biomechanics of External Fixation the performance of the grid. GridSim simulator is used for evaluating the performance of the proposed algorithm. The results show that the proposed algorithm, HGASA, outperforms Genetic Algorithms (GA) by 9 % and Simulated Annealing (SA) by 21 % and Ant Colony Optimization (ACO) by 50 %.