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Titlebook: Distributed Computing and Internet Technology; 12th International C Nikolaj Bj?rner,Sanjiva Prasad,Laxmi Parida Conference proceedings 2016

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樓主: antibody
41#
發(fā)表于 2025-3-28 15:24:59 | 只看該作者
42#
發(fā)表于 2025-3-28 20:29:28 | 只看該作者
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.
43#
發(fā)表于 2025-3-29 01:24:57 | 只看該作者
44#
發(fā)表于 2025-3-29 06:36:23 | 只看該作者
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.
45#
發(fā)表于 2025-3-29 09:00:03 | 只看該作者
46#
發(fā)表于 2025-3-29 14:41:23 | 只看該作者
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?%.
47#
發(fā)表于 2025-3-29 16:57:55 | 只看該作者
48#
發(fā)表于 2025-3-29 23:29:04 | 只看該作者
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.
49#
發(fā)表于 2025-3-30 01:44:36 | 只看該作者
50#
發(fā)表于 2025-3-30 07:01:17 | 只看該作者
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?%.
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