Search Results - parallel distribution ((window algorithm) OR (((mining algorithm) OR (bees algorithm))))

Parallel execution of distributed SVM using MPI (CoDLib) by Salleh N.S.M., Suliman A., Ahmad A.R.

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Solving multi-objective dynamic vehicle routing problem with time windows using multi-objective algorithm by Khoo, Thau Soon

“…Our algorithm uses non-fitness evolutionary distributed parallelized adaptive large neighbourhood search (NEDPALNS). …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System by Abidin, Zulkifli Zainal

“…A number of benchmark function processes were conducted to assess the performance of proposed FOA (Fly Optimisation Algorithm).…”
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Development of appoinment scheduling agent using distributed constraint satisfaction (DisCS) by Mohd. Azmi, Nurulhuda Firdaus, Eri, Zeti Darleena, Ibrahim, Nuzulha Khilwani, Ahmad Rusli, Rozana Diana

“…The processing time of each job has to fit within a giv en time window and with n jobs available for processing. The goal of this research is to develop appointment scheduling agent in reservation environment with implementation of Distributed Constraint Satisfaction (DisCS). …”
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Random sampling method of large-scale graph data classification by Rashed Mustafa, Mohammad Sultan Mahmud, Mahir Shadid

“…Mining a large number of graphs becomes a challenging task because state-of-the-art methods are not scalable due to the memory limit. …”
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K Nearest Neighbor Joins And Mapreduce Process Enforcement For The Cluster Of Data Sets In Bigdata by Md Shah, Wahidah, Othman, Mohd Fairuz Iskandar, Hussian Hassan, Ali Abdul, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar

“…K Nearest Neighbor Joins (KNN join) are regarded as highly primitive and expensive operations in the data mining.The efficient use of KNN join has proven good results in finding the objects from two data sets prevailed in the huge databases.This has been achieved with the combination of K-Nearest Neighbor query and join operation to find the distinct objects from different data sets.MapReduce is a newly introduced program with the combination of Map Procedure method and Reduce Method widely used in BigData.MapReduce is enriched with parallel distributed algorithm to find the results on a cluster of data sets in BigData.In this paper,the combination of KNN join and MapReduce methods are utilized on the cluster of data sets in BigData for knowledge discovery.Exploring the pinpoint data from huge data sets stored in Big Data demands the distributed large scale data processing.The present research paper is focusing on generic steps for KNN joins exploration operations on MapReduce.The operations of KNN Join are targeted to perform the data partitioning and data pre-processing and necessary calculations.By utilizing the combination of KNN joins with MapReduce methods on BigData data sets will demonstrate a solution for complex computational analysis. …”
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Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system by Beg, Abul Hashem

“…Nowadays, in the grid community, distributed and clustering system, a lot of work has been focused on providing efficient and safe replication management services through designing of algorithms and systems. …”
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