Search Results - (( data distribution using algorithm ) OR ( data distribution drops algorithm ))
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1
RED, GRED, AGRED congestion control algorithms in heterogeneous traffic types
Published 2013“…The performance metric used to show the analysis of these algorithms with mentioned distributions is Packet Loss (Pl).Results of simulation show that each algorithm performed a fair dropping of data packets without considering of their types.…”
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2
Red, gred, agred congestion control algorithms in heterogeneous traffic types
Published 2013“…The performance metric used to show the analysis of these algorithms with mentioned distributions is Packet Loss (Pl). …”
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3
Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Anderson-Darling (AD) and Goodness of Fit test is used to identify the best fitted distribution model to the real data. …”
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4
Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Anderson-Darling (AD) and Goodness of Fit test is used to identify the best fitted distribution model to the real data. …”
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Thesis -
5
OPTIMAL CABLE SELECTION IN RADIAL DISTRIBUTION NETWORKS CONSIDERING PLANNER'S DEFINED VOLTAGE DROP LIMIT
Published 2002“…New computer algorithms are provided to determine the optimal cable size selection considering radial distribution networks and planner of power distribution defined allowable voltage drop all over the networks. …”
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6
TDMA scheduling analysis of energy consumption for iot wireless sensor network
Published 2019“…The algorithm use time division multiplexing algorithm together with hello message to allocate the neighbors and their energy level to find the best way to communicate and reduce packet drop, less signal interference and increase throughput. …”
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Thesis -
7
An enhanced cluster head selection algorithm for routing in mobile AD-HOC network
Published 2017“…The characteristics of MANET have led to many difficulties in partitioning the main networks to a cluster, selecting a node to be the cluster head, and choosing the most suitable route for sending data from a sender to a receiver. This thesis proposes an algorithm for increasing the stability of the cluster by selecting the most stable cluster head, maintaining the cluster structure with minimum maintenance overhead, and finding the best performance routing algorithm for use over MANET. …”
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8
Energy balancing mechanisms for decentralized routing protocols in wireless sensor networks
Published 2012“…Finally, we propose Self-Decision Route Selection scheme which is an improvement of the Hop-based Spanning Tree (HST) algorithm that is used in some routing protocols such as AODV and DSR. …”
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9
Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam
Published 2023“…The monthly storage capacity fails in 2077, substantially earlier than the other three algorithms in Scenario 2. The LFWOA was used in this study to improve the efficacy of the algorithms by delivering a more accurate monthly storage capacity and reservoir risk assessment for Scenario 2 of RCP 2.6. …”
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Final Year Project / Dissertation / Thesis -
10
Data discovery algorithm for scientific data grid environment
Published 2005“…By using this model, we study various discovery algorithms for locating data sets in a data grid system. …”
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11
Binary Vote Assignment on Cloud Quorum Algorithm for Fragmented MyGRANTS Database Replication
Published 2015“…We address how to build reliable systems by using the proposed BVACQ algorithm for distributed database fragmentation by using Malaysian Greater Research Network (MyGRANTS) data in our case study. …”
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12
Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. …”
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13
Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification
Published 2018“…It still takes a long time to compute the topic distribution of the data. There are still room for improvement in the time taken for the algorithm to complete the topic distribution. …”
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14
Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification
Published 2018“…It still takes a long time to compute the topic distribution of the data. There are still room for improvement in the time taken for the algorithm to complete the topic distribution. …”
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15
Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data
Published 2013“…Focus is on modelling the extreme data based on block maxima approach using Gumbel distribution. …”
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16
Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad
Published 2017“…To avoid such overheads many techniques have been used, however in this thesis stream-based data processing model is used in which data is processed in the form of continuous instances of data items. …”
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17
Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour
Published 2022“…Meanwhile, k-NN algorithm is used to classify the experimental data. …”
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Proceeding Paper -
18
Ancestral dynamic voting algorithm for mutual exclusion in partitioned distributed systems
Published 2013“…Data replication is a known redundancy used in fault-tolerant distributed system. …”
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19
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…All the data for 18-bus system test are taken from previous work, and all the data for 49-bus system test are taken from an existing Iraqi distribution network. …”
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20
Improved genetic algorithm for scheduling divisible data grid application
Published 2007“…Data Grid technology promises geographically distributed scientists to access and share physically distributed resources such as computing resources, networks, storages, and most importantly data collections for large scale data intensive problems. …”
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