Search Results - parallel computing ((best algorithm) OR (((bee algorithm) OR (tree algorithm))))*
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Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function
Published 2012“…This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. …”
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Multi-criteria divisible load scheduling in binary tree network
Published 2016“…In the real applications, the processors may cheat the algorithm, which means, the processors might not report their true computation or communication rates. …”
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Speeding up index construction with GPU for DNA data sequences
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Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network
Published 2005“…The efficient combination of simulated annealing algorithm with the best heuristic algorithms gave much better result in a very minimal time. …”
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Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches
Published 2009“…In the first algorithm, packet scheduling is formulated as a tree-searching problem. …”
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Image classification using two dimensional wavelet coefficients with parallel computing
Published 2020“…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm
Published 2024“…Through an extensive computational experiment involving a well-known benchmark suite, the ABC algorithm demonstrated remarkable performance, consistently outperforming several popular metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Memetic Algorithms, and Whale Optimization Algorithm in 75% of the problems. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA
Published 2018“…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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Parallel batch self-organizing map on graphics processing unit using CUDA
Published 2018“…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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Accelerating DNA sequence alignment based on smith waterman algorithm using recursive variable expansion / Muhamad Faiz Ismail
Published 2014“…Reconfigurable computing, in which general purpose processor (GPP) is increasingly used for high performance computing where massive fine-grain parallelism can be exploited. …”
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A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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A parallel-model speech emotion recognition network based on feature clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance.
Published 2012“…Hardware implementations with target FPGA for GF (p) Montgomery ECC are also presented. The best performance level was achieved when parallelizing Montgomery ECC computations to eight parallel multipliers (PM) using homogeneous coordinates. …”
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Parallelizing GF (p) montgomery elliptic curve crypto-system operations to improve security and performance.
Published 2013“…Hardware implementations with target FPGA for GF (p) Montgomery ECC are also presented. The best performance level was achieved when parallelizing Montgomery ECC computations to eight parallel multipliers (PM) using homogeneous coordinates. …”
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