Search Results - parallel computing ((search algorithm) OR (tree algorithm))*

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  1. 1
    Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches

    Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches by Liew, S.Y., Wong, E.S.K.

    Published 2009
    “…In the first algorithm, packet scheduling is formulated as a tree-searching problem. …”
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  2. 2
    CUDA implementation of fractal image compression

    CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

    Published 2023
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  3. 3
    An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title

    An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title by Khalaf, Emad Taha

    Published 2019
    “…Searches within a group can be done using a half-searching algorithm to improve the response time for data retrieval. …”
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  4. 4
    Algorithm optimization and low cost bit-serial architecture design for integer-pixel and sub-pixel motion estimation in H.264/AVC / Mohammad Reza Hosseiny Fatemi

    Algorithm optimization and low cost bit-serial architecture design for integer-pixel and sub-pixel motion estimation in H.264/AVC / Mohammad Reza Hosseiny Fatemi by Hosseiny Fatemi, Mohammad Reza

    Published 2012
    “…To address the computational complexity and memory bandwidth requirement problems of interpolate and search method in the SME of H.264/AVC, we introduce a low complexity algorithm and its hardware architecture for SME with quarter-pixel accuracy that is based on parabolic interpolation free algorithms. …”
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  5. 5
    Robust partitioning and indexing for iris biometric database based on local features

    Robust partitioning and indexing for iris biometric database based on local features by Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy

    Published 2018
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  6. 6
    Parallel Homologous Search With Hirschberg Algorithm: A Hybrid MPI-Pthreads Solution.

    Parallel Homologous Search With Hirschberg Algorithm: A Hybrid MPI-Pthreads Solution. by Abdul Rashid, Nur'Aini, Abdullah, Rosni, Hj. Talib, Abdullah Zawawi

    Published 2007
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  7. 7
    Parallel Quick-Skip Search Hybrid Algorithm For The Exact String Matching Problem.

    Parallel Quick-Skip Search Hybrid Algorithm For The Exact String Matching Problem. by Naser, Mustafa Abdulsahib

    Published 2010
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  8. 8
    An Experimental Study on Relationship between Performance and Energy Consumption of Serial and Parallel Text Searching Algorithm.

    An Experimental Study on Relationship between Performance and Energy Consumption of Serial and Parallel Text Searching Algorithm. by Isahak, Siti Zulaikha

    Published 2013
    “…How to reduce execution time in searching large amount of data? In this paper, text searching algorithm is using to study the relationship between performance of computer and amount of energy produced in serial and parallel text searching algorithm. …”
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  9. 9
    Parallel metaheuristic algorithm for route planning using CUDA

    Parallel metaheuristic algorithm for route planning using CUDA by Looi, Daniel Jun Jie

    Published 2025
    “…Area of Study: Massively Parallel Computing, Combinatorial Optimization Keywords: Parallel Metaheuristic Algorithm, Travelling Salesman Problem, CUDA, GPU, Genetic Algorithm…”
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  10. 10
    A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences

    A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences by M. Othman, Razib, Deris, Safaai, Md. IIlias, Rosli

    Published 2008
    “…Then, the parallel genetic algorithm is employed to perform batch retrieval and to accelerate the search in large search space of the Gene Ontology graph. …”
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  11. 11
    Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems

    Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems by Uvaraja, Vikneswary

    Published 2018
    “…Additionally, the MTS algorithm is also implemented in parallel computing to produce parallel MTS for generating comparable solutions in shorter computational times. …”
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  12. 12
    Speeding up index construction with GPU for DNA data sequences

    Speeding up index construction with GPU for DNA data sequences by Rahmaddiansyah, , Abdul Rashid, Nur’aini

    Published 2011
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  13. 13
    Parallel multiple tabu search for multiobjective Urban Transit Scheduling Problem

    Parallel multiple tabu search for multiobjective Urban Transit Scheduling Problem by Uvaraja, Vikneswary, Lai, Soon Lee, Abd Rahmin, Nor Aliza, Hsin, Vonn Seow

    Published 2020
    “…A set covering model was then adopted to minimize the number of buses and drivers simultaneously. A parallel tabu search algorithm was proposed to solve the problem by modifying the initialization process and incorporating intensification and diversification approaches to guide the search effectively from the different feasible domain in finding optimal solutions with lesser computational effort. …”
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  14. 14
    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

    Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function by Hammash, Nayif Mohammed

    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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  15. 15
    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network by Abduh Kaid, Monir Abdullah

    Published 2005
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  16. 16
    Local search approaches for patient scheduling problem in parallel operating theatre

    Local search approaches for patient scheduling problem in parallel operating theatre by Alimin, Nur Neesha

    Published 2020
    “…The objective of the study is to propose different types of local search which are local search(LS), improved local search(ILS) and mixed local search (MLS) for solving patient scheduling problem in parallel OT. …”
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  17. 17
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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  18. 18
    Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

    Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources by Abed, Munther Hameed, Mohd Nizam Mohmad, Kahar

    Published 2022
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  19. 19
    The development of semantic meta-database: an ontology based semantic integration of biological databases

    The development of semantic meta-database: an ontology based semantic integration of biological databases by Samsudin, Ruhaidah, Deris, Safaai, Othman, Muhammad Razib, Md. Illias, Rosli

    Published 2007
    “…The tool comprises two intelligent algorithms. The first algorithm combines parallel genetic algorithm with the split-and-merge algorithm. …”
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  20. 20
    Image classification using two dimensional wavelet coefficients with parallel computing

    Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

    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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