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  1. 1
    Topics in graph algorithms

    Topics in graph algorithms by Abu-Khzam, Faisal Nabih

    Published 2003
    “…This is achieved by implementing some algorithms for the vertex cover problem, and conducting experiments on real data sets. …”
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  2. 2
    An enhanced quorum selection algorithm

    An enhanced quorum selection algorithm by Haraty, Ramzi A.

    Published 2009
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  3. 3
    An Improved Quorum Selection Algorithm

    An Improved Quorum Selection Algorithm by Haraty, Ramzi A.

    Published 2008
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  4. 4
    Scalable Parallel Algorithms for FPT Problems

    Scalable Parallel Algorithms for FPT Problems by Abu-Khzam, Faisal N.

    Published 2006
    “…To accomplish this, an efficient sequential algorithm and various forms of parallel algorithms are devised, implemented, and compared. …”
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  5. 5
    Squirrel Search Algorithm for Portfolio Optimization

    Squirrel Search Algorithm for Portfolio Optimization by Dhaini, Mahdi

    Published 2019
    “…However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. …”
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  6. 6
    Novel algorithms for accurate DNA base-calling

    Novel algorithms for accurate DNA base-calling by Mohammed, Omniyah Gul

    Published 2013
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  7. 7
    Highly scalable parallel search-tree algorithms

    Highly scalable parallel search-tree algorithms by Abu-Khzam, Faisal N.

    Published 2017
    “…We introduce the notion of a virtual topology and explore the use of search-tree indexing to achieve highly scalable parallel search-tree algorithms for NP-hard problems. …”
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  8. 8
    Asymptotically faster algorithms for parameterized FACE COVER

    Asymptotically faster algorithms for parameterized FACE COVER by Abu-Khzam, Faisal N.

    Published 2005
    “…In this paper, a structure the- orem of Aksionov et al. is combined with a detailed case analysis to produce a face cover algorithm that runs in O(4.5414k +n2) time.…”
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  9. 9
    Scalable parallel algorithms for difficult combinatorial problems

    Scalable parallel algorithms for difficult combinatorial problems by Abu-Khzam, Faisal N.

    Published 2004
    “…A novel combination of emergent algorithmic methods, powerful computational platforms and supporting infrastructure is described. …”
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  10. 10
    A kernelization algorithm for d-Hitting Set

    A kernelization algorithm for d-Hitting Set by Abu-Khzam, Faisal N.

    Published 2010
    “…For a given parameterized problem, π, a kernelization algorithm is a polynomial-time pre-processing procedure that transforms an arbitrary instance of π into an equivalent one whose size depends only on the input parameter(s). …”
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  11. 11
    A bounded search tree algorithm for parameterized

    A bounded search tree algorithm for parameterized by Abu-Khzam, Faisal N.

    Published 2008
    “…Hence, we get both a O(k4.6056+n2) time algorithm for solving red–blue dominating set on planar graphs and a polynomial time algorithm for producing a linear kernel for annotated face cover.…”
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  12. 12
    The Role of KM in Enhancing AI Algorithms and Systems

    The Role of KM in Enhancing AI Algorithms and Systems by AlGhanem, Hani

    Published 2020
    “…However, studies were focusing on specific methods and the impact on some AI algorithms, neglecting the role of other KM processes and how it may affect the AI system to achieve the objective, which reduces the adoption in some organizations. …”
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  13. 13
    Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm

    Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm by Abu Zitar, Raed

    Published 2022
    “…The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. …”
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  14. 14
    An improved quorum selection algorithm (IQSA). (c2007)

    An improved quorum selection algorithm (IQSA). (c2007) by Younes, Samer

    Published 2007
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  15. 15
    Learning control algorithms for tracking "slowly" varying trajectories

    Learning control algorithms for tracking "slowly" varying trajectories by Saab, Samer S.

    Published 1997
    “…In theory, the uniform convergence of learning algorithms is achieved as the number of iterations tends to infinity. …”
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  16. 16
    Fuzzy simulated evolution algorithm for VLSI cell placement

    Fuzzy simulated evolution algorithm for VLSI cell placement by Sait, Sadiq M.

    Published 2003
    “…In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. Identification of near optimal solutions is achieved through a novel goal-directed fuzzy search approach. …”
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  17. 17
    Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks

    Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks by Hassan, Yasmin Adel

    Published 2010
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  18. 18
    A Study on Applying Data Mining in Airline Industry for Demand Forecasting by predicting Item Criticality

    A Study on Applying Data Mining in Airline Industry for Demand Forecasting by predicting Item Criticality by KARAM, ZAINAB HAMDAN

    Published 2018
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  19. 19
    Burst Round Robin as a Proportional-Share Scheduling Algorithm

    Burst Round Robin as a Proportional-Share Scheduling Algorithm by Helmy, Tarek

    Published 2020
    “…In this paper we introduce Burst Round Robin, a proportional-share scheduling algorithm as an attempt to combine the low scheduling overhead of round robin algorithms and favor shortest jobs. …”
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  20. 20
    Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm

    Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

    Published 2024
    “…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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