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  1. 1
    OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network

    OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network by Alhakimi, Mohammed Ameen, Latip, Rohaya

    Published 2019
    “…This study presents an optimal tasks scheduling algorithm by enhancing Max-Min algorithm. …”
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  2. 2
    A particle swarm optimization and min-max­-based workflow scheduling algorithm with QoS satisfaction for service-­oriented grids

    A particle swarm optimization and min-max­-based workflow scheduling algorithm with QoS satisfaction for service-­oriented grids by Ambursa, Faruku Umar, Latip, Rohaya, Abdullah, Azizol, K. Subramaniam, Shamala

    Published 2017
    “…The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. …”
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  3. 3
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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    Thesis
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  4. 4
    Fair bandwidth assignment in hierarchical scheduling for mobile WiMAX system

    Fair bandwidth assignment in hierarchical scheduling for mobile WiMAX system by Alsahag, Ali Mohammed Mansoor, Mohd Ali, Borhanuddin, Noordin, Nor Kamariah, Mohamad, Hafizal

    Published 2011
    “…Simulations result showed that the proposed algorithm optimize the overall system throughput and assigns bandwidth effectively to the different service classes while ensuring that the QoS requirements are satisfied.…”
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  5. 5
    An optimal tasks scheduling algorithm based on QoS in cloud computing network

    An optimal tasks scheduling algorithm based on QoS in cloud computing network by Alhakimi, Mohammed Ameen Mohammed Abdo

    Published 2017
    “…This study presents an optimal task scheduling algorithm by enhancing Max-Min and TS algorithm. …”
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  6. 6
    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier

    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

    Published 2018
    “…There are a lot of feature extraction methods and classification methods for iris classification. …”
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    Monograph
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  7. 7
    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach

    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

    Published 2009
    “…This method carries the advantages of the two previous methods in order to improve the classification tasks. …”
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  8. 8
    Pattern Recognition for Human Diseases Classification in Spectral Analysis

    Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

    Published 2022
    “…Typically, pattern recognition consists of two components: exploratory data analysis and classification method. …”
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  9. 9
    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks

    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks by Mubarak, Mohammed Awadh Ahmed Ben

    Published 2013
    “…For real-time applications, simulation results show that RFuzSAHO decision algorithm enhances the VoIP quality (Mean Opinion Score, MOS) up to 5.4% compared to FuzSAHO algorithm when the MS velocity is 20m/s.…”
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  10. 10
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…Hence, we restrict the analysis to MAX2SAT clauses. We utilize Dev C++ as the platform of training and testing our proposed algorithm. …”
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  11. 11
    An exploration technique for the interacted multiple ant colonies optimization framework

    An exploration technique for the interacted multiple ant colonies optimization framework by Aljanaby A., Ku-Mahamud K.R., Norwawi N.Md.

    Published 2024
    Conference Paper
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  12. 12
    The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition

    The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition by M. A., Ameedeen, Marhaini, M. S.

    Published 2016
    “…While the algorithm helped to classify different types of wavelengths produced from the sensor, a classification-based algorithm via Pattern Recognition Method will be used to classify and match the food components. …”
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  13. 13
    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin

    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin by Alsahag, Ali Mohammed, Mohd Ali, Borhanuddin, Noordin, Nor Kamariah, Mohamad, Hafizal

    Published 2014
    “…Simulation results show that the proposed algorithm manages to optimize the overall system utilization while at the same time guarantee the maximum latency requirements for real-time traffic.…”
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  14. 14
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  15. 15
    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification

    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
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  16. 16
    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC by Jamil, Nur Farahim

    Published 2014
    “…This project proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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    Final Year Project
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  17. 17
    Grid base classifier in comparison to nonparametric methods in multiclass classification

    Grid base classifier in comparison to nonparametric methods in multiclass classification by Moheb Pour, Majid Reza, Jantan, Adznan, Saripan, M. Iqbal

    Published 2010
    “…This method carries the advantages of the two previous methods in order to improve the classification tasks. …”
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  18. 18
    Design of a pattern classification system using extreme learning machine

    Design of a pattern classification system using extreme learning machine by Sakthivel Thangavelu

    Published 2024
    text::Final Year Project
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  19. 19
    Multilevel learning in Kohonen SOM network for classification problems

    Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

    Published 2006
    “…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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  20. 20
    Fair energy-efficient resource allocation for downlink NOMA heterogeneous networks

    Fair energy-efficient resource allocation for downlink NOMA heterogeneous networks by Ali, Zuhura Juma, Noordin, Nor Kamariah, Sali, Aduwati, Hashim, Fazirulhisyam

    Published 2020
    “…The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. …”
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