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  1. 1
    Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)

    Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR) by Noraziah, Ahmad, Abdalla, Ahmed N., Beg, Abul Hashem

    Published 2013
    “…Proper mechanism is significantly required in order to manage the complex heterogeneous data replication. This paper presents a new algorithm namely the Persistence Layer Synchronous Replication (PLSR) in order to manage the agent handling the replication. …”
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  2. 2
    Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system

    Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system by Beg, Abul Hashem

    Published 2011
    “…All the replication servers established its connection through interfaces. …”
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  3. 3
    Improving response time, availability and reliability through asynchronous replication technique in cluster architecture of web server cluster

    Improving response time, availability and reliability through asynchronous replication technique in cluster architecture of web server cluster by Aznida Hayati, Zakaria@Mohamad, Ahmad Nazari, Mohd Rose, Zarina, Mohamad, Wan Suryani, Wan Awang

    Published 2010
    “…This paper proposes an algorithm of data replication scheme based on asynchronous replication in order to improve web server cluster system reliability. …”
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  4. 4
    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
    “…There are two general paradigms for pattern recognition classification which are supervised and unsupervised learning. …”
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  5. 5
    A technique of dispatching algorithms for web-server cluster

    A technique of dispatching algorithms for web-server cluster by Zarina Mohamad

    Published 2011
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  6. 6
    Fault Tolerence on Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique

    Fault Tolerence on Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique by Noraziah, Ahmad, Ainul Azila, Che Fauzi, Fairuzullah, A., Nurzety Aqtar, Ahmad Azuan, Azlina, Zainuddin

    Published 2013
    “…It also increases the degrees of data availability. This is because the missing data from during the server failure has been reconciled and replicated after that server recovered.…”
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  7. 7
    Fault Tolerance On Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique

    Fault Tolerance On Binary Vote Assignment Cloud Quorum (BVACQ) Replication Technique by Noraziah, Ahmad, Abdulla Fairuzullah, Ahmad Tajuddin, Ainul Azila, Che Fauzi, Nurzety Aqtar, Ahmad Azuan, Azlina, Zainuddin, Herawan, Tutut

    Published 2013
    “…It also increases the degrees of data availability. This is be cause the missing data from during the server failure has been reconciled and replicated after that server recovered.…”
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  8. 8
    Binary Vote Assignment on Cloud Quorum Algorithm for Fragmented MyGRANTS Database Replication

    Binary Vote Assignment on Cloud Quorum Algorithm for Fragmented MyGRANTS Database Replication by Noraziah, Ahmad, Ainul Azila, Che Fauzi, Herawan, Tutut, Zailani, Abdullah

    Published 2015
    “…Data replication is one of the mechanisms to manage data since it improves data accessibility and reliability. …”
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  9. 9
    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
    “…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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  10. 10
    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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  11. 11
    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks by F. M., Mohammed

    Published 2014
    “…Pattern classification is one of the major components for the design and development of a computerized pattern recognition system. …”
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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
    Machine Learning Approach Regarding The Classification And Prediction Of Dog Sounds: A Case Study Of South Indian Breeds

    Machine Learning Approach Regarding The Classification And Prediction Of Dog Sounds: A Case Study Of South Indian Breeds by Prabu Mohandas, Jerline Sheebha Anni, Khairunnisa Hasikin, Dhayalan Velauthapillai, Veena Raj, Thanihaichelvan Murugathas, Rajkumar Thanasekaran, Muhammad Mokhzaini Azizan

    Published 2024
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  14. 14
    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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  15. 15
    Genetic algorithm fuzzy logic for medical knowledge-based pattern classification

    Genetic algorithm fuzzy logic for medical knowledge-based pattern classification by Tan, Chin Hooi, Tan, Mei Sze, Chang, Siow Wee, Yap, Keem Siah, Yap, Hwa Jen, Wong, Shen Yuong

    Published 2018
    “…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
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  16. 16
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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  17. 17
    A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

    A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification by Wong, S.Y., Yap, K.S., Yap, H.J., Tan, S.C.

    Published 2015
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  18. 18
    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
    “…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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  19. 19
    Design of a pattern classification system using extreme learning machine

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

    Published 2023
    text::Final Year Project
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  20. 20
    Comparative study of machine learning algorithms in data classification

    Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

    Published 2025
    “…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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