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  1. 1
    Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field

    Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field by Toh, Cheng Chuan

    Published 2018
    “…This represents sparseness constraints yield to decrease ambiguity and provide uniqueness to the model.In comparison in between β-divergence,α-divergence,sparse β-divergence and sparse α-divergence NMF2D,it found that SDR value of sparse α-divergence NMF2D is the best decomposition method among all divergences.This can be concluded that sparse α-divergence NMF2D is more applicable in separating real data recording.…”
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  2. 2
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…By using the proposed algorithm, the sparse coefficients are learned by exploiting the relationships among different multi-view features and leveraging the knowledge from multiple related tasks. …”
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  3. 3
    Counting roots of the Polynomial systems by using mixed volume of the Newton polytopes / Nur Suhailah Norazhar, Nur Ain Sofiya Zainuri and Nor Suhada Mohd Rosdi

    Counting roots of the Polynomial systems by using mixed volume of the Newton polytopes / Nur Suhailah Norazhar, Nur Ain Sofiya Zainuri and Nor Suhada Mohd Rosdi by Norazhar, Nur Suhailah, Zainuri, Nur Ain Sofiya, Mohd Rosdi, Nor Suhada

    Published 2024
    “…Since the study dealing with the sparse polynomial systems with two variables, to derive the sparse matrices, the developed Maple program, “multires.mpl” is used. …”
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  4. 4
    Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks

    Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks by Krishnasamy, Ganesh, Paramesran, Raveendran

    Published 2019
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  5. 5
    DEVELOPMENT OF POINT CLOUD DESCRIPTORS FOR ROBUST 3D RECOGNITION

    DEVELOPMENT OF POINT CLOUD DESCRIPTORS FOR ROBUST 3D RECOGNITION by OSMAN ALI, YASIR SALIH

    Published 2015
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  6. 6
    New Quarter-Sweep-Based Accelerated Over-Relaxation Iterative Algorithms and their Parallel Implementations in Solving the 2D Poisson Equation

    New Quarter-Sweep-Based Accelerated Over-Relaxation Iterative Algorithms and their Parallel Implementations in Solving the 2D Poisson Equation by Rakhimov, Shukhrat

    Published 2010
    “…The parallel strategies used in the new algorithms are based on the message latency minimization and processor-independent iterations.…”
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  7. 7
    Dynamical fuzzy autocatalytic set of combustion process in circulating fluidized bed boiler using transition matrix / Hazwani Hashim

    Dynamical fuzzy autocatalytic set of combustion process in circulating fluidized bed boiler using transition matrix / Hazwani Hashim by Hashim, Hazwani

    Published 2014
    “…The development of an algorithm to calculate left Perron vector or steady state vector of the transition matrix is required in order to investigate dynamical behaviour of the system through graph dynamics of FACS. …”
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  8. 8
    Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research

    Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research by Talib, Mohammed Saad, Hassan, Aslinda, Abal Abas, Zuraida, Hassan, Ali Abdul Hussian, Ali, Mohanad Faeq, Al-Araji, Zaid Jasim

    Published 2019
    “…Vehicular Adhoc Networks (VANETs) represents a developed type of an effective communication technology to facilitate the process of information dissemination among vehicles. …”
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  9. 9
    Backhaul load and performance optimality of partial joint processing schemes in LTE-A networks

    Backhaul load and performance optimality of partial joint processing schemes in LTE-A networks by Kousha, Mohammad

    Published 2014
    “…The proposed algorithm also leverages the location dependency of the joint processing. …”
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