Search Results - (( data replication method algorithm ) OR ( parameter adaptation max algorithm ))

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  1. 1

    Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX. by Mohd Ali, Borhanuddin, Ismail, Alyani, Noordin, Nor Kamariah, Ben-Mubarak, Mohammed A., Ng , Ben Chee Kyun

    Published 2013
    “…The proposed algorithm is derived from the self-adaptive handover parameters to overcome the mobile WiMAX ping-pong handover and handover delay issues. …”
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    Article
  2. 2

    Quality of service management algorithms in WiMAX networks by Saidu, Ibrahim

    Published 2015
    “…Firstly, a QoS-Aware CAC scheme for Mobile WiMAX networks is proposed to prevent the starvation problem of the highest and the lowest service classes due to the linear adaptation technique used to accommodate more users into the network as well as inefficient bandwidth utilization because of the way the adaptive reserved bandwidth threshold for handoff is adjusted. …”
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    Thesis
  3. 3

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

    Published 2013
    “…The third scheme is a fuzzy logic-based self-adaptive handover (called FuzSAHO) for handover decision to provide an intelligent self-adaptive handover parameters technique based on received signal strength indicator (RSSI) and MS velocity. …”
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  4. 4

    Dynamic replication algorithm in data grid: Survey by K. Madi, Mohammed, Hassan, Suhaidi

    Published 2008
    “…For improving the performance of file accesses and to ease the sharing amongst distributed collaboration, such a system needs replication services. Data replication is a common method used to improve the performance of data access in distributed systems. …”
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    Book Section
  5. 5

    Enhanced replication strategy with balanced quorum technique and data center selection method in cloud environment by Mohd Ali, Fazlina

    Published 2022
    “…In order to mitigate the issues, ‘cloud data replication’ is commonly implemented for better data performance and promising business continuity. …”
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    Thesis
  6. 6

    Reactive approach for automating exploration and exploitation in ant colony optimization by Allwawi, Rafid Sagban Abbood

    Published 2016
    “…The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. …”
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    Thesis
  7. 7

    Lambda-max criteria weight determination in an adaptive neuro-fuzzy inference system / Rosma Mohd Dom, Daud Mohamad and Ajab Bai Akbarally by Mohd Dom, Rosma, Mohamad, Daud, Akbarally, Ajab Bai

    Published 2012
    “…A neuro-fuzzy system is a fuzzy system that uses learning algorithms derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. …”
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    Research Reports
  8. 8

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

    Published 2011
    “…The motivation of implementation is to make sure the data replication is easy to maintain and cost effective. …”
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    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

    Published 2023
    “…To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. …”
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    Article
  13. 13

    Rockfall source identification using a hybrid Gaussian mixture-ensemble machine learning model and LiDAR data by Fanos, Ali Mutar, Pradhan, Biswajeet, Mansor, Shattri, Md Yusoff, Zainuddin, Abdullah, Ahmad Fikri, Jung, Hyung Sup

    Published 2019
    “…In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.…”
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    Article
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    An intelligent network selection mechanism for vertical handover decision in vehicular Ad Hoc wireless networks by Fazli, Azzali

    Published 2020
    “…The proposed intelligent network selection is known as the Adaptive Handover Decision (AHD) scheme that uses Fuzzy Logic (FL) and Simple Additive Weighting (SAW) algorithms, namely F-SAW scheme. …”
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    Thesis
  17. 17

    A framework for automatic modelling of survival using fuzzy inference. by Hamdan, Hazlina, Garibaldi, Jonathan M.

    Published 2012
    “…After the initialisation of the fuzzy inference structure, the replication data (until time to event) will be subject to be trained using the gradient descent and nonnegative least square algorithm to estimate the conditional event probability. …”
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    Conference or Workshop Item
  18. 18

    A Novel Path Prediction Strategy for Tracking Intelligent Travelers by Motlagh, Omid Reza Esmaeili

    Published 2009
    “…The FCM nodes are a novel selection of kinematical factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the decisional behaviors of the intelligent traveler. …”
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  19. 19

    Access control model based on trust, purpose, and role in materialized view for privacy protection by Salji, Mohd Rafiz

    Published 2019
    “…This study focuses on data privacy protection in materialized view. Materialized view is a replica of a table which is created in a very large system where data are replicated from the master tables. …”
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    Thesis
  20. 20

    HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text by Islam, Md Shofiqul, Sultana, Sunjida, Debnath, Uttam Kumar, Al Mahmud, Jubayer, Islam, S. M. Jahidul

    Published 2021
    “…The accuracy of the proposed HARC method was 82.50 percent IMDB, 98.00 percent for toxic data, 92.31 percent for Cornflower, and 94.60 percent for Emotion recognition data. …”
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    Article