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  1. 1
    QoS evaluation of different TCPs congestion control algorithm using NS2

    QoS evaluation of different TCPs congestion control algorithm using NS2 by Tarmizi, M., Albagul, A., Khalifa, Othman Omran, Wahyudi, Martono

    Published 2006
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  2. 2
    Neighbour-based on-demand routing algorithms for mobile ad hoc networks

    Neighbour-based on-demand routing algorithms for mobile ad hoc networks by Ejmaa, Ali Mohamed E.

    Published 2017
    “…In terms of the applications, The DCFP is more suitable to be used for education applications, while the SNBR is a good algorithm designed to be used for rescue system as data and energy is the main concern. …”
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  3. 3
    DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

    DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS by AYOUB MOHAMMED, MOHAMMED ABDALLA

    Published 2011
    “…It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. …”
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  4. 4
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  5. 5
    Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm

    Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm by Pham Q.B., Afan H.A., Mohammadi B., Ahmed A.N., Linh N.T.T., Vo N.D., Moazenzadeh R., Yu P.-S., El-Shafie A.

    Published 2023
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  6. 6
    OPTIMAL CABLE SELECTION IN RADIAL DISTRIBUTION NETWORKS CONSIDERING PLANNER'S DEFINED VOLTAGE DROP LIMIT

    OPTIMAL CABLE SELECTION IN RADIAL DISTRIBUTION NETWORKS CONSIDERING PLANNER'S DEFINED VOLTAGE DROP LIMIT by Ab Ghani, Mohd Ruddin

    Published 2002
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  7. 7
    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS

    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS by AZMAN, MOHAMAD AFIQ

    Published 2014
    “…The user interface known as Xsniffer software is used to observe the data transmitted as well as providing the user with information on time taken for the data to be delivered corresponding to the implemented algorithm. …”
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  8. 8
    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS

    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS by AZMAN, MOHAMAD AFIQ

    Published 2014
    “…The user interface known as Xsniffer software is used to observe the data transmitted as well as providing the user with information on time taken for the data to be delivered corresponding to the implemented algorithm. …”
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  9. 9
    RED, GRED, AGRED congestion control algorithms in heterogeneous traffic types

    RED, GRED, AGRED congestion control algorithms in heterogeneous traffic types by Seifaddini, Omid, Abdullah, Azizol, Vosough, Hamid

    Published 2013
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  10. 10
    AntNet: a robust routing algorithm for data networks

    AntNet: a robust routing algorithm for data networks by Haseeb, Shariq, Sidek, Khairul Azami, Ismail, Ahmad Faris, Weng Kin, Lai, Yit Mei, Aw

    Published 2004
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  11. 11
    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

    Published 2025
    “…Moreover, the LSA optimization technique is introduced to optimally determine the LSTM deep neural model hyperparameters including the number of hidden neurons, learn rate, epoch, learn rate drop factor, learn rate drop period, and gradient decay factor. …”
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  12. 12
    Red, gred, agred congestion control algorithms in heterogeneous traffic types

    Red, gred, agred congestion control algorithms in heterogeneous traffic types by Seifaddini, Omid, Abdullah, Azizol, Vosough, Hamid

    Published 2013
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  13. 13
    Simulation of single and dual layered rapid pressure swing adsorption

    Simulation of single and dual layered rapid pressure swing adsorption by Lai, Yin Ling

    Published 2013
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  14. 14
    Dropping down the maximum item set: improving the stylometric authorship attribution algorithm in the text mining for authorship investigation

    Dropping down the maximum item set: improving the stylometric authorship attribution algorithm in the text mining for authorship investigation by Mustafa, Tareef Kamil, Mustapha, Norwati, Azmi Murad, Masrah Azrifah, Sulaiman, Md. Nasir

    Published 2010
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  15. 15
    Global-Local Partial Least Squares Discriminant Analysis And Its Extension In Reproducing Kernel Hilbert Space

    Global-Local Partial Least Squares Discriminant Analysis And Its Extension In Reproducing Kernel Hilbert Space by Muhammad, Aminu

    Published 2021
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  16. 16
    Automated traffic counting data collection and analysis

    Automated traffic counting data collection and analysis by Low, Anand Hong Ren

    Published 2021
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  17. 17
    Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants

    Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants by M, Irfan, N, Lukman, A. A, Alfauzi, J, Jumadi

    Published 2019
    “…Information about chili pests is collected so that it becomes a database that can be used to identify disease pests using the data mining method. The use of data mining algorithms is expected to help in the identification of pests and diseases in chili plants. …”
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  18. 18
    Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study

    Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study by Ayoub, Mohammed Abdalla, Demiral, B.M.R

    Published 2010
    “…The data covered a wide range of variables such as oil rate (up to 25000 STB/D), water cut (up to 60%), angles of inclination (from -80 to 210), pipe length up to 26.0 km and pressure drop (from 10 to 250 psi). the model has been generated using the Back-propagation technique with Bayesian Regularization training algorithm for predicting pressure drop in pipelines under various angles of inclination. …”
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  19. 19
    Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri

    Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri by Mohd Zamri, Muhammad Azrill

    Published 2017
    “…The right determination of an undergraduate programme selection by applicants is not an easy task. Some applicants who enrolled for unsuitable programme ended up failed to progress or dropped out from the programme. …”
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  20. 20
    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method

    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

    Published 2024
    thesis::doctoral thesis
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