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  1. 1
    Lumped parameter modelling in femoral popliteal artery for normal and severe conditions

    Lumped parameter modelling in femoral popliteal artery for normal and severe conditions by Kaha, Asrizan, Taib, Ishkrizat, Paisal, Muhammad Sufyan Amir, Tajul Arifin, Ahmad Mubarak, Ismail, Al Emran, Asmuin, Norzelawati, Abdul Haq, Reazul Haq, Chatpun, Surapong, Yamamoto, Takahisa, Osman, Kahar

    Published 2018
    “…In conclusion, the obtained waveform propagation from the linear analysis of the lumped parameter modelling shows that the periodic mean pressures and flow rate distributions in large arteries are highly depend on the resistances and the compliances. …”
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  2. 2
    Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms

    Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms by Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

    Published 2022
    “…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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  3. 3
    Determining penetration limit of central distributed generation topology in radial distribution networks

    Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

    Published 2021
    “…Improper utilization of distributed generation units in distribution networks lead to frequency variations, raise system power losses, voltage deviation, and altering the fault current value. …”
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  4. 4
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…One of the important assumptions of the linear model is that the error terms are normally distributed. Unfortunately, many researchers are not aware that the performance of the OLS can be very poor when the data set that one often makes a normal assumption, has a heavy-tailed distribution which may arise as a result of the presence of outliers. …”
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  5. 5
    Effect of parameters variation on the performance of particle swarm optimization algorithm for tag coverage problem of radio frequency identification network

    Effect of parameters variation on the performance of particle swarm optimization algorithm for tag coverage problem of radio frequency identification network by Nawawi, Azli, Hasnan, Khalid, Ngali, Zamani, Sidek, Noor Azizah

    Published 2016
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  6. 6
    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system

    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system by Ali, Hazem I.

    Published 2010
    “…These algorithms are designed to achieve the robustness over a wide range of system parameters change and disturbances. …”
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  7. 7
    Statistical approach on grading: mixture modeling

    Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

    Published 2006
    “…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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  8. 8
    Voltage Variation Analysis By Using Gabor Transform

    Voltage Variation Analysis By Using Gabor Transform by Abdullah, Abdul Rahim, Tee, Wei Hown, Yusoff, Mohd Rahimi

    Published 2019
    “…The parameters extracted can detect the voltage variation signals successfully. …”
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  9. 9
    Entropy in portfolio optimization / Yasaman Izadparast Shirazi

    Entropy in portfolio optimization / Yasaman Izadparast Shirazi by Yasaman Izadparast, Shirazi

    Published 2017
    “…More specifically, we use multi-objective models that are the mean-entropy-entropy (MEE). The purpose of this new model is to overcome the limitations as observed in a traditional model; that is, having performance close to Markowitz’s mean-variance (MV) model when data comes from a normal distribution, but exhibit better performance when data comes from a non-normal distribution. …”
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  10. 10
    Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan

    Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

    Published 2015
    “…Directional statistics is a branch of statistics which deal with the data in angle form in which the method of analysis is different from linear data. For example, the distribution analogues to the normal distribution in linear data is known as circular normal distribution. …”
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  11. 11
    Performance of particle swarm optimization under different range of direct current motor's moment of inertia / Mohd Azri Abdul Aziz

    Performance of particle swarm optimization under different range of direct current motor's moment of inertia / Mohd Azri Abdul Aziz by Abdul Aziz, Mohd Azri

    Published 2018
    “…For WTRI alone, several variations of weight combinations have been tested in determining their effects on the performance of PSO algorithm under different range of moment of inertia, from 3.88xl0"7 kgm2 to 3.88x10"1 kgm2 which is within a range found in literature. …”
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  12. 12
    TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

    TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms by Marina, Patrick, Mah, Yau Seng, Putuhena, Frederik Josep, Wang, Yin Chai, Onni Suhaiza, Selaman

    Published 2016
    “…These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.…”
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  13. 13
    Statistical approach on grading the student achievement via normal mixture modeling

    Statistical approach on grading the student achievement via normal mixture modeling by Md. Desa, Zairul Nor Deana, Mohamad, Ismail, Mohd. Khalid, Zarina, Md. Zin, Hanafiah

    Published 2006
    “…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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  14. 14
    Fire severity and post-fire generation using : Landsat [NDVI], [NBR] and [SAVI] / Nur Izzaty Dali

    Fire severity and post-fire generation using : Landsat [NDVI], [NBR] and [SAVI] / Nur Izzaty Dali by Dali, Nur Izzaty

    Published 2018
    “…Pre-fire and post-fire of Landsat 7 ETM+ images were obtained to identify the fire severity using Normalized Burn Ratio algorithms. The objectives of this study are (1) to produce Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Soil Adjusted Vegetation Index (SAVI) and (2) to determine the changes of forest distribution based on NDVI, NBR and SAVI changes. …”
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  15. 15
    Performance of particle swarm optimization under different range of direct current motor's moment of inertia / Mohd Azri Abdul Aziz

    Performance of particle swarm optimization under different range of direct current motor's moment of inertia / Mohd Azri Abdul Aziz by Abdul Aziz, Mohd Azri

    Published 2018
    “…For WTRI alone, several variations of weight combinations have been tested in determining their effects on the performance of PSO algorithm under different range of moment of inertia, from 3.88x10-7 kgm2 to 3.88x10- 1 kgm2 which is within a range found in literature. …”
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  16. 16
    A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru

    A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru by Rahmad Ramadhan, Laska, Anne Mudya, Yolanda

    Published 2024
    “…However, Z-Score Normalization, sometimes referred to as Standardization, standardizes the data by dividing by the standard deviation and subtracting the mean, maintaining the shape of the distribution and making it resistant to outliers. …”
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  17. 17
    Development Of An Algorithm To Reduce The Topographical Effects In Reflected Radiance

    Development Of An Algorithm To Reduce The Topographical Effects In Reflected Radiance by Yeap, Eng Choo

    Published 2020
    “…These algorithms use data from extraterrestrial irradiance, atmospheric profiles, digital elevation models, and radiative transfer models to calculate the amount of irradiance on Earth’s surface to reduce distortions due to the topographic effect. …”
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  18. 18
    Performance evaluation of smoothed functional algorithm based methods for sigmoid-PID control optimization in MIMO twin-rotor systems

    Performance evaluation of smoothed functional algorithm based methods for sigmoid-PID control optimization in MIMO twin-rotor systems by Mok, Ren Hao, Mohd Ashraf, Ahmad

    Published 2024
    “…This paper explores the tuning of the Sigmoid Proportional-Integral-Derivative (SPID) controller using variations of the Smoothed Functional Algorithm (SFA) for the underactuated Multiple-Input Multiple-Output (MIMO) twin-rotor system. …”
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  19. 19
    Statistical approach on grading the student achievement via mixture modelling

    Statistical approach on grading the student achievement via mixture modelling by Md. Desa, Zairul Nor Deana, Mohamad, lsmail

    Published 2006
    “…In the conditional Bayesian model, we assume the Normal Mixture distribution where the grades are distinctively separated means and proportions of the Normal Mixture distribution. …”
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  20. 20
    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…These algorithms are data-driven and do not require thresholds or predefined assumptions. …”
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