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  1. 1
    Linear optimization method by using a new iterative algorithm for available transfer capability (ATC)

    Linear optimization method by using a new iterative algorithm for available transfer capability (ATC) by Hojabri, Mojgan, Hizam, Hashim, Mariun, Norman, Abdullah, Senan Mahmood

    Published 2009
    “…Most studies involved load flow based and contingencies that often can be performed in reasonable time with the use of linear methods. This paper presents a simple and accurate method for calculating ATC with a linear optimization method. …”
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  2. 2
    Comparison Between Linear Programming And Integer Linear Programming: A Review

    Comparison Between Linear Programming And Integer Linear Programming: A Review by Sam, Mei Lee, Saptari, Adi, Salleh, Mohd Rizal, Mohamad, Effendi

    Published 2018
    “…For ILP, available algorithms can be classified into exact algorithms and heuristic algorithms. …”
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  3. 3
    Clustering Spatial Data Using a Kernel-Based Algorithm

    Clustering Spatial Data Using a Kernel-Based Algorithm by Awan, A. Majid, Md. Sap, Mohd. Noor

    Published 2005
    “…This paper presents a method for unsupervised partitioning of data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. …”
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  4. 4
    Enhanced algorithms for three-dimensional object interpreter

    Enhanced algorithms for three-dimensional object interpreter by Haron, Habibollah

    Published 2004
    “…The linear system is solved using bisection method of total least square approximation. …”
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  5. 5
    Genetic algorithm techniques for the design of nonlinear microwave circuits

    Genetic algorithm techniques for the design of nonlinear microwave circuits by Arsat, Rashidah

    Published 2004
    “…In this report a hybrid method of Sample Balance Genetic Algorithm (SBGA) used to analyze nonlinear diode excited by RF circuit. …”
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  6. 6
    Detection of multiple outliners in linear regression using nonparametric methods

    Detection of multiple outliners in linear regression using nonparametric methods by Adnan, Robiah

    Published 2004
    “…Generally, method proposed by Sebert et al. (1998) is based on the use of single linkage clustering algorithm with the Euclidean distances to cluster the points in the plots of standard predicted versus residuals values from a linear regression model. …”
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    Monograph
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  7. 7
    Linear quadratic regulator with genetic algorithm for flexible structures vibration control

    Linear quadratic regulator with genetic algorithm for flexible structures vibration control by Jafari, Mohammad

    Published 2015
    “…The PID, which is the most direct method, will be used as a reference in finding the better methods. …”
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  8. 8
    Accurate Real-TIme Object Tracking with Linear Prediction Method

    Accurate Real-TIme Object Tracking with Linear Prediction Method by P. Y., Yeoh, Syed Abu Bakar, Syed Abdul Rahman

    Published 2003
    “…This paper presents an efficient technique for real-time tracking of a single moving object in terrestrial scenes using a stationary camera. The tracking algorithm is based on the linear prediction (LP) solved by the maximum entropy method (MEM). …”
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  9. 9
    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control by Srazhidinov, Radik

    Published 2016
    “…Filtered-X least mean square (FXLMS) control algorithm is a conventional algorithm employed to cancel the noise in linear environment. …”
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  10. 10
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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  11. 11
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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  12. 12
    Model structure selection for a discrete-time non-linear system using genetic algorithm

    Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. …”
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  13. 13
    Integration of projection histograms and linear prediction for object tracking

    Integration of projection histograms and linear prediction for object tracking by Yeoh, Phaik Yong, Abu Bakar, Syed Abdul Rahman

    Published 2003
    “…The proposed tracking algorithm integrates the Projection Histograms technique with the Linear Prediction method in order to achieve a faster tracking speed. …”
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  14. 14
    Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions

    Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions by Abbas, Iraq Tareq

    Published 2018
    “…Later, this algorithm was used to solve bi-objective Production Planning (PP) and Scheduling Problem (Sch.P). …”
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  15. 15
    The efficiency of conjugate gradient methods with global convergence / Siti Nur Hafiza Shamsudin

    The efficiency of conjugate gradient methods with global convergence / Siti Nur Hafiza Shamsudin by Shamsudin, Siti Nur Hafiza

    Published 2019
    “…Conjugate gradient methods are usually used to solve any problem that related to large number of variables such as a large linear system of equations. …”
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  16. 16
    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
    “…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. …”
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  17. 17
    Dynamic determinant matrix-based block cipher algorithm

    Dynamic determinant matrix-based block cipher algorithm by Juremi, Julia

    Published 2018
    “…The performance analysis in terms of processing speed of the encryption and decryption process of the DDBC algorithm shows minimal differences in both AES and DDBC algorithm despite of the difference method of transformation used in both algorithms. …”
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  18. 18
    Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling

    Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

    Published 2018
    “…To illustrate the potential of built-in algorithm, a case study which emphasized on tumour was used. …”
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    Proceeding Paper
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  19. 19
    Path planning algorithm for a car like robot based on MILP method

    Path planning algorithm for a car like robot based on MILP method by Mohd Sadri, Mohd Pawzi

    Published 2013
    “…The findings of this research have shown that the MILP method can be used in the path planning problem in terms of finding a safe and shortest path. …”
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  20. 20
    VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS

    VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS by KAMARUDDIN, ERNA SHAFFIQA

    Published 2018
    “…The feature extraction obtain is trained using Linear SVM and Decision Trees. The result shows that the Linear SVM algorithm outperform ACF algorithm with accuracy percentage of 88.5% compared to 62.8% for smaller datasets.…”
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