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  1. 1
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  2. 2
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  3. 3
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
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  4. 4
    Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain

    Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

    Published 2020
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  5. 5
    Double-layered hybrid neural network approach for solving mixed integer quadratic bilevel problems

    Double-layered hybrid neural network approach for solving mixed integer quadratic bilevel problems by Shamshul Bahar, Yaakob, Prof. Madya, Watada, Junzo

    Published 2010
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  6. 6
    Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat

    Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat by Fatima, Jannat

    Published 2022
    “…The result implies a good connection between how a novice programmer goes through a programming problem and his/her emotional arousal at that moment. …”
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  7. 7
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…Therefore, a new classifier based on genetic programming (GP) and support vector machine (SVM) is proposed in this thesis in order to solve the imbalanced classification problem without changing the data properties. …”
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  8. 8
    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)

    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

    Published 2022
    “…Optimization algorithms can be used to solve Non-deterministic Polynomial (NP) hard problem like system management. …”
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  9. 9
    Development of dynamic programming algorithm for maintenance scheduling problem

    Development of dynamic programming algorithm for maintenance scheduling problem by Zafira Adlia, Mohd Fauzi

    Published 2020
    “…The objectives of this research are to develop a dynamic programming algorithm for the maintenance scheduling problem that can deal with the uncertainty and to determine the optimum maintenance schedule that will change according to the uncertainty that happened. …”
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  10. 10
    Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem

    Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem by Sulaiman M.A.H.B., Suliman A., Ahmad A.R.

    Published 2023
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  11. 11
    Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application

    Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

    Published 2020
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  12. 12
    Impact learning: A learning method from feature's impact and competition

    Impact learning: A learning method from feature's impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

    Published 2023
    “…Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. …”
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  13. 13
    Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes

    Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes by Nor Atiqah, Zolpakar, Lodhi, Swati Singh, Pathak, Sunil, Sharma, Mohita Anand

    Published 2020
    “…The conventional methods for solving multi-objective problems consist of random searches, dynamic programming, and gradient methods whereas modern heuristic methods include cognitive paradigm as artificial neural networks, simulated annealing and Lagrangian approcehes. …”
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  14. 14
    Case Slicing Technique for Feature Selection

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…One of the problems addressed by machine learning is data classification. …”
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  15. 15
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
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  16. 16
    Predicting uniaxial compressive strength using Support Vector Machine algorithm

    Predicting uniaxial compressive strength using Support Vector Machine algorithm by Zakaria, Hafedz, Abdullah, Rini Asnida, Ismail, Amelia Ritahani, Amin, Mohd For

    Published 2019
    “…This paper presents the application of Support Vector Machine (SVM) algorithm to predict the UCS. An algorithm has been tested on a series of rock data using dry density and velocity parameters. …”
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  17. 17
    Restaurant locator using Djikstra Algorithm / Mohamad Aliff Hakimi Lukman Hakim

    Restaurant locator using Djikstra Algorithm / Mohamad Aliff Hakimi Lukman Hakim by Lukman Hakim, Mohamad Aliff Hakimi

    Published 2017
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  18. 18
    Impact learning : A learning method from feature’s impact and competition

    Impact learning : A learning method from feature’s impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

    Published 2023
    “…Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. …”
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  19. 19
    The solution of 2048 puzzle game using A* algorithm / Zafhira Ismail

    The solution of 2048 puzzle game using A* algorithm / Zafhira Ismail by Ismail, Zafhira

    Published 2017
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  20. 20
    Literature Review of Optimization Techniques for Chatter Suppression In Machining

    Literature Review of Optimization Techniques for Chatter Suppression In Machining by A. R., Yusoff, Mohamed Reza Zalani, Mohamed Suffian, Mohd Yusof, Taib

    Published 2011
    “…In this paper, it can be observed that for chatter suppression, optimization focuses on spindle design, tool path, cutting process, and variable pitch. Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.…”
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