Search Results - (( variable learning real algorithm ) OR ( java application optimization algorithm ))

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

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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    Conference or Workshop Item
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    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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    Final Year Project
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    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…The objective of this paper is to introduce a combination of advantages of different algorithm scheme into one learning algorithm. For this purpose, three models is developed, first using recursive least square algorithm (RLS), second using recursive instrument variable (RIV) algorithm and lastly using combination of this two algorithms. …”
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    Ensemble dual recursive learning algorithms for identifying flow with leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…The objective of this paper is to introduce a combination of advantages of different algorithm scheme into one learning algorithm. For this purpose, three models is developed, first using recursive least square algorithm (RLS), second using recursive instrument variable (RIV) algorithm and lastly using combination of this two algorithm. …”
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    Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems by Yousefi, M., Darus, A.N., Yousefi, M., Hooshyar, D.

    Published 2015
    “…In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. …”
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    Article
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    Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2024
    “…Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. …”
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    Article
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    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    “…The firefly algorithm remains a feasible alternative for shallow architectural network models, while metaheuristic algorithms such as the Particle swarm algorithm and Bat algorithm are better options for deeper architectural network models. …”
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    Thesis
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    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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    Thesis
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    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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    Thesis
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    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
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    Thesis
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    Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

    Published 2021
    “…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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