Search Results - (( model validation based algorithm ) OR ( parameter automation a algorithm ))*

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  1. 1
    Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification

    Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification by Ling, Hui Yean

    Published 2025
    “…This study validates the effectiveness of deep learning based segmentation methods for automated soil crack recognition and quantification, contributing to engineering applications with improved methodologies for analysing desiccation behaviour in expansive soils. …”
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  2. 2
    Adaptive linux-based TCP congestion control algorithm for high-speed networks

    Adaptive linux-based TCP congestion control algorithm for high-speed networks by Alrshah, Mohamed A.

    Published 2017
    “…The main contributions of this model are: First, to validate the simulation results of AF-based CCA by comparing them to the numerical results of this model and to the results of NewReno as a benchmark. …”
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  3. 3
    Enhancement Of Static Code Analysis Malware Detection Framework For Android Category-Based Application

    Enhancement Of Static Code Analysis Malware Detection Framework For Android Category-Based Application by Aminordin, Azmi

    Published 2021
    “…This study suggests the work to combine the optimization of feature selection and algorithm parameters to achieve higher accuracy and acquire more reliable comparison.…”
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  4. 4
    Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

    Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

    Published 2023
    “…In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. …”
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  5. 5
    Identification of debris flow initiation zones using topographic model and airborne laser scanning data

    Identification of debris flow initiation zones using topographic model and airborne laser scanning data by Lay, Usman Salihu, Pradhan, Biswajeet

    Published 2017
    “…Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). …”
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  6. 6
    Stereo Camera-Based Aerial Target Geolocation in a Ground-Based Platform Environment

    Stereo Camera-Based Aerial Target Geolocation in a Ground-Based Platform Environment by Amir Hilmi Bin Ahmad Azizi

    Published 2024
    thesis::master thesis
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  7. 7
    System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach

    System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach by Abdullah Hashim, Azrul Azim, Abdul Ghani, Nor Maniha, Ahmad, Salmiah, Nasir, Ahmad Nor Kasruddin

    Published 2024
    “…As for validation, PI-ABC showed similar results in HIL environment with a steady-state error of -0.0314, an overshoot of 5.0007%, a rise time of 1.3805 s, and a settling time of 9.1002 s. …”
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  8. 8
    System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach

    System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach by Azrul Azim, Abdullah Hashim, Nor Maniha, Abdul Ghani, Salmiah, Ahmad, Ahmad Nor Kasruddin, Nasir

    Published 2024
    “…As for validation, PI-ABC showed similar results in HIL environment with a steady-state error of -0.0314, an overshoot of 5.0007%, a rise time of 1.3805 s, and a settling time of 9.1002 s. …”
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  9. 9
    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Ayop Azmi, Nurnajmin Qasrina Ann, Pebrianti, Dwi, Abas, Mohammad Fadhil, Bayuaji, Luhur

    Published 2023
    “…Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper�parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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  10. 10
    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohammad Fadhil, Abas, Bayuaji, Luhur

    Published 2023
    “…Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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  11. 11
    Real-time oil palm fruit bunch ripeness grading system using image processing techniques

    Real-time oil palm fruit bunch ripeness grading system using image processing techniques by Alfatni, Meftah Salem M.

    Published 2013
    “…These results are optimal based on the thorns model. A new approach was developed using expert rules-based system. …”
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  12. 12
    Improved criteria determination of an automated negative lightning return strokes characterisation using Brute-Force search algorithm

    Improved criteria determination of an automated negative lightning return strokes characterisation using Brute-Force search algorithm by Abdul Haris, Faranadia

    Published 2021
    “…The statistical analysis showed a good agreement between manual and automated data on each parameter, with a percentage difference observed between 0.08% and 6.88%. …”
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  13. 13
    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
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  14. 14
    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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  15. 15
    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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  16. 16
    Automated real-time electrocardiogram diagnosis based on the modified Pan-Tompkins algorithm for long-term monitoring systems

    Automated real-time electrocardiogram diagnosis based on the modified Pan-Tompkins algorithm for long-term monitoring systems by Gerasimova, Yuliya, Sidi, Fatimah, Ivel, Victor, Avdeyev, Vladimir, Abdullah, Lili Nurliyana, Moldakhmetov, Sayat

    Published 2025
    “…A modified Pan-Tompkins algorithm for determining the boundaries of the QRS system has been developed. …”
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  17. 17
    Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm.

    Performance optimization of simultaneous machine and automated guided vehicle scheduling using fuzzy logic controller based genetic algorithm. by Sulaiman, Shamsuddin, Mohd Ariffin, Mohd Khairol Anuar, Badakshian, Mostafa

    Published 2012
    “…Previously, genetic algorithm (GA) is considered as a heuristic method to solve AGVscheduling problem. …”
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  18. 18
    Automated Model Generation Approach Using MATLAB

    Automated Model Generation Approach Using MATLAB by Xia, Likun

    Published 2011
    “…An estimation algorithm is then required in order to obtain parameters for these models. …”
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  19. 19
    Using simulated annealing algorithm for optimization of quay cranes and automated guided vehicles scheduling

    Using simulated annealing algorithm for optimization of quay cranes and automated guided vehicles scheduling by Homayouni, Seyed Mahdi, Tang, Sai Hong, Ismail, Napsiah, Mohd Ariffin, Mohd Khairol Anuar

    Published 2011
    “…This model minimizes the makespan of all the loading and unloading tasks for a set of cranes in a scheduling problem. Based on the simulated annealing (SA) algorithm, a scheduling method is proposed to solve the problem in a relatively short period of time. …”
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  20. 20
    Reactive approach for automating exploration and exploitation in ant colony optimization

    Reactive approach for automating exploration and exploitation in ant colony optimization by Allwawi, Rafid Sagban Abbood

    Published 2016
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