Search Results - (( model validation a algorithm ) OR ( user classification swarm algorithm ))

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  1. 1
    Malicious URL classification using artificial fish swarm optimization and deep learning

    Malicious URL classification using artificial fish swarm optimization and deep learning by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed

    Published 2023
    “…With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. …”
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  2. 2
    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam

    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

    Published 2017
    “…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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    Thesis
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  3. 3
    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy by Al-Dabbagh, Rawaa Dawoud, Neri, Ferrante, Idris, Norisma, Baba, Mohd Sapiyan

    Published 2018
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  4. 4
    An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

    An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection by Shing, Chiang Tan, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Lim, Pey, Yun Goh, Chee, Peng Lim

    Published 2023
    “…During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. …”
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  5. 5
    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients

    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients by Moghbel, Mehrdad

    Published 2017
    “…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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  6. 6
    Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm

    Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm by Ariff Nijay, Ramli

    Published 2023
    “…Lastly, a study of the industrial case was performed as a validation of the developed model and algorithm. …”
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  7. 7
    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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  8. 8
    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation

    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon

    Published 2015
    “…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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  9. 9
    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)

    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA) by Ainul, H. M. Y., Salleh, S. M., Halib, N., Taib, H., Fathi, M. S.

    Published 2024
    “…System identification is a method to build a model for a dynamic system from the experimental data. …”
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  10. 10
    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score

    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

    Published 2021
    “…However, the conversion of a complex form of ML algorithms into a simple statistical model is the prime concern. …”
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  11. 11
    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation

    Implementation of New Improved Round Robin (NIRR) CPU scheduling algorithm using discrete event simulation by Chang, Jan Voon, Ahmad, Idawaty

    Published 2016
    “…The main objective of this research is to validate the NIRR algorithm by developing a comprehensive simulation model using Discrete Event Simulation (DES). …”
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  12. 12
    Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)

    Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA) by Ainul, H. M. Y., Salleh, S. M., Halib, N., Taib, H., Fathi, M. S.

    Published 2018
    “…System identification is a method to build a model for a dynamic system from the experimental data. …”
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  13. 13
    Simplified approach to validate constitutive model formulation of orthotropic materials undergoing finite strain deformation

    Simplified approach to validate constitutive model formulation of orthotropic materials undergoing finite strain deformation by Mohd Nor, Mohd Khir, Ma`at, Norzarina

    Published 2016
    “…The validation process was performed by conducting a series of a single element analysis of a uniaxial strain test and uniaxial stress test. …”
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  14. 14
    Performance of Semi Active Lateral Control (SALC) algorithm for semi active suspension system in multibody co-simulation method / M. M. Abdul Majid ...[et al.]

    Performance of Semi Active Lateral Control (SALC) algorithm for semi active suspension system in multibody co-simulation method / M. M. Abdul Majid ...[et al.] by Abdul Majid, M. M., Salleh, M. S., Abu Hashim, M. A., Ismail, N. H., Mansor, S., Abu Bakar, S. A.

    Published 2018
    “…The experimental data used for simulation model correlation and validation. New controller algorithm (SALC) was than developed in Matlab/Simulink and integrate with correlated vehicle plant model for handling performance validation against passive suspension and Skyhook. …”
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  15. 15
    Dynamic modelling of a flexible beam structure using feedforward neural networks for active vibration control

    Dynamic modelling of a flexible beam structure using feedforward neural networks for active vibration control by Tuan Abdul Rahman, Tuan Ahmad Zahidi, As'arry, Azizan, Abdul Jalil, Nawal Aswan, Raja Ahmad, Raja Mohd Kamil

    Published 2019
    “…Correlation tests were used to validate the obtained model. Based on the proposed method, a small mean squared error value has been achieved in the validation phase. …”
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  16. 16
    Prediction of Machine Failure by Using Machine Learning Algorithm

    Prediction of Machine Failure by Using Machine Learning Algorithm by Fakhrurazi, Nur Amalina

    Published 2019
    “…Validation for the model is analyzed by using validation testing data and cross validation. …”
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    Final Year Project
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  17. 17
    System identification using Extended Kalman Filter

    System identification using Extended Kalman Filter by Alias, Ahmad Hafizi

    Published 2017
    “…In order to evaluate the performance of the EKF learning algorithm, the proposed algorithm validation were analyzed using model validation methods as a checker such as One Step Ahead (OSA) and correlation coefficient (R2). …”
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  18. 18
    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
    “…First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. …”
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  19. 19
    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
    “…First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. …”
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  20. 20
    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
    “…First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. …”
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