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  1. 1
    Using the bees algorithm to optimise a support vector machine for wood defect classification

    Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

    Published 2007
    “…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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  2. 2
    LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting

    LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar

    Published 2015
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  3. 3
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  4. 4
    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

    Published 2014
    “…This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. …”
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  5. 5
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. …”
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  6. 6
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. …”
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  7. 7
    Comparative performance of machine learning algorithms for cryptocurrency forecasting

    Comparative performance of machine learning algorithms for cryptocurrency forecasting by Hitam, Nor Azizah, Ismail, Amelia Ritahani

    Published 2018
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  8. 8
    Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data

    Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data by Mohammad Aman Ullah

    Published 2024
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  9. 9
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…Studies have shown how user perception can have a strong influence on policies and decision-making processes in a place, society, and nation. This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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  10. 10
    Evaluating JA-ABC5 hyperparameter optimisation with classifiers

    Evaluating JA-ABC5 hyperparameter optimisation with classifiers by Ravindran, Nadarajan, Noorazliza, Sulaiman, Junita, Mohamad-Saleh

    Published 2024
    “…Because of its simplicity, flexibility, and robustness, the Artificial Bee Colony (ABC) algorithm, a swarm intelligence-based optimisation method, has been widely applied in a variety of fields. …”
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  11. 11
    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification

    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification by Hijazi, Musab, Zeki, Akram M., Ismail, Amelia Ritahani

    Published 2023
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  12. 12
    The design and applications of the african buffalo algorithm for general optimization problems

    The design and applications of the african buffalo algorithm for general optimization problems by Odili, Julius Beneoluchi

    Published 2017
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  13. 13
    Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation

    Evaluating Bees Algorithm for Sequence-based T-way Testing Test Data Generation by M. H., Mohamed Zabil, Kamal Z., Zamli, K. C., Lim

    Published 2018
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  14. 14
    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman

    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman by Azman, Muhammad Izzat Azri

    Published 2017
    “…This project used Artificial Bee Colony Algorithms (ABC) by adapting the food foraging behaviour of bee in honey bee and find a suitable house for home buyer based on their requirement. …”
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  15. 15
    Artificial Bee Colony Algorithm for Pairwise Test Generation

    Artificial Bee Colony Algorithm for Pairwise Test Generation by Alazzawi, Ammar K., Homaid, Ameen A. Ba, Alomoush, Alaa A., Alsewari, Abdulrahman A.

    Published 2017
    “…PABC progresses as a means to achieve the effective use of the artificial bee colony algorithm for pairwise testing reduction.…”
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  16. 16
    A Knowledge Management System for Assessing Lecturer Competence in Indonesian Higher Educational Institutions

    A Knowledge Management System for Assessing Lecturer Competence in Indonesian Higher Educational Institutions by Syaripudin, Undang

    Published 2025
    “…Lecturer competency measurement is carried out by first checking employee status using the SVM algorithm with an accuracy value of 72.28%, then using a hybrid SVM and PSO algorithm with an accuracy value of 100%. …”
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  17. 17
    Anomaly detection in system log files using machine learning algorithms / Zahedeh Zamanian

    Anomaly detection in system log files using machine learning algorithms / Zahedeh Zamanian by Zahedeh, Zamanian

    Published 2019
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  18. 18
    The comparative study of model-based and appearance based gait recognition for leave bag behind

    The comparative study of model-based and appearance based gait recognition for leave bag behind by Zainol, Norfazilah

    Published 2018
    “…Meanwhile, the accuracy and misclassification rate (MER) of Model-based approaches obtained is 97.00% and 3.00% respectively tested on SVM classifier then the accuracy and misclassification rate (MER) of Model-based approaches is 99.00% and 1.00% respectively tested on KNN algorithm. …”
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  19. 19
    Implementing a T-Way Test Generation Strategy Using Bees Algorithm

    Implementing a T-Way Test Generation Strategy Using Bees Algorithm by Mohd Hazli, Mohamed Zabil, Kamal Z., Zamli

    Published 2013
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  20. 20
    Salivary based SERS analysia in recognition of NS1 for PCA-SVM classification of dengue fever / Afaf Rozan Mohd Radzol

    Salivary based SERS analysia in recognition of NS1 for PCA-SVM classification of dengue fever / Afaf Rozan Mohd Radzol by Mohd Radzol, Afaf Rozan

    Published 2018
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