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  1. 1

    An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani by Ab Ghani, Nur Laila

    Published 2015
    “…The urban growth images obtained are analysed to improve existing classification algorithms. The improved algorithm is constructed by adding new parameter and classification rule to existing algorithm. …”
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    Thesis
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    An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

    Published 2018
    “…The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. …”
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    Thesis
  4. 4

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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    Thesis
  5. 5

    An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons by Ghanem, Waheed Ali H. M., Aman, Jantan, Ahmed Ghaleb, Sanaa Abduljabbar, Naseer, Abdullah B.

    Published 2020
    “…This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. …”
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    Article
  6. 6

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

    Published 2017
    “…For a fair performance evaluation, the selection of the best peak model requires experimental exploration by using a common and unbiased classification approach. …”
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    Thesis
  7. 7

    Genetic algorithm fuzzy logic for medical knowledge-based pattern classification by Tan, Chin Hooi, Tan, Mei Sze, Chang, Siow Wee, Yap, Keem Siah, Yap, Hwa Jen, Wong, Shen Yuong

    Published 2018
    “…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
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    Article
  8. 8

    Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO by Sharifah Sakinah, Syed Ahmad

    Published 2014
    “…This paper proposes the integration of feature reduction and data reduction for fuzzy modeling using Cooperative Binary Particle Swarm Optimization (CBPSO). …”
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    Conference or Workshop Item
  9. 9

    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2021
    “…Consequently, optimization algorithms including binary gravitation search algorithm (BGSA) and binary particle swarm optimization (BPSO), were employed to identify the optimal channels for gender classification. …”
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    Article
  10. 10

    Genetic algorithm fuzzy logic for medical knowledge-based pattern classification by Tan C.H., Tan M.S., Chang S.-W., Yap K.S., Yap H.J., Wong S.Y.

    Published 2023
    “…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
    Article
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    An enhanced soft set data reduction using decision partition order technique by Mohammed, Mohammed Adam Taheir

    Published 2017
    “…Also, the accuracy of original soft-set optimal and sub-optimal results have been improved using an intelligent SSR-BPSO-BBO algorithm. The computational cost of search domain (space) has been enhanced using proposed Markov Chain Model.…”
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    Thesis
  12. 12

    Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

    Published 2022
    “…The hybrid method was exemplified in a binary classification between digits ‘4’ and ‘9’ from a multiple features dataset. …”
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    Final Year Project / Dissertation / Thesis
  13. 13

    Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem by Yusof, Norfadzlia Mohd, Muda, Azah Kamilah, Pratama, Satrya Fajri, Carbo-Dorca, Ramon, Abraham, Ajith

    Published 2023
    “…In cheminformatics, choosing the right descriptors is a crucial step in improving predictive models, particularly those that use machine learning algorithms. …”
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    Conference or Workshop Item
  14. 14

    Hybrid binary grey Wolf with Harris hawks optimizer for feature selection by Al-Wajih, R., Abdulkadir, S.J., Aziz, N., Al-Tashi, Q., Talpur, N.

    Published 2021
    “…To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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    Article
  15. 15

    Hybrid Binary Grey Wolf with Harris Hawks Optimizer for Feature Selection by Al-Wajih, R., Abdulkadir, S.J., Aziz, N., Al-Tashi, Q., Talpur, N.

    Published 2021
    “…To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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    Article
  16. 16

    Hybrid binary grey Wolf with Harris hawks optimizer for feature selection by Al-Wajih, R., Abdulkadir, S.J., Aziz, N., Al-Tashi, Q., Talpur, N.

    Published 2021
    “…To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. …”
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    Article
  17. 17

    Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network by Gao, Yuan, Mohd Kasihmuddin, Mohd Shareduwan, Chen, Ju, Zheng, Chengfeng, Romli, Nurul Atiqah, Mansor, Mohd. Asyraf, Zamri, Nur Ezlin

    Published 2024
    “…Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. …”
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  18. 18

    Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network by Al-Hiyali, M.I., Yahya, N., Faye, I., Hussein, A.F.

    Published 2021
    “…The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80. …”
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  19. 19

    Identification of autism subtypes based on wavelet coherence of BOLD FMRI signals using convolutional neural network by Al-Hiyali, M.I., Yahya, N., Faye, I., Hussein, A.F.

    Published 2021
    “…The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80. …”
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    Article
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