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  1. 1

    Mutable composite firefly algorithm for gene selection in microarray based cancer classification by Fajila, Mohamed Nisper Fathima

    Published 2022
    “…Evaluation was performed based on two metrics: classification accuracy and size of feature set. …”
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    Thesis
  2. 2

    Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd by Nur Farahaina, Idris

    Published 2022
    “…The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy.…”
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    Thesis
  3. 3

    An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

    Published 2019
    “…Thus, to deal with huge dataset, data mining technique can be improved by introducing discretization algorithm to increase classification performance. …”
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  4. 4

    Hybrid performance measures and mixed evaluation method for data classification problems by Hossin, Mohammad

    Published 2012
    “…This study investigates two different issues of performance measure in data classification problem. …”
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    Thesis
  5. 5

    A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

    Published 2023
    “…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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    Article
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    Academic leadership bio-inspired classification model using negative selection algorithm by Jantan, Hamidah, Sa’dan, Siti ‘Aisyah, Che Azemi, Nur Hamizah Syafiqah

    Published 2015
    “…Several experiments were carried out by using different set of training and testing data-sets to evaluate the accuracy of the proposed model.As a result, the accuracy of the proposed model is considered excellent for academic leadership classification.For future work, in order to enhance the proposed bio-inspired classification model, a comparative study should be conducted using other established artificial immune system classification algorithms i.e. clonal selection and artificial immune network.…”
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    Conference or Workshop Item
  8. 8

    Classification of JPEG files by using extreme learning machine by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…Recovery of data files when their system information missing is a challenging research issue. …”
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    Article
  9. 9

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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    Thesis
  10. 10

    Personality prediction using Random Forest algorithm / Wan Abdul Qayyum Abdul Wahab by Abdul Wahab, Wan Abdul Qayyum

    Published 2023
    “…The research effort attempted to create a personality prediction system based on the Random Forest algorithm. …”
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    Thesis
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    Empowering cloud providers: optimised locust-inspired algorithm for SLA violation mitigation in green cloud computing by Alsaaidah, Yousef A., Muhammed, Abdullah, Ala’anzy, Mohammed Alaa, Othman, Mohamed, Abdullah, Azizol

    Published 2025
    “…It enhances the locust-inspired algorithm by integrating SLA-awareness and adaptive host classification and is evaluated using real workload traces in the CloudSim toolkit. …”
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    Article
  14. 14

    Single classifer vs. ensemble machine learning approaches for mental health prediction by Jetli Chung, Jason Teo

    Published 2023
    “…As such, this study aims to empirically evaluate several popular machine learning algorithms in classifying and predicting mental health problems based on a given data set, both from a single classifier approach as well as an ensemble machine learning approach. …”
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    Article
  15. 15

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. …”
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    Thesis
  16. 16

    Feature fusion using a modified genetic algorithm for face and signature recognition system by Suryanti, Awang

    Published 2015
    “…Balance of the combined features has not been assessed whereas it is essential to ensure one of the biometrics does not dominate accuracy performance. To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. …”
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    Thesis
  17. 17

    Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi by Sherksi, Nasereddin Ibrahim

    Published 2020
    “…The successfully achieved four set objectives inclusive of (1) a new classification model to detect water leakage, (2) analysis of the effects of leakage size on the variables within a WDS, i.e. flow, pressure, pipe volume, velocity and water demand, (3) locating and specifying the leakage size in the WDS, and (4) evaluate the performance of the designed K-NN algorithm for accurate leak detection. …”
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    Thesis
  18. 18

    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

    Published 2019
    “…Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. …”
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    Thesis
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    Stress Classification using Deep Learning with 1D Convolutional Neural Networks by Abdulrazak Yahya, Saleh, Khai Xian, Lau

    Published 2021
    “…The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. …”
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    Article