Search Results - (( code classification rules algorithm ) OR ( evolution optimisation based algorithm ))

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  1. 1

    Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2014
    “…This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. …”
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    Proceeding Paper
  2. 2

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
    “…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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    Article
  3. 3
  4. 4

    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
    “…This research proposed an algorithm named Genetic Algorithm Fuzzy Logic (GAFL) with Pittsburg approach for rules learning and induction in genetic fuzzy system knowledge discovery. …”
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    Article
  5. 5

    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
    “…This research proposed an algorithm named Genetic Algorithm Fuzzy Logic (GAFL) with Pittsburg approach for rules learning and induction in genetic fuzzy system knowledge discovery. …”
    Article
  6. 6

    Chain coding and pre processing stages of handwritten character image file by Suliman, Azizah, Sulaiman, Md. Nasir, Othman, Mohamed, O. K. Rahmat, Rahmita Wirza

    Published 2010
    “…Fuzzy Logic is used in the classification phase while HMM is used in the process of extracting features for the preparation of linguistic variables of the fuzzy rules. …”
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    Article
  7. 7

    Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

    Published 2014
    “…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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    Article
  8. 8

    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

    Published 2018
    “…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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    Thesis
  9. 9

    POWER QUALITY CLASSIFICATION WITH DE-NOISING SCHEME USING WAVELET TRANSFORM AND RULE- BASED METHOD by HENG KEOW, CHUAH

    Published 2012
    “…Unique features from the I", 4t h ,7th and 8thl evel details are obtained as criteria for developing a Rules-Based Algorithm for classifying disturbances that have occurred. …”
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    Thesis
  10. 10

    A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

    Published 2026
    “…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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    Article
  11. 11

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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    Article
  12. 12

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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    Article
  13. 13
  14. 14

    Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

    Published 2021
    “…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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    Book Section
  15. 15

    B-spline curve fitting with different parameterization methods by Kheng, Jia Shen

    Published 2020
    “…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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    Final Year Project / Dissertation / Thesis
  16. 16

    Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods by Kadhem, Athraa Ali

    Published 2017
    “…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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    Thesis
  17. 17
  18. 18

    iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems by Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur

    Published 2024
    “…In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. …”
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    Article
  19. 19

    A self‐configured link adaptation for green LTE downlink transmission by Salman, Mustafa Ismael, Ng, Chee Kyun, Noordin, Nor Kamariah, Mohd Ali, Borhanuddin, Sali, Aduwati

    Published 2015
    “…Current and next‐generation cellular networks require such interactive techniques in order to be self‐optimised without complex modifications.…”
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    Article
  20. 20

    Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel by Amin, A. K. M. Nurul, Hafiz, A.M. Khalid, Lajis, M. A.

    Published 2011
    “…Shunmugam et al. [7] also combined RSM with differential evolution and genetic algorithms to draw a comparison between these methods. …”
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    Book Chapter