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  1. 1

    Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer by Tuerxun, Adilijiang

    Published 2017
    “…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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    Thesis
  2. 2

    Comparative analysis for topic classification in juz Al-Baqarah by Rahman, Mohamad Izzuddin, Samsudin, Noor Azah, Mustapha, Aida, Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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    Article
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    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. …”
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    Article
  5. 5

    A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

    Published 2020
    “…Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. …”
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    Article
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    Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector by Wang G., Mukhtar A., Moayedi H., Khalilpoor N., Tt Q.

    Published 2025
    “…These findings underscore the importance of algorithm selection in optimizing predictive models for energy consumption forecasting. …”
    Article
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    A new soft computing model for daily streamflow forecasting by Sammen S.S., Ehteram M., Abba S.I., Abdulkadir R.A., Ahmed A.N., El-Shafie A.

    Published 2023
    “…Climate change; Data streams; Floods; Forecasting; Genetic algorithms; Hydroelectric power plants; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Soft computing; Soil conservation; Water conservation; Water management; Water supply; Flow quantification; Multi layer perceptron; Optimization algorithms; Predicting models; Root mean square errors; Soft computing models; Streamflow forecasting; Watershed management; Stream flow; algorithm; forecasting method; hydroelectric power plant; numerical model; optimization; principal component analysis; streamflow; watershed; Helianthus…”
    Article
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    New bio-inspired barnacle optimizers based least-square support vector machine for time-series prediction of pandemic outbreaks by Marzia, Ahmed

    Published 2024
    “…Firstly, this thesis proposes hybrid optimized machine learning models by combining improved BMO variants with Least Squares Support Vector Machines (LSSVM) to effectively capture intricate temporal patterns. …”
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    Thesis
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    An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system by M. W., Mustafa, H., Shareef, M. H., Sulaiman, S. N., Abd. Khalid, S. R., Abd. Rahim, Omar, Aliman

    Published 2011
    “…The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. …”
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    Conference or Workshop Item
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    Hybrid least squares support vector machines for short term predictive analysis by Zuriani, Mustaffa, Ernawan, Ferda, M. H., Sulaiman, Syafiq Fauzi, Kamarulzaman

    Published 2017
    “…Moth-flame Optimization (MFO) algorithm is a relatively new optimization algorithm which is classified as Swarm Intelligence (SI). …”
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    Flock optimization algorithm-based deep learning model for diabetic disease detection improvement by Balasubramaniyan, Divager, Husin, Nor Azura, Mustapha, Norwati, Mohd Sharef, Nurfadhlina, Mohd Aris, Teh Noranis

    Published 2024
    “…Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. …”
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    Article