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  1. 1
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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  2. 2
    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

    Published 2025
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  3. 3
    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection

    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

    Published 2020
    “…The proposed CGWO and OBCGWO are then applied to select the relevant features from the original feature set. Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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  4. 4
    GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators

    GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators by Kun, Liu, Alli, Hassan, Abd Rahman, Khairul Aidil Azlin

    Published 2024
    “…In the era of big data, numerous predictive models are based on more redundant features, which increases the complexity of the algorithms and the difficulty of analyzing customer churn. …”
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  5. 5
    Voting algorithms for large scale fault-tolerant systems

    Voting algorithms for large scale fault-tolerant systems by Karimi, Abbas

    Published 2011
    “…By extracting various plots from different aspects, we demonstrated that, compared to the basic algorithm, our algorithm has higher reliability and availability as the quantity of the input data increases. …”
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  6. 6
    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Ramarao, Taj, Mohammed Baloch

    Published 2008
    “…The algorithms are developed using MATLABTM software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytemsTM, and the spectra of the fault data are analyzed using fast Fourier transform which facilitates extraction of distinct features of each type of fault. …”
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  7. 7
    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Rama Rao

    Published 2009
    “…The algorithms are developed using MATLAB software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems, and the spectra of the fault data are analyzed using fast Fourier transform to extract features of each type of fault. …”
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  8. 8
    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network

    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network by Muhammad Sidik, Siti Syatirah

    Published 2023
    “…This proposed model will be employed in the Improved Reverse Analysis method to extract the relationship between various fields of real-life data sets based on logical representation. …”
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  9. 9
    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition

    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition by Al-Qammaz, Abdullah Yousef Awwad

    Published 2019
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  10. 10
    GA Based Feature Recognition of Step File for CAD/CAM Integration

    GA Based Feature Recognition of Step File for CAD/CAM Integration by Syafnil, Alfais Admiral

    Published 2009
    “…A GA model is proposed for optimizing the coordinates which is used for feature recognition. …”
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  11. 11
    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm by Iqbal, M.J., Faye, I., Said, A.M.D., Samir, B.B.

    Published 2017
    “…A statistical metric-based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. …”
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  12. 12
    Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks

    Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks by K.S.R, Rao, F. D., Zahlay

    Published 2008
    “…The algorithms are developed using MATLABâ„¢ software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytemsâ„¢, and the spectra of the fault data are analyzed using fast Fourier transform which facilitates extraction of distinct features of each type of fault. …”
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  13. 13
    Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis

    Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis by Atik, Faysal

    Published 2022
    “…Artificial intelligence can be applied in fault feature extraction and classification. It is crucial to use an effective feature extraction method to obtain most of the fault information and a robust classifier to classify those features. …”
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  14. 14
    Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke

    Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke by Zhang , Ke

    Published 2024
    “…The deep features of decomposed components are extracted using the VGG16 model. …”
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  15. 15
    Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti

    Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti by Abd Mukti, Shahrul Nizan

    Published 2022
    “…The study set four main objectives to achieve its aim: (1) To analyse RGB and multispectral sensor calibration, (2) To evaluate the optimal flight parameters for pothole modelling production using RGB imagery, (3) To investigate various classifier algorithms and band combinations for pothole region areas using multispectral imagery and (4) To validate geometric information from the extracted pothole. …”
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  16. 16
    Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data

    Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data by Sameen, Maher Ibrahim

    Published 2018
    “…There was a need for efficient segmentation algorithm, optimization strategy, feature extraction and classification, and robust statistical and computational intelligence models to accomplish the set aims. …”
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  17. 17
    Class binarization with self-adaptive algorithm to improve human activity recognition

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…However, the learning complexity of classification is increased due to the expansion number of learning model. Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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  18. 18
    Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data

    Multi-class classification automated machine learning for predicting earthquakes using global geomagnetic field data by Qaedi, Kasyful, Abdullah, Mardina, Yusof, Khairul Adib, Hayakawa, Masashi, Zulhamidi, Nur Fatin Irdina

    Published 2025
    “…The extracted features were the input for AutoML, an automatic algorithm selection that was measured by Bayesian Optimization algorithm to select the best performance model. …”
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  19. 19
    The classification of wink-based eeg signals by means of transfer learning models

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

    Published 2021
    “…This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . …”
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  20. 20
    A new model for iris data set classification based on linear support vector machine parameter's optimization

    A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

    Published 2020
    “…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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