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  1. 1
    Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification

    Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification by Wong S.Y., Yap K.S., Zhai Q., Li X.

    Published 2023
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  2. 2
    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition

    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

    Published 2002
    “…The rough sets vector quantization model proved its usefulness in the speech recognition framework, however it can be extended to different applications that involve large amounts of data such as speaker verification.…”
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  3. 3
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  4. 4
    Development of optimized damage prediction method for health monitoring of ultra high performance fiber-reinforced concrete communication tower

    Development of optimized damage prediction method for health monitoring of ultra high performance fiber-reinforced concrete communication tower by Gatea, Sarah Jabbar

    Published 2018
    “…The results are used to develop the hybrid learning algorithm based on the AdaBoost, Bagging, and RUSBoost methods to predict the damage in the tower based on dynamic frequency domain. …”
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  5. 5
    A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru

    A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru by Wang, Yanru

    Published 2018
    “…In addition, ANFIS uses hybrid learning algorithm. It is mixed with least mean square and gradient descent method, which cause many advantages, such as much better learning ability and less computational time. …”
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  6. 6
    Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method

    Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method by Tehrany, Mahyat Shafapour, Pradhan, Biswajeet, Jebur, Mustafa Neamah

    Published 2015
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  7. 7
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…In line with the No Free Lunch theorem which suggests that no single metaheuristic is the best for all optimization problems, the search for better algorithms is still a worthy endeavour. Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic algorithm which is appealing to researcher owing to its demonstrated performance as cited in the scientific literature. …”
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  8. 8
    Deep learning-based single-shot and real-time vehicle detection and ego-lane estimation

    Deep learning-based single-shot and real-time vehicle detection and ego-lane estimation by Abdul Matin, M. A. A., Ahmad Fakhri, A. S., Mohd Zaki, Hasan Firdaus, Zainal Abidin, Zulkifli, Mohd Mustafah, Y., Abd Rahman, H., Mahamud, N. H., Hanizam, S., Ahmad Rudin, N. S.

    Published 2020
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  9. 9
    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method

    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

    Published 2024
    thesis::doctoral thesis
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  10. 10
    Multi-stage feature selection in identifying potential biomarkers for cancer classification

    Multi-stage feature selection in identifying potential biomarkers for cancer classification by Wong, Yit Khee, Chan, Weng Howe, Nies, Hui Wen, Moorthy, Kohbalan

    Published 2022
    “…Besides conducting laboratory analysis, potential biomarkers also can be identified by analysing gene expression data through feature selection and machine learning. Many algorithms have been applied and introduced in this area, yet the challenge of high dimensionality of gene expression data remains and it could lead to the existence of noise that could negatively impact the analysis outcome. …”
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