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  1. 1
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  2. 2
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A.

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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  3. 3
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Ahmed Dheyab, Saad, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  4. 4
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2022
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  5. 5
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama

    Published 2023
    “…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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  6. 6
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  7. 7
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  8. 8
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    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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  9. 9
    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…Another structure of MLP trained using backpropagation algorithm is used to detect and locate the base of the young corn tree using the skeleton of the segmented image. …”
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  10. 10
    Assessment of cognitive load using multimedia learning and resting states with deep learning perspective

    Assessment of cognitive load using multimedia learning and resting states with deep learning perspective by Qayyum, A., Faye, I., Malik, A.S., Mazher, M.

    Published 2019
    “…It is a well-understood fact that the brain activity increases with the increased demand of cognition. The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. …”
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  11. 11
    Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device

    Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device by Shanmuga, Pillai A/L Murutha Muthu

    Published 2021
    “…SAX algorithm changes the original time series data into a symbolic string and perform the discretization by dividing a time series into equal-sized segments. …”
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  12. 12
    Machine Learning and Dyslexia-Diagnostic and Classification System (DCS) for Kids with Learning Disabilities

    Machine Learning and Dyslexia-Diagnostic and Classification System (DCS) for Kids with Learning Disabilities by Rehman Ullah, Khan, Julia Ai Cheng, Lee, Oon, Yin Bee

    Published 2018
    “…This research proposes such a machine learning based diagnostic and classification system. …”
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  13. 13
    A review of feature selection on text classification

    A review of feature selection on text classification by Nur Syafiqah, Mohd Nafis, Suryanti, Awang

    Published 2018
    “…However, in filter approach, the classification accuracies cannot be guaranteed because it does not incorporate with any learning algorithm. …”
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  14. 14
    Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms

    Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms by Shaharum, Nur Shafira Nisa, Mohd Shafri, Helmi Zulhaidi, Wan Ab. Karim Ghani, Wan Azlina, Samsatli, Sheila, Al-Habshi, Mohammed Mustafa, Yusuf, Badronnisa

    Published 2020
    “…In this study, 30 m Landsat 8 data were processed using a cloud computing platform of Google Earth Engine (GEE) in order to classify oil palm land cover using non-parametric machine learning algorithms such as Support Vector Machine (SVM), Classification and Regression Tree (CART) and Random Forest (RF) for the first time over Peninsular Malaysia. …”
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  15. 15
    Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI

    Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI by Ramli, Zarina

    Published 2024
    “…This study investigates the efficacy of staging classification using diffusion-weighted imaging magnetic resonance imaging (DWIMRI) through radiomic analysis and machine learning. …”
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  16. 16
    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification

    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

    Published 2023
    “…Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. …”
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  17. 17
    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 Whale optimization algorithm-Variational mode decomposition is used to optimally decompose the baby cry signals. …”
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  18. 18
    The classification of skateboarding trick manoeuvres: A K-nearest neighbour approach

    The classification of skateboarding trick manoeuvres: A K-nearest neighbour approach by Muhammad Ar Rahim, Ibrahim, Muhammad Amirul, Abdullah, Muhammad Nur Aiman, Shapiee, Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa, Muhammad Aizzat, Zakaria, Noor Azuan, Abu Osman, Anwar P. P., Abdul Majeed

    “…A variation of k-NN algorithms were tested based on the number of neighbours, as well as the weight and the type of distance metric used. …”
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  19. 19
    Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying

    Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying by Khaw , Hui Ying

    Published 2019
    “…In order to reduce the training time and computational cost of the algorithm, Principal Components Analysis (PCA) pretraining strategy is deployed to obtain data adaptive filter banks. …”
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  20. 20
    Cyber parental control framework for objectionable web content classification and filtering based on topic modelling using enhanced latent dirichlet allocation / Hamza H. M. Altarturi

    Cyber parental control framework for objectionable web content classification and filtering based on topic modelling using enhanced latent dirichlet allocation / Hamza H. M. Altart... by Hamza H. M. , Altarturi

    Published 2023
    “…Despite substantial advancements in automating web classification that combines web mining and content classification methods, the study identifies a gap in applying advanced machine learning algorithms for superior objectionable web content classification. …”
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