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  1. 1
    Anomaly-based intrusion detection through K-means clustering and naives Bayes classification

    Anomaly-based intrusion detection through K-means clustering and naives Bayes classification by Mohamed Yassin, Warusia, Udzir, Nur Izura, Muda, Zaiton, Sulaiman, Md. Nasir

    Published 2013
    “…Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate. Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. …”
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  2. 2
    Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification

    Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification by Yassin, Warusia, Udzir, Nur Izura, Muda, Zaiton, Sulaiman, Md Nasir

    Published 2013
    “…Regrettably, the foremost challenge of this method is to minimize false alarm while maximizing detection and accuracy rate.We propose an integrated machine learning algorithm across K-Mean s clustering and Naïve Bayes Classifier called KMC+NBC to overcome the aforesaid drawbacks.K-Means clustering is applied to labeling and gathers the entire data into corresponding cluster sets based on the data behavior,i.e.…”
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  3. 3
    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks

    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks by Ihsanto, Eko, Ramli, Kalamullah, Sudiana, Dodi, Gunawan, Teddy Surya

    Published 2020
    “…Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. …”
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  4. 4
    Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves

    Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

    Published 2024
    “…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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  5. 5
    Handgrip strength evaluation using neuro fuzzy approach

    Handgrip strength evaluation using neuro fuzzy approach by Seng, W.C., Chitsaz, M.

    Published 2010
    “…Multilevel Perception neural network utilizes the back-propagation learning algorithm is suitable to discover relationships and patterns in the dataset. …”
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  6. 6
    The new efficient and accurate attribute-oriented clustering algorithms for categorical data

    The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

    Published 2012
    “…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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  7. 7
    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams

    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams by Zakaria, Neili, Sundaraj, Kenneth

    Published 2023
    “…Breathing sounds are a rich source of information that can assist doctors in diagnosing pulmonary diseases in a non-invasive manner. Several algorithms can be developed based on these sounds to create an automatic classification system for lung diseases. …”
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  8. 8
    Development of an Isolated Digit Speech Recognition Based on Multilayer Perceptron Model

    Development of an Isolated Digit Speech Recognition Based on Multilayer Perceptron Model by Mohamad Hussin, Ummu Salmah

    Published 2004
    “…Another contribution in the preprocessing phase is in normalization phase. Three normalization methods are introduced to normalize the speech data before propagating to NN. …”
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  9. 9
    Deep learning-based item classification for retail automation

    Deep learning-based item classification for retail automation by Ling, Ji Xiang

    Published 2025
    “…This project focuses on developing a deep learning-based system for retail item classification. …”
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  10. 10
    Multistage quality control in manufacturing process using blockchain with machine learning technique

    Multistage quality control in manufacturing process using blockchain with machine learning technique by Gu, J., Zhao, L., Yue, X., Arshad, N.I., Mohamad, U.H.

    Published 2023
    “…To this goal, a variety of machine learning algorithms are being studied. Data protection and monitoring is also another concern that is a critical component of the organization. …”
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  11. 11
    Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

    Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

    Published 2020
    “…In order to achieve this, several data pre-processing method is implemented to improve the model performance. …”
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  12. 12
    Incremental learning for large-scale stream data and its application to cybersecurity

    Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

    Published 2015
    “…These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. …”
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  13. 13
    Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

    Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models. by Kwad, Ayad Mahmood

    Published 2022
    “…The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. …”
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  14. 14
    Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour

    Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour by Neyamadpour, Ahmad

    Published 2010
    “…In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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  15. 15
    Improving K-Means Clustering using discretization technique in Network Intrusion Detection System

    Improving K-Means Clustering using discretization technique in Network Intrusion Detection System by Tahir, H.M., Said, A.M., Osman, N.H., Zakaria, N.H., Sabri, P.N.M., Katuk, N.

    Published 2016
    “…Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K-Means Clustering with discretization technique and Naïve Bayes Classifier (KMC-D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation Dataset. …”
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  16. 16
    Malware Detection using Deep Learning (DL)

    Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

    Published 2024
    journal::journal article
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  17. 17
    An enhanced gated recurrent unit with auto-encoder for solving text classification problems

    An enhanced gated recurrent unit with auto-encoder for solving text classification problems by Zulqarnain, Muhammad

    Published 2020
    “…Classification has become an important task for categorizing documents automatically based on their respective groups. Gated Recurrent Unit (GRU) is a type of Recurrent Neural Networks (RNNs), and a deep learning algorithm that contains update gate and reset gate. …”
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  18. 18
    Algorithm enhancement for host-based intrusion detection system using discriminant analysis

    Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

    Published 2004
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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  19. 19
    A study on advanced statistical analysis for network anomaly detection

    A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

    Published 2005
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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  20. 20
    Pengkelasan Sel Kanser Pangkal Rahim Kepada Sel Normal Dan Tidak Normal Menggunakan Analisis Pembezalayan Dan Rangkaian Neural

    Pengkelasan Sel Kanser Pangkal Rahim Kepada Sel Normal Dan Tidak Normal Menggunakan Analisis Pembezalayan Dan Rangkaian Neural by Saidin, Mohammad Norrish

    Published 2006
    “…The system is built to classify some certain data into two classes, which are normal or abnormal cells. …”
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