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  1. 1
    Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

    Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2014
    “…In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. …”
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    Proceeding Paper
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  2. 2
    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
    “…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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  3. 3
    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach

    An integrated anomaly intrusion detection scheme using statistical, hybridized classifiers and signature approach by Mohamed Yassin, Warusia

    Published 2015
    “…As ADS can process massive volumes of packets, the amount of processing time needed to discover the pattern of the packets is also increased accordingly and resulting in late detection of the attack packets. …”
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  4. 4
    Network intrusion detection and alert system

    Network intrusion detection and alert system by To, Jin Yi

    Published 2024
    “…Signature-based detection compares network traffic packets with a real-time updated database of known attack patterns, while anomaly-based detection algorithms learn normal behavior patterns and identify deviations. …”
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  5. 5
    Non-invasive pathological voice classifications using linear and non-linear classifiers

    Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

    Published 2010
    “…In the second experiment, the detection of the specific type of voice disorders has been carried out through twoclass pattern classification problems. …”
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  6. 6
    A lightweight graph-based pattern recognition scheme in mobile ad hoc networks.

    A lightweight graph-based pattern recognition scheme in mobile ad hoc networks. by Raja Mahmood, Raja Azlina, Muhamad Amin, Anang Hudaya, Amir, Amiza, Khan, Asad I.

    Published 2012
    “…Both algorithms show comparable detection results. Thus, the lightweight, low computation DHGN based detection scheme offers an effective security solution in MANETs.…”
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  7. 7
    Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection

    Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection by Ahmed Khan, Fazeel, Abubakar, Adamu

    Published 2024
    “…This study develops and analyze using neural network-based models for effective classification of data packets using the DSCP header field. …”
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  8. 8
    Source code classification using latent semantic indexing with structural and frequency term weighting

    Source code classification using latent semantic indexing with structural and frequency term weighting by Yusof, Yuhanis, Alhersh, Taha, Mahmuddin, Massudi, Mohamed Din, Aniza

    Published 2012
    “…Furthermore,it is also learned that the use of structural information in the weighting scheme contribute to a better classification.…”
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  9. 9
    A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

    A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals by Ong, Pauline, Zainuddin, Zarita, Kee, Huong Lai

    Published 2018
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  10. 10
    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain by Ong, Pauline, Tieh, Tony Hieng Cai, Lai, Kee Huong, Lee, Woon Kiow, Ismon, Maznan

    Published 2019
    “…Second, the MFO algorithm was utilised to select the optimal discriminative features. …”
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  11. 11
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
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  12. 12
    Chain coding and pre processing stages of handwritten character image file

    Chain coding and pre processing stages of handwritten character image file by Suliman, Azizah, Sulaiman, Md. Nasir, Othman, Mohamed, O. K. Rahmat, Rahmita Wirza

    Published 2010
    “…This paper discusses in detail some of the algorithms used in the pre-processing stages of an offline handwritten character image file. …”
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  13. 13
    A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure‑free electroencephalography signals

    A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure‑free electroencephalography signals by Ong, Pauline, Zainuddin, Zarita, Kee, Huong Lai

    Published 2017
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  14. 14
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum, A., Malik, A.S., Saad, N.M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T.A., Bin Jafaar, M.Y.

    Published 2017
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  15. 15
    Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

    Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification by Nisar, Humaira, Malik, Aamir Saeed, Choi, Tae-Sun

    Published 2012
    “…This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. …”
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  16. 16
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum A., Malik A.S., Saad N.M., Iqbal M., Faris Abdullah M., Rasheed W., Rashid Abdullah T.A., Bin Jafaar M.Y.

    Published 2023
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  17. 17
    Crow Search Freeman Chain Code (CS-FCC) feature extraction algorithm for handwritten character recognition

    Crow Search Freeman Chain Code (CS-FCC) feature extraction algorithm for handwritten character recognition by Muhammad Arif, Mohamad, Zalili, Musa, Amelia Ritahani, Ismail

    Published 2023
    “…The algorithm experiments are carried out using the chain code representation created from previous research of the Centre of Excellence for Document Analysis and Recognition (CEDAR) dataset, which consists of 126 upper-case letter characters. …”
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    Conference or Workshop Item
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  18. 18
    Maldroid- attribute selection analysis for malware classification

    Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

    Published 2019
    “…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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  19. 19
    Software Classification Using Structure-Based Descriptors

    Software Classification Using Structure-Based Descriptors by Ramadan, Qusai Hussein

    Published 2009
    “…This work includes the use of structure information contained in source code programs to automate program classification. …”
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  20. 20
    Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest

    Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest by Ghulam Hussain, Muhammad Thaqif, Shafeeq Lone, Aman, Maspo, Nur-Adib, Attarbashi, Zainab

    Published 2026
    “…The framework analyzes extracted traffic features, including packet length and IP address patterns, to detect deviations from normal behaviour without requiring labelled data. …”
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