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  1. 1
    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

    Published 2024
    “…The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. …”
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  2. 2
    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]

    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

    Published 2023
    “…To improve the detection accuracy for future research, it is suggested that the malware dataset be enhanced using several architectures, such as Linux and Android, and use additional supervised and unsupervised machine learning algorithms.…”
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  3. 3
    A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works

    A Recent Research on Malware Detection Using Machine Learning Algorithm: Current Challenges and Future Works by Gorment N.Z., Selamat A., Krejcar O.

    Published 2023
    Conference Paper
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  4. 4
    A new SMS spam detection method using both Content-Based and non Content-Based features

    A new SMS spam detection method using both Content-Based and non Content-Based features by Sulaiman N.F., Jali M.Z.

    Published 2024
    Conference Paper
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  5. 5
    Early detection of heart disease using Random Forest Algorithm / Muhammad Iqbal Suhaidin ... [et al.]

    Early detection of heart disease using Random Forest Algorithm / Muhammad Iqbal Suhaidin ... [et al.] by Suhaidin, Muhammad Iqbal, Mohamed Yusoff, Syarifah Adilah, Johan, Elly Johana, Mydin, Azlina, Wan Mohamad, Wan Anisha

    Published 2023
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  6. 6
    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system

    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

    Published 2022
    “…Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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  7. 7
    A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions

    A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions by Tao, Stefanus Hwa Kieu, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, Hoshang Kolivand

    Published 2020
    “…This paper presents a survey of deep learning for lung disease detection in medical images. …”
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  8. 8
    A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection

    A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection by Bohani F.A., Suliman A., Saripuddin M., Sameon S.S., Md Salleh N.S., Nazeri S.

    Published 2023
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  9. 9
    An improved diagnostic algorithm based on deep learning for ischemic stroke detection in posterior fossa

    An improved diagnostic algorithm based on deep learning for ischemic stroke detection in posterior fossa by Muhd Suberi, Anis Azwani

    Published 2020
    “…Therefore, a new diagnostic algorithm based on DL is proposed for ischemic stroke detection in PF. …”
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  10. 10
    Wearable based-sensor fall detection system using machine learning algorithm

    Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

    Published 2021
    “…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
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  11. 11
    DEVELOPMENT OF DRIVER DROWSINESS DETECTION ALGORITHM

    DEVELOPMENT OF DRIVER DROWSINESS DETECTION ALGORITHM by YVONNE, PHUA YEE WUN

    Published 2022
    “…The modified LeNet-5 achieved the highest accuracy of 92.22% among the four proposed algorithms. It sets a great benchmark for future work on driver drowsiness detection. …”
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    Final Year Project Report / IMRAD
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  12. 12
    Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system

    Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system by Balla, Asaad, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Mubarak, Sinil

    Published 2022
    “…In this paper, we have examined and presented the most recent research on developing robust IDSs using Deep Learning (DL) algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Stacked Autoencoders (SAE), and Deep Belief Networks (DBN). …”
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  13. 13
    Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

    Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges by Ridwan M.A., Radzi N.A.M., Abdullah F., Jalil Y.E.

    Published 2023
    “…Intrusion detection; Machine learning; Quality of service; Complex problems; Computational model; Future challenges; Improve performance; Ml algorithms; Performance issues; Resource management; Rule based algorithms; Complex networks…”
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  14. 14
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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  15. 15
    A new mobile botnet classification based on permission and API calls

    A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

    Published 2024
    Conference Paper
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  16. 16
    The development of a tracking algorithm for ambulance detection using squaring of RGB and HSV color processing techniques

    The development of a tracking algorithm for ambulance detection using squaring of RGB and HSV color processing techniques by Mohammad Syawaludin Syafiq, Hassan

    Published 2016
    “…In the future, it is expected that the detection from a tracking algorithm will automatically switch the traffic light signals based on the road conditions. …”
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  17. 17
    Detection of DDoS attacks in IoT networks using machine learning algorithms

    Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

    Published 2025
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    Proceeding Paper
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  18. 18
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
    “…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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  19. 19
    Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan

    Early autism spectrum disorder detection using machine learning / Muhammad Shahmi Shahron Nizan by Shahron Nizan, Muhammad Shahmi

    Published 2023
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  20. 20
    Autism Spectrum Disorder Classification Using Deep Learning

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.…”
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