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  1. 1
    Classifying good and bad websites

    Classifying good and bad websites by Koo, Ee Woon

    Published 2015
    “…The classification process is made easy by using set of features generated from HTML codes. …”
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    Final Year Project Report / IMRAD
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  2. 2
    Enhancement of feature sets for subjectivity analysis on Malay-English code-switching text

    Enhancement of feature sets for subjectivity analysis on Malay-English code-switching text by Kasmuri, Emaliana

    Published 2023
    “…In the unified code-switching feature set, the extracted Malay and English features were unified using an adapted algorithm known as the Malay-English Unified POS. …”
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    Thesis
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  3. 3
    Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir

    Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

    Published 2019
    “…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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  4. 4
    Enhanced Flipping Technique to Reduce Variability in Image Steganography

    Enhanced Flipping Technique to Reduce Variability in Image Steganography by Kamil S., Abdullah S.N.H.S., Hasan M.K., Bohani F.A.

    Published 2023
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  5. 5
    Classification of metamorphic virus using n-grams signatures

    Classification of metamorphic virus using n-grams signatures by A Hamid, Isredza Rahmi, Md Sani, Nur Sakinah, Abdullah, Zubaile, Mohd Foozy, Cik Feresa, Kipli, Kuryati

    Published 2020
    “…Then, the virus cluster is evaluated using Naïve Bayes algorithm in terms of accuracy using performance metric. …”
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    Conference or Workshop Item
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  6. 6
    Discovering optimal features using static analysis and a genetic search based method for Android malware detection

    Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Firdaus, Ahmad, Anuar, Nor Badrul, Karim, Ahmad, Razak, Mohd Faizal Ab

    Published 2018
    “…To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). …”
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  7. 7
    Discovering optimal features using static analysis and a genetic search based method for Android malware detection

    Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Ahmad Firdaus, Zainal Abidin, Nor Badrul, Anuar, Ahmad, Karim, Mohd Faizal, Ab Razak

    Published 2018
    “…To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). …”
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  8. 8
    Detection of SQL injection attack using machine learning

    Detection of SQL injection attack using machine learning by Tung, Tean Thong

    Published 2024
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    Final Year Project / Dissertation / Thesis
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  9. 9
    Comparative analysis for topic classification in juz Al-Baqarah

    Comparative analysis for topic classification in juz Al-Baqarah by Rahman, Mohamad Izzuddin, Samsudin, Noor Azah, Mustapha, Aida, Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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  10. 10
    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool

    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

    Published 2018
    “…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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  11. 11
    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

    Published 2020
    “…The methodology started with the pallet-level which firstly determined by manual clustering according to the product code number of the tags that were manufactured for defining the actual level. …”
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  12. 12
    Phishing image spam classification research trends: Survey and open issues

    Phishing image spam classification research trends: Survey and open issues by John Abari, Ovye, Mohd Sani, Nor Fazlida, Khalid, Fatimah, Mohd Yunus Bin Sharum, Mohd Yunus, Mohd Ariffin, Noor Afiza

    Published 2020
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