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  1. 1
    Detection of Fraud and Non-Fraud Transaction using Machine Learning Algorithm

    Detection of Fraud and Non-Fraud Transaction using Machine Learning Algorithm by Sujang Karing, Darryl

    Published 2022
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    Final Year Project
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  2. 2
    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm

    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

    Published 2023
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  3. 3
    Fraud detection in shipping industry based on location using machine learning comparison techniques

    Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

    Published 2023
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  4. 4
    Credit Card Fraud Detection Using AdaBoost and Majority Voting

    Credit Card Fraud Detection Using AdaBoost and Majority Voting by Randhawa, Kuldeep, Loo, Chu Kiong, Seera, Manjeevan, Lim, Chee Peng, Nandi, Asoke K.

    Published 2018
    “…In this paper, machine learning algorithms are used to detect credit card fraud. …”
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  5. 5
    Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques

    Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques by Gasim, Esraa Faisal Malik

    Published 2023
    “…The second contribution to this research is to develop multiple hybrid machine learning models in order to enhance the detection of fraudulent activities in the credit card fraud detection domain.…”
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  6. 6
    A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques

    A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques by Subramaniam G.A.L., Mahmoud M.A., Abdulwahid S.N., Gunasekaran S.S.

    Published 2025
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  7. 7
    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh

    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

    Published 2019
    “…Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. …”
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  8. 8
    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo

    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo by Terence, Yong Koon Beh, Swee, Chuan Tan, Hwee, Theng Yeo

    Published 2014
    “…This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. …”
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  9. 9
    Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

    Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms by Lei, X., Mohamad, U.H., Sarlan, A., Shutaywi, M., Daradkeh, Y.I., Mohammed, H.O.

    Published 2022
    “…In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. …”
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  10. 10
    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
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  11. 11
    A comparative analysis of anti-phishing website techniques: identifying optimal approaches to enhance cybersecurity

    A comparative analysis of anti-phishing website techniques: identifying optimal approaches to enhance cybersecurity by Yau, Jia Xin

    Published 2023
    “…The study evaluates and compares the efficacy of multiple machine learning algorithms, including the Autoencoder classifier, Extreme Gradient Boost (XGBoost), and Random Forest classifier, using metrics such as accuracy, precision, recall, and F1-score. …”
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  12. 12
    An enhanced android botnet detection approach using feature refinement

    An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

    Published 2019
    “…The experimental and statistical tests show that 97.28% accuracy achieved by Random Forest machine classifier, it performs well as compared to other classification algorithms. …”
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  13. 13
    Intelligent deep machine learning cyber phishing URL detection based on BERT features extraction

    Intelligent deep machine learning cyber phishing URL detection based on BERT features extraction by Muna Elsadig, Ashraf Osman Ibrahim Elsayed, Shakila Basheer, Manal Abdullah Alohali, Sara Alshunaifi, Haya Alqahtani, Nihal Alharbi, Wamda Nagmeldin

    Published 2022
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  14. 14
    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
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  15. 15
    Efficient ML technique in blockchain-based solution in carbon credit for mitigating greenwashing

    Efficient ML technique in blockchain-based solution in carbon credit for mitigating greenwashing by Raja Segaran, Bama, Mohd Rum, Siti Nurulain, Hafez Ninggal, Mohd Izuan, Mohd Aris, Teh Noranis

    Published 2025
    “…However, while blockchain ensures transparency, it lacks real-time anomaly detection capabilities. ML algorithms, particularly supervised models such as Random Forest, XGBoost, and Neural Networks, are well-suited for detecting fraudulent patterns and verifying the authenticity of forest carbon credit transactions. …”
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  16. 16
    An Educational Tool Aimed at Learning Metaheuristics

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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  17. 17
    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
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  18. 18
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  19. 19
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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  20. 20
    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
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