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  1. 1
    A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment

    A Proposed False Report Identification Algorithm for a Mobile Application in the IoT Environment by Rajoo, S, Magalingam, P, Idris, NB, Samy, GN, Maarop, N, Shanmugam, B, Perumal, S

    Published 2024
    Proceedings Paper
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  2. 2
    Named entity recognition using a new fuzzy support vector machine.

    Named entity recognition using a new fuzzy support vector machine. by Mansouri, Alireza, Affendy, Lilly Suriani, Mamat, Ali

    Published 2008
    “…Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. …”
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  3. 3
    Intelligent cooperative web caching policies for media objects based on J48 decision tree and Naive bayes supervised machine learning algorithms in structured peer-to-peer systems

    Intelligent cooperative web caching policies for media objects based on J48 decision tree and Naive bayes supervised machine learning algorithms in structured peer-to-peer systems by Ibrahim, Hamidah, Yasin, Waheed, Udzir, Nur Izura, Abdul Hamid, Nor Asilah Wati

    Published 2016
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  4. 4
    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier

    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  5. 5
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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    Final Year Project
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  6. 6
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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    Final Year Project
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  7. 7
    Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram

    Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram by Faris Francis Singaram, Fareena

    Published 2021
    “…The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. …”
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    Thesis
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  8. 8
    Defect green coffee bean detection using image recognition and supervised learning

    Defect green coffee bean detection using image recognition and supervised learning by Shafian Izan Sofian

    Published 2022
    “…The model was integrated as web application with Flask where user can upload the image and the system will return result with precision and prediction. …”
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    Academic Exercise
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  9. 9
    Entiment analysis of public perception on AI chatbots using Support Vector Machine (SVM) algoritm / Tuan Nur Azlina Tuan Ibrahim

    Entiment analysis of public perception on AI chatbots using Support Vector Machine (SVM) algoritm / Tuan Nur Azlina Tuan Ibrahim by Tuan Ibrahim, Tuan Nur Azlina

    Published 2024
    “…Facing challenges with existing methods, the Support Vector Machine (SVM) algorithm is employed for its proficiency in handling textual data. …”
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  10. 10
    Content-based recommender system for an academic social network / Vala Ali Rohani

    Content-based recommender system for an academic social network / Vala Ali Rohani by Vala Ali, Rohani

    Published 2014
    “…The algorithm exploits all interests and preferences in a hierarchy tree structure. …”
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    Thesis
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  11. 11
    Mixed variable ant colony optimization technique for feature subset selection and model selection

    Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  12. 12
    Forest mapping in Peninsular Malaysia using Random Forest and Support Vector Machine Classifiers on Google Earth Engine

    Forest mapping in Peninsular Malaysia using Random Forest and Support Vector Machine Classifiers on Google Earth Engine by Farah Nuralissa Muhammad, Lam, Kuok Choy

    Published 2023
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  13. 13
    Age prediction on face features via multiple classifiers

    Age prediction on face features via multiple classifiers by Mohamad, F.S., Iqtait, M., Alsuhimat, F.

    Published 2018
    “…We were able to recognize that the accuracy of SVR algorithm is better than the accuracy of KNN and SVM classifiers.…”
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  14. 14
    Classification of basal stem rot disease in oil palm using dielectric spectroscopy

    Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

    Published 2018
    “…For feature selection algorithms, SVM-FS model gave the best classification accuracies compared to GA and RF; ranged from 81.82% to 88.64% with SVM and kNN as the best classifiers. …”
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  15. 15
    Improving Support Vector Machine Performance using Modified Similarity Distance Plotting-Data Reduction

    Improving Support Vector Machine Performance using Modified Similarity Distance Plotting-Data Reduction by Abdul Muqtasid, Rushdi, Mohammad, Hossin, Norita, Norwawi

    Published 2025
    “…The Support Vector Machine (SVM) is well-regarded for its high classification accuracy, but its computational efficiency is often challenged by large datasets. …”
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  16. 16
    Analysing machine learning models to detect disaster events using social media

    Analysing machine learning models to detect disaster events using social media by Faris Azni Azlan, Mr.

    Published 2023
    text::Thesis
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  17. 17
    Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data

    Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data by Mohammad Aman Ullah

    Published 2024
    thesis::doctoral thesis
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  18. 18
    An Automated System For Classifying Conference Papers

    An Automated System For Classifying Conference Papers by Ngan, Seon Choon Han

    Published 2021
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    Final Year Project / Dissertation / Thesis
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  19. 19
    Gender identification using support vector machines

    Gender identification using support vector machines by Nur Ayuni Binti Jalaluddin

    Published 2023
    “…This thesis is introduces SVM theory application and its algorithmic implementations.…”
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  20. 20
    Support vector machine in precision agriculture: a review

    Support vector machine in precision agriculture: a review by Kok, Zhi Hong, Mohamed Shariff, Abdul Rashid, M. Alfatni, Meftah Salem, Bejo, Siti Khairunniza

    Published 2021
    “…The applications of SVM in precision agriculture (PA) are compared by identifying its interactions with variables, comparing its model performance, highlighting its strengths and weaknesses, as well as suggestions for improvements. …”
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