Search Results - (( developing theme classification algorithm ) OR ( java application max algorithm ))

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  1. 1

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…To address these problems, this research proposes three new distributed static batch mode inspired algorithms. The first (proposed) algorithm is based on Min-Min, called Min-Diff, the second algorithm is based on Max-Min, called Max-Average, and the third algorithm is to handle the load balancing, called Efficient Load Balancing (ELB). …”
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    Thesis
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    Evaluating the usability of a Quranic theme extraction and visualization system using task-based usability testing by Azuan, Alya Maisarah

    Published 2025
    “…This study presents a task-based usability evaluation of a Quranic Theme Extraction and Visualization System, which integrates Natural Language Processing (NLP) techniques which are RAKE algorithm for keyword extraction and DistilBERT for theme classification. …”
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    Article
  5. 5

    Prediction of breast cancer diagnosis using machine learning in Malaysian women by Mokhtar, Tengku Muhammad Hanis Tengku

    Published 2024
    “…The three frequently used ML algorithms were deep learning, support vector machine (SVM), and cluster analysis. …”
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    Thesis
  6. 6

    Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim by Nulkasim @ Mohd Kassim, Liliana

    Published 2017
    “…A random forest classifier algorithm is employed in Spyder IDE using python language as a machine learning language for predict the type of an audio. …”
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    Thesis
  7. 7

    Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

    Published 2025
    “…Among these, Naive Bayes achieved the highest performance with an accuracy of 93.67% and an F1-score of 93.54%, making it the most effective model for sentiment classification in this context. The results were visualized using an interactive dashboard developed in Power BI, allowing users to explore sentiment trends, keyword frequency, and review distributions by gender, platform, and time. …”
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    Student Project