Search Results - (( developing theme classification algorithm ) OR ( java applications usage algorithm ))
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An Effective Fast Searching Algorithm for Internet Crawling Usage
Published 2016“…The search algorithm is a crucial part in any internet applications. …”
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Conference or Workshop Item -
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Classification for Quran authentication using characters and diacritics hashed values
Published 2024journal::journal article -
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Evaluating the usability of a Quranic theme extraction and visualization system using task-based usability testing
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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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…The experimental result shows that using Java 2, this method improves the speed of PrefixSpan up to almost two orders of magnitude as well as the memory usage to more than one order of magnitude.…”
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Prediction of breast cancer diagnosis using machine learning in Malaysian women
Published 2024“…The three frequently used ML algorithms were deep learning, support vector machine (SVM), and cluster analysis. …”
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Thesis -
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Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim
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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Analyzing customer reviews for ARBA Travel using sentiment analysis
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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