Search Results - (( developing theme classification algorithm ) OR ( java code classification algorithm ))

Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. …”
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Comparative analysis of text classification algorithms for automated labelling of quranic verses by Adeleke, Abdullah, Samsudin, Noor Azah, Mustapha, Aida, Mohd Nawi, Nazri

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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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Classification for Quran authentication using characters and diacritics hashed values by Farida Hazwani Mohd Ridzuan, Madihah Mohd Saudi, Shirzad Z., Azni A.H.

Evaluating the usability of a Quranic theme extraction and visualization system using task-based usability testing by Azuan, Alya Maisarah

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

“…The three frequently used ML algorithms were deep learning, support vector machine (SVM), and cluster analysis. …”
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Identification of Quran recitation segment from speech video recording / Liliana Nulkasim @ Mohd Kassim by Nulkasim @ Mohd Kassim, Liliana

“…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 by Abdullah, Nurulain

“…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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