Search Results - (( developing theme classification algorithm ) OR ( java visualization learning algorithm ))
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Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…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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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
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Classification for Quran authentication using characters and diacritics hashed values
Published 2024journal::journal article -
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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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Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil
Published 2024“…The model will be designed and developed using a deep learning algorithm, which is YOLOv8, the latest version of YOLO. …”
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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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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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