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Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A... by Hamsani, Aqil Khairy, Mohd Bahrin, Ummu Fatihah, Wan Abdul Manan, Wan Dorishah

“…The problem statement revolves around the need for an efficient approach to classify application reviews to their sentiments, such as the user’s downloaded application not working as intended, and it can take time for users to read every single review. The project's objectives are to study the classifying approach of Google Play Store application reviews using the Decision Tree algorithm, develop a prototype of a classifying application program, and evaluate the accuracy model of the review classification program. …”
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Design and performance analysis of a fast 4-way set associative cache controller using Tree Pseudo Least Recently Used algorithm by Hazlan, Mohamed Alfian Al-Zikry, Gunawan, Teddy Surya, Yaacob, Mashkuri, Kartiwi, Mira, Arifin, Fatchul

“…A key feature of this design is the incorporation of the Tree Pseudo Least Recently Used (PLRU) algorithm for cache replacement, a strategic choice aimed at optimizing cache performance. …”
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Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins by Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

An early warning system for students at risk using supervised machine learning by Yam, Zheng Hong, Mohd Norshahriel, Abd Rani, Nabilah Filzah, Mohd Radzuan, Lim, Huay Yen, Sarasvathi, Nagalingam

“…Besides, this study also focuses on the Decision Tree, Random Forest, and XGBoost models in the system. …”
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