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An Enhanced Random Forest (ERF)-based Machine Learning Framework for Resampling, Prediction, and Classification of Mobile Applications using Textual Features

Shahbaz HussainDepartment of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, PakistanNadeem SarwarDepartment of Computer Science, Bahria University, Lahore Campus, Lahore, PakistanArshad AliFaculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, Saudi ArabiaHamayun KhanDepartment of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, PakistanIrfanud DinDepartment of Computer Science, New Uzbekistan University, Tashkent, UzbekistanAbdullah M. AlqahtaniCollege of Engineering & Computer Science, Department of Electrical & Electronic Engineering, Jazan University, Saudi ArabiaMuhammad ShabirNetwork Security Forensic Group, School of Technology, Asia Pacifc University, MalaysiaAitizaz AliFaculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, Saudi Arabia

Engineering Technology & Applied Science Researchjournal2025en

ABI

Аннотация

The amount of mobile applications is increasing rapidly, and it is difficult for software developers to identify the numerous key factors that affect their rating and performance. This study presents a machine-learning framework to improve decisions in adding new features to mobile applications and enhancing overall performance. A dataset of app attributes from the Apple AppStore was used, exploiting NLP techniques to preprocess the textual information and develop an Enhanced Random Forest (ERF) framework to assess and forecast ratings for multifunctional apps and investigate the connections between features and user ratings. The ERF model was compared with other renowned ML methods including Decision Trees (DT), Naive Bayes (NB), CNN, and ANN. The experimental results showed that the proposed model predicts app ratings more effectively compared to other complex models. The proposed model achieved precision, recall, and F1-score of 92.76%, 99.33%, and 95.93%, respectively.

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Spam and Phishing Detection Web Data Mining and Analysis Data Stream Mining Techniques

Идентификаторлар

DOI: 10.48084/etasr.9148

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