An Enhanced Random Forest (ERF)-based Machine Learning Framework for Resampling, Prediction, and Classification of Mobile Applications using Textual Features
Received: 1 October 2024 | Revised: 1 November 2024 and 14 November 2024 | Accepted: 5 December 2024 | Online: 13 December 2024
Corresponding author: Aitizaz Ali
Abstract
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.
Keywords:
machine learning, reliability, mobile applications, sustainable learning, predicting mobile app ratings, user ratings, XGBoost, random forest, NLP high-dimensional datasets, convolutional neural network (CNN), NSL-KDD, UNSW-NB15, mean square errorDownloads
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Copyright (c) 2024 Shahbaz Hussain, Nadeem Sarwar, Arshad Ali, Hamayun Khan, Irfanud Din, Abdullah M. Alqahtani, Mohamed Shabir, aitizaz.ali@apu.edu.my
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