Machine Learning in Various Applications
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
Future research should focus on integrating advanced machine learning models and real-time data for improving prediction accuracy and personalization across diverse systems such as adaptive learning technologies, recommendation systems, and smart food court recommendations.
Consensus across the literature
The papers collectively establish the need for enhanced machine learning techniques but leave open specific algorithms and datasets to be used.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- Adaptive Learning Technologies in Modern Education (2026) · doi
In future work, the system can be enhanced by incorporating advanced machine learning models, real-time analytics, and larger educational datasets to further improve prediction accuracy and personalization capabilities.
Keywords: future system enhanced incorporating advanced machine learning models real time analytics larger educational datasets further - Personalized Recommendation System for E-Commerce Platform (2026) · doi
The recommendation accuracy can be further improved by using real-time user data, larger datasets, and advanced machine learning models.
Keywords: recommendation accuracy further improved using real time user larger datasets advanced machine learning models
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