Summary of Predicting Next Useful Location with Context-awareness: the State-of-the-art, by Alireza Nezhadettehad et al.
Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
by Alireza Nezhadettehad, Arkady Zaslavsky, Rakib Abdur, Siraj Ahmed Shaikh, Seng W. Loke, Guang-Li Huang, Alireza Hassani
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys the recent developments in predicting the future location of mobile objects, which is crucial for various applications such as traffic congestion control, location-aware advertisements, and public health monitoring. By leveraging historical and real-time contextual information from smartphones, location sensors, and social networks, researchers can recognize mobility patterns and achieve more accurate predictions using artificial intelligence and machine learning techniques. The paper defines the next useful location prediction problem with context-awareness, analyzes nearly thirty studies in this field, and discusses the advantages and disadvantages of different approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting where people or things will be in the future. This is important for things like traffic control, advertising, and tracking public health. The researchers use information from smartphones, location sensors, and social networks to figure out where things are likely to go. They then use artificial intelligence and machine learning to make more accurate predictions. |
Keywords
* Artificial intelligence * Machine learning * Tracking
