Summary of An Enhanced Analysis Of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function, by Ayad Ghany Ismaeel et al.
An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
by Ayad Ghany Ismaeel, S.J. Jereesha Mary, C. Anitha, Jaganathan Logeshwaran, Sarmad Nozad Mahmood, Sameer Alani, Akram H. Shather
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Networking and Internet Architecture (cs.NI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel strategy for enhancing smart city traffic intelligence using deep radial basis function (RBF) networks. Traditional traffic analysis methods rely on simplistic models that fail to capture complex urban traffic dynamics, whereas deep learning techniques like RBF networks can extract valuable insights from traffic data and enable precise predictions. The proposed method combines the adaptability of deep learning with the discriminative capability of radial basis functions, allowing it to learn intricate relationships and nonlinear patterns in traffic data. The paper evaluates its efficacy using real-world traffic datasets from a smart city environment, demonstrating that the deep RBF based approach outperforms conventional methods in terms of prediction accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Smart cities are making urban living better by using technology to optimize things like transportation. One key part is managing traffic flow, which affects people’s daily lives. This paper comes up with a new way to make smart city traffic smarter using special computer models called deep radial basis function (RBF) networks. These models can learn from big data and predict what will happen next, unlike simpler methods that don’t capture the complexity of urban traffic patterns. The researchers tested this method on real-life traffic data from a smart city and found it works better than other approaches in making accurate predictions. |
Keywords
* Artificial intelligence * Deep learning
