Summary of Maintaining and Managing Road Quality:using Mlp and Dnn, by Makgotso Jacqueline Maotwana
Maintaining and Managing Road Quality:Using MLP and DNN
by Makgotso Jacqueline Maotwana
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 The paper aims to develop and evaluate machine learning models for accurately classifying road conditions into four categories: good, satisfactory, poor, and very poor. Using a Kaggle dataset of road images, the authors implemented various approaches, including a baseline Multilayer Perceptron (MLP), Deep Neural Network (DNN) using Keras, Logistic Regression, and a wide model incorporating feature engineering with the K-Nearest Neighbors (KNN) algorithm. The DNN achieved the best accuracy, while the MLP provided a solid foundation. The Logistic Regression offered interpretability and insights into important features. The paper demonstrates that machine learning can automate road condition monitoring, saving time and money on maintenance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make roads safer by using machines to tell if they’re good or bad. It uses special pictures of roads to teach computers to decide what kind of shape the road is in. They tried different ways for the computer to figure it out, like making a simple map and using big neural networks. The best one was the biggest one, but the simple one was still helpful. This could help make cities safer by letting machines check the roads instead of people. |
Keywords
» Artificial intelligence » Feature engineering » Logistic regression » Machine learning » Neural network
