Summary of Esds: Ai-powered Early Stunting Detection and Monitoring System Using Edited Radius-smote Algorithm, by A.a. Gde Yogi Pramana et al.
ESDS: AI-Powered Early Stunting Detection and Monitoring System using Edited Radius-SMOTE Algorithm
by A.A. Gde Yogi Pramana, Haidar Muhammad Zidan, Muhammad Fazil Maulana, Oskar Natan
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 proposes a novel approach for detecting stunting in Indonesian children using load cell sensors and ultrasonic sensors. The traditional diagnostic process is often hindered by lack of experience among medical workers, incompatible equipment, and inefficient bureaucracy. To overcome these challenges, the authors employ machine learning algorithms to classify sensor readings into normal, stunted, or stunting categories. The experiment results demonstrate high sensitivity for both load cell (0.9919) and ultrasonic sensors (0.9986), as well as an accuracy rate of 98% for the machine learning model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in Indonesia: detecting when kids are too short because they don’t get enough food or nutrients. This can cause serious health problems later on. The current way of doing this is not very good, with doctors who aren’t experienced and equipment that doesn’t work well together. To fix this, the authors use special sensors to measure children’s height and then train computers to look at these measurements to figure out if a child is stunted or not. The results show that this method works really well. |
Keywords
* Artificial intelligence * Machine learning
