Machine learning model for IoT-Edge device based Water Quality Monitoring
Author:
Affiliation:
1. University of Hyderabad,School of Computer and Information Sciences,Hyderabad,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9797887/9797888/09798212.pdf?arnumber=9798212
Reference18 articles.
1. Efficient Water Quality Prediction Using Supervised Machine Learning
2. Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong
3. Towards a time and cost effective approach to water quality index class prediction
4. Hybrid soft computing approach for determining water quality indicator: Euphrates River
5. Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators
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1. Experimental Study of Real-Time Water Quality Analysis with Deep Neural Network (DNN) on Edge Computing;2024 10th International Conference on Engineering, Applied Sciences, and Technology (ICEAST);2024-05-01
2. Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases;Electronics;2024-02-03
3. Contributions to Reduce the Gap on Water Quality Analysis in Chile and Latin America: State of the Art;2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON);2023-12-05
4. Security in IoT systems using natural language processing: Future challenges and directions;Internet Technology Letters;2023-02-22
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