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AiCareAir :
AiCareAir : Hybrid-Ensemble Internet-of-Things Sensing Unit Model for Air Pollutant Control
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- Borah, Jintu (författare)
- Natl Inst Technol Meghalaya, India
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- Nadzir, Mohd. Shahrul Mohd (författare)
- UKM, Malaysia
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- Cayetano, Mylene G. (författare)
- UP Diliman, Philippines
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- Majumdar, Shubhankar (författare)
- Natl Inst Technol Meghalaya, India
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- Ghayvat, Hemant (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Technol Univ Denmark, Denmark
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- Srivastava, Gautam (författare)
- Lebanese Amer Univ, Lebanon;China Med Univ, Taiwan
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(creator_code:org_t)
- IEEE, 2024
- 2024
- Engelska.
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 24:13, s. 21558-21565
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The detrimental effects on human health caused by air pollution show that being able to predict air quality is a task of utmost significance. The application of artificial intelligence (AI) and the Internet of Things (IoT) is seen as promising in this domain. The performances of state-of-the-art models in terms of prediction accuracy vary with different pollutants and are acceptable only for certain pollutants. This article uses machine learning (ML) and deep learning (DL) models to predict the concentrations of six major air pollutants. Data are collected over eight months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this article a hybrid-ensemble model is proposed using a combination of ML models, specifically random forest, K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and neural network (NN) models, namely, long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional NNs (CNNs). Here, a hybrid-ensemble learning model is created using five various ML models as weak learners. In previous ensemble models, a homogeneous group of weak learners are used; however, this work uses a heterogeneous group of weak learners. The prediction accuracy is compared using R2 score, absolute, squared, and root-mean-squared errors (RMSEs).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Adam optimizer
- convolutional neural networks (CNNs)
- gated recurrent units (GRUs)
- Keras API
- long short-term memory (LSTM)
- Scikit learn
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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