ArticleSensors (Basel, Switzerland)2017
Indoor Air Quality Analysis Using Deep Learning with Sensor Data.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Article
- Designing a residual-enhanced hybrid Prophet-LSTM framework for urban air pollution forecasting in Beijing.Scientific reports · 2025Article
- Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping.Scientific reports · 2025Article
- Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks.Entropy (Basel, Switzerland) · 2024Article
- Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach.Sensors (Basel, Switzerland) · 2023Article
- A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools.International journal of environmental research and public health · 2023Review
- Multi-Sensor Platform for Predictive Air Quality Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ.Tuberculosis and respiratory diseases · 2023Article
- Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models.International journal of environmental research and public health · 2022Article
- ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.Sensors (Basel, Switzerland) · 2022Article
- Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm.Journal of environmental health science & engineering · 2021Article
- RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.