ArticleScientific reports2023
A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed.
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- Scaling digital models.Scientific reports · 2026Article
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- Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.AAPS PharmSciTech · 2025Article
- Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.ACS omega · 2025Article
- Use of Artificial Neural Networks for Recycled Pellets Identification: Polypropylene-Based Composites.Polymers · 2025Article
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- Machine learning workflows beyond linear models in low-data regimes.Chemical science · 2025Article
- Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Nanoparticle Skin Penetration: Depths and Routes Modeled In-Silico.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Physicochemical Parameters and Multivariate Analysis to Predict the Sensory Quality in Specialty Coffee from Panama.ACS omega · 2025Article
- Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City.Scientific reports · 2025Article
- Analysis of cultivated land degradation in southern China: diagnostics, drivers, and restoration solutions.Frontiers in plant science · 2025Article
- Soft sensing modeling of penicillin fermentation process based on local selection ensemble learning.Scientific reports · 2024Article
- Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors.Sensors (Basel, Switzerland) · 2024Review
- Radiomics in PET/CT and HRCT for systemic sclerosis-associated interstitial lung disease: breakthroughs and future directions.Radiologia brasileiraArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM
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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.