A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms
This research presents a robust and comprehensive framework for predicting the density of hybrid nanofluids using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10892114/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849689636622827520 |
|---|---|
| author | Priya Mathur Hammad Shaikh Farhan Sheth Dheeraj Kumar Amit Kumar Gupta |
| author_facet | Priya Mathur Hammad Shaikh Farhan Sheth Dheeraj Kumar Amit Kumar Gupta |
| author_sort | Priya Mathur |
| collection | DOAJ |
| description | This research presents a robust and comprehensive framework for predicting the density of hybrid nanofluids using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (°C), volume concentration (<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>), base fluid density (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {bf}}$ </tex-math></inline-formula>), density of primary and secondary nanoparticles (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np2}}$ </tex-math></inline-formula>), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). In particular, autoencoder-based augmentation combined with hyperparameter optimization consistently improved predictive accuracy across all models. For machine learning models, Gradient Boosting achieved the most remarkable performance, with R2 scores of 0.99999 and minimal MSE values of 0.00091. Among deep learning models, Recurrent Neural Networks (RNN) stacked with Linear Regression achieved superior performance with an R2 of 0.9999, MSE of 0.0014, and MAE of 0.012. The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. This framework offers a scalable and reliable tool for advancing nanofluid-based applications in thermal engineering and related domains. |
| format | Article |
| id | doaj-art-bae7f53a736f4212b176be8e8a9f17da |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bae7f53a736f4212b176be8e8a9f17da2025-08-20T03:21:33ZengIEEEIEEE Access2169-35362025-01-0113357503577910.1109/ACCESS.2025.354347510892114A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning ParadigmsPriya Mathur0https://orcid.org/0000-0003-0378-7171Hammad Shaikh1https://orcid.org/0009-0004-5312-5509Farhan Sheth2https://orcid.org/0009-0009-9371-6983Dheeraj Kumar3https://orcid.org/0009-0000-0296-0572Amit Kumar Gupta4https://orcid.org/0000-0002-5345-2794Poornima Institute of Engineering and Technology, Jaipur, Rajasthan, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaThis research presents a robust and comprehensive framework for predicting the density of hybrid nanofluids using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (°C), volume concentration (<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula>), base fluid density (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {bf}}$ </tex-math></inline-formula>), density of primary and secondary nanoparticles (<inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho _{\text {np2}}$ </tex-math></inline-formula>), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). In particular, autoencoder-based augmentation combined with hyperparameter optimization consistently improved predictive accuracy across all models. For machine learning models, Gradient Boosting achieved the most remarkable performance, with R2 scores of 0.99999 and minimal MSE values of 0.00091. Among deep learning models, Recurrent Neural Networks (RNN) stacked with Linear Regression achieved superior performance with an R2 of 0.9999, MSE of 0.0014, and MAE of 0.012. The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. This framework offers a scalable and reliable tool for advancing nanofluid-based applications in thermal engineering and related domains.https://ieeexplore.ieee.org/document/10892114/Density predictionhybrid nanofluidsmachine learningdeep learningdata augmentationmeta-heuristic optimization |
| spellingShingle | Priya Mathur Hammad Shaikh Farhan Sheth Dheeraj Kumar Amit Kumar Gupta A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms IEEE Access Density prediction hybrid nanofluids machine learning deep learning data augmentation meta-heuristic optimization |
| title | A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms |
| title_full | A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms |
| title_fullStr | A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms |
| title_full_unstemmed | A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms |
| title_short | A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms |
| title_sort | computational intelligence framework integrating data augmentation and meta heuristic optimization algorithms for enhanced hybrid nanofluid density prediction through machine and deep learning paradigms |
| topic | Density prediction hybrid nanofluids machine learning deep learning data augmentation meta-heuristic optimization |
| url | https://ieeexplore.ieee.org/document/10892114/ |
| work_keys_str_mv | AT priyamathur acomputationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT hammadshaikh acomputationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT farhansheth acomputationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT dheerajkumar acomputationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT amitkumargupta acomputationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT priyamathur computationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT hammadshaikh computationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT farhansheth computationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT dheerajkumar computationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms AT amitkumargupta computationalintelligenceframeworkintegratingdataaugmentationandmetaheuristicoptimizationalgorithmsforenhancedhybridnanofluiddensitypredictionthroughmachineanddeeplearningparadigms |