Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System

Wen, Hung-Ta; Wu, Hom-Yu; Liao, Kuo-Chien

doi:10.3390/inventions7040126

Cited by 10 publications

(8 citation statements)

References 55 publications

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“…Scalability in all circumstances is the primary reason behind XGBoost's success. XGBoost can use the least number of resources to solve issues at real-world scale [37]. The block storage structure, which supports parallel computing, was adopted.…”

Section: Extreme Gradient Boostmentioning

confidence: 99%

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

Prasad,

Santra,

Jayaganthan

2024

Metals

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The synthesis of silver nanoparticles (AgNPs) holds significant promise for various applications in fields ranging from medicine to electronics. Accurately predicting the particle size during synthesis is crucial for optimizing the properties and performance of these nanoparticles. In this study, we compare the efficacy of tree-based models compared with the existing models, for predicting the particle size in silver nanoparticle synthesis. The study investigates the influence of input features, such as reaction parameters, precursor concentrations, etc., on the predictive performance of each model type. Overall, this study contributes to the understanding of modeling techniques for nanoparticle synthesis and underscores the importance of selecting appropriate methodologies for accurate particle size prediction, thereby facilitating the optimization of synthesis processes and enhancing the effectiveness of silver nanoparticle-based applications.

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Section: Extreme Gradient Boostmentioning

confidence: 99%

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

Prasad,

Santra,

Jayaganthan

2024

Metals

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“…The XGB method is one of the ensemble learning methods based on Gradient Boosting [56] and is used in classification and regression problems [57][58][59]. The method aims to optimize the cost objective function, which consists of the combination of loss function and regularization terms.…”

Section: Extreme Gradient Boosting (Xgb)mentioning

confidence: 99%

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Kabas,

Ercan,

Dinca

2024

Applied Sciences

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Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy.

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“…XGBoost is a robust algorithm widely used in various domains, including data science competitions, finance, healthcare, and more. The utilization of the XGBoost model was employed for simulating a biomass gasification system [24], achieving high performance with a coefficient of determination of 0.96. In the context of predicting product sales in the large-scale retail sector, the XGBoost algorithm, combined with the Augmented Data (AD) technique, improved prediction accuracy and decreased error by about an order of magnitude [25].…”

Section: Machine Learning (Ml) Layermentioning

confidence: 99%

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Alharbi,

Cherif,

Nadeem

2023

Smart Cities

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Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

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Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System

Cited by 10 publications

References 55 publications

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

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