Comparative Study on the Machine Learning and Regression-Based Approaches to Predict the Hydraulic Jump Sequent Depth Ratio

Baharvand, Saman; Jozaghi, Ali; Fatahi-Alkouhi, Reza; Karimzadeh, Saeed Daei-; Nasiri, Ruhollah; Lashkar-Ara, Babak

doi:10.1007/s40996-020-00526-2

Cited by 20 publications

(5 citation statements)

References 38 publications

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“…The linear regression is what we are talking about here. When we compare the outcomes of previous designs for the cost of healthcare forecasting approach, we can see that our system is more efficient, demonstrating that a more explicit approach for an issue such as healthcare cost forecasting is conceivable [ 38 – 40 ]. Our research, on the other hand, clearly reveals that healthcare spending is highly connected inside the Medicare program.…”

Section: Discussionmentioning

confidence: 99%

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

Taloba

El-Aziz

Alshanbari

et al. 2022

Journal of Healthcare Engineering

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Medical costs are one of the most common recurring expenses in a person’s life. Based on different research studies, BMI, ageing, smoking, and other factors are all related to greater personal medical care costs. The estimates of the expenditures of health care related to obesity are needed to help create cost-effective obesity prevention strategies. Obesity prevention at a young age is a top concern in global health, clinical practice, and public health. To avoid these restrictions, genetic variants are employed as instrumental variables in this research. Using statistics from public huge datasets, the impact of body mass index (BMI) on overall healthcare expenses is predicted. A multiview learning architecture can be used to leverage BMI information in records, including diagnostic texts, diagnostic IDs, and patient traits. A hierarchy perception structure was suggested to choose significant words, health checks, and diagnoses for training phase informative data representations, because various words, diagnoses, and previous health care have varying significance for expense calculation. In this system model, linear regression analysis, naive Bayes classifier, and random forest algorithms were compared using a business analytic method that applied statistical and machine-learning approaches. According to the results of our forecasting method, linear regression has the maximum accuracy of 97.89 percent in forecasting overall healthcare costs. In terms of financial statistics, our methodology provides a predictive method.

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Section: Discussionmentioning

confidence: 99%

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

Taloba

El-Aziz

Alshanbari

et al. 2022

Journal of Healthcare Engineering

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show abstract

“…However, the patterns in equations ( 14) and ( 15) are highly non-linear, hence linear regression algorithms are inappropriate for this work. In addition, linear regression algorithms have been outperformed by other regression techniques when evaluated on other regression tasks [60,61]. For logic-based approaches such as BRT and RF, the accuracy of such algorithms has been proven to be affected by the discretization in the input feature [62].…”

Section: Selection Of Regression Based Machine Learning Approachesmentioning

confidence: 99%

Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography

Chen¹,

Hassan²,

Tallman³

et al. 2022

Smart Mater. Struct.

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Conductive nanocomposites, enabled by their piezoresistivity, have emerged as a new instrument in structural health monitoring. To this end, studies have recently found that electrical resistance tomography (ERT), a non-destructive conductivity imaging technique, can be utilized with piezoresistive nanocomposites to detect and localize damage. Furthermore, by incorporating complementary optimization protocols, the mechanical state of the nanocomposites can also be determined. In many cases, however, such approaches may be associated with high computational cost. To address this, we develop deep learned frameworks using neural networks to directly predict strain and stress distributions -- thereby bypassing the need to solve the ERT inverse problem or execute an optimization protocol to assess mechanical state. The feasibility of the learned frameworks is validated using simulated and experimental data considering a carbon nanofiber plate in tension. Results show that the learned frameworks are capable of directly and reliably predicting strain and stress distributions based on ERT voltage measurements.

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“…In recent years, machine learning (ML) approaches have been used in several studies as an alternative to empirical equations to predict the dispersion coefficient in natural streams. The main advantage of using ML models (also known as soft computing techniques) is their independency from the physics of the problem (Baharvand et al, 2021;Salazar and Crookston 2019). Several studies have examined the ability of these approaches to predict longitudinal dispersion coefficients.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

Baharvand,

Ahmari

2024

Water Resour Manage

Self Cite

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Several empirical equations and machine learning approaches have been developed to predict dispersion coefficients in open channels; however, the ability of some learning-based models to predict these coefficients has not yet been evaluated, and the direct application of machine learning-based dispersion coefficients to Lagrangian sediment transport models has not been studied. In this research, data from previous studies is used to evaluate the ability of ensemble machine learning models, i.e., random forest regression (RFR) and gradient boosting regression (GBR), to predict longitudinal and transverse dispersion in natural streams. The optimal principal parameters of ensemble models were adjusted using the grid-search cross-validation technique, and the machine learning-based dispersion models were integrated with a Lagrangian particle tracking model to simulate suspended sediment concentration in natural streams. The resulting suspended sediment concentration distribution was compared with the field data. The results showed that GBR model, with a coefficient of determination (R 2 ) of 0.95, performed better than the RFR model, with R 2 =0.9, in predicting the longitudinal dispersion coefficients in a natural stream in both training and testing stages. However, the RFR model with R 2 = 0.94 performed better than the GBR (R 2 = 0.91) in predicting the transverse dispersion in testing stage. Both models underestimated the dispersion coefficients in the training and testing stages. Comparison between the PTM with ensemble dispersion coefficients and empirical-based dispersion relationships revealed the better performance of the GBR model compared to the other two methods.

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Comparative Study on the Machine Learning and Regression-Based Approaches to Predict the Hydraulic Jump Sequent Depth Ratio

Cited by 20 publications

References 38 publications

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning

Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography

Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

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