Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm

Bashar, Shikder Shafiul; Miah, Md. Sazal; Karim, A.H.M. Zadidul; Mahmud, Md. Abdullah Al

doi:10.1109/eict48899.2019.9068845

Cited by 13 publications

(4 citation statements)

References 9 publications

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“…Supervised machine learning is the application of algorithms capable of producing generalities in patterns via the use of externally supplied data to predict future patterns and instances (Singh et al, 2016). Several types of supervised machine learning algorithms exist, but for this analysis SVM (Noble, 2006), RFR (Breiman, 2001), DTR (Bashar et al, 2019), and PLS (Manikanta et al, 2015) regression algorithms were utilized. Each algorithm has advantages and disadvantages when applied to a unique dataset; thus, implementation of these four enabled comprehensive analysis of supervised learners on the composite and RFE datasets.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Estimating Reservoir Sedimentation Using Deep Learning

Cox¹,

Meyer²,

Botero‐Acosta³

et al. 2024

Preprint

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Several reservoirs across the US are filling with sediment, which jeopardizes their functionality and increases maintenance costs. The US Army Corps of Engineers (USACE) developed the Reservoir Sedimentation Information (RSI) system to assess reservoir aggradation and track dam operation suitability for water-resource management and dam safety. The RSI dataset contains historical elevation-capacity data for approximately 400 dams (excluding navigation structures) which correspond to less than 1% of dams across the US. Thus, there is a critical need to develop methods for estimating reservoir sedimentation for unmonitored sites. The goal of this project was to create a generalized method for estimating reservoir sedimentation rates using reservoir design information and watershed data. To meet this objective, geospatial tools were used to build a refined composite dataset to complement the RSI system’s data with precipitation and watershed characteristics. Nine deep learning models were then used on the benchmark dataset to determine its accuracy at predicting capacity loss for the RSI reservoirs: four supervised machine learning models, four deep neural network (DNN) models, and a multilinear power regression model. A DNN model, containing a progressively increasing node and layer construction, was deemed the most accurate, with R2 values from its calibration and validation datasets being 0.83 and 0.70, respectively. The best model was recalibrated over the entire dataset, which showed greater accuracy on the prediction of RSI reservoir’s capacity loss, with an R2 of 0.81. This predictive model could be used to evaluate the capacity loss of unmonitored reservoirs, forecast sedimentation rates under future climate conditions, and identify reservoirs with the highest risk of losing functionality.

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Section: Supervised Machine Learningmentioning

confidence: 99%

Estimating Reservoir Sedimentation Using Deep Learning

Cox¹,

Meyer²,

Botero‐Acosta³

et al. 2024

Preprint

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show abstract

“…Therefore, the number and dimensionality of the data suggest that high-dimensional methods (eg, artificial neural networks) are unsuitable for this task. Therefore, we evaluated the following methods: Linear Regression 28 Decision Tree Regression 29 Random Forest Regression 30 Support Vector Regression (SVR) 31 Nu Support Vector Regression (NuSVR) 32 Gaussian Process Regressor 33 K-Nearest Neighbors Regressor 34 (KNeighbors) Ridge Regression 35 Lasso Regression 36 To assess the performance of these models, we utilized key performance metrics, including R 2 and explained variance scores. The R 2 score measures the extent to which the model captures variance in the target variable.…”

Section: Dovepressmentioning

confidence: 99%

Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature

Bonaccorso,

Ortis,

Musumeci

et al. 2024

IJN

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“…One of the processes of Data Mining that can provide knowledge about the interrelationship between data variables is Random Forest. Random Forest Regression Algorithms are used to match the data and human resource from the test data [4]. Today Big Data is becoming a relevant issue for the world, interest in Data Science and Machine Learning is growing.…”

Section: Implementation Of Machine Learning To Determinementioning

confidence: 99%

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

Prasetyaningrun

Pratama

Chandra

2021

Int. Intell. Research.

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In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value

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Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm

Cited by 13 publications

References 9 publications

Estimating Reservoir Sedimentation Using Deep Learning

Estimating Reservoir Sedimentation Using Deep Learning

Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

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