Adaptive Boosting and Bootstrapped Aggregation based Ensemble Machine Learning Methods for Photovoltaic Systems Output Current Prediction

Omer, Zahi M.; Shareef, Hussain

doi:10.1109/aupec48547.2019.211856

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The method used in this research is Bootstrap [17][18][19][20][21][22][23]. The bootstrap method is a resampling technique used to estimate statistics on a population by sampling the dataset with replacement.…”

Section: Methodsmentioning

confidence: 99%

“…It can be used to estimate summary statistics such as the mean or standard deviation, and It is used in Machine Learning to estimate the skill of machine learning models when making predictions on data not included in the training data. One of the methods used is Bootstrap [15][16][17][18], as shown in Figure 1. Bootstrap is a widely used statistical tool that is very powerful in quantifying the uncertainty associated with a given estimator or statistical learning method.…”

Section: Methodsmentioning

confidence: 99%

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The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

Hia,

Sihombing,

Juledi

2023

IOTA

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Often, after the learning and teaching process is over, students will be tested with quizzes, midterm exams, and even end-of-semester exams, but these exams still take time after the teacher has taught several weeks or months that have passed; what if after teaching, for example, a math lesson, and students immediately understand or do not understand at all, and this can be detected using Machine Learning. The variable that can be raised is the value or quiz grade of a particular subject; for example, mathematics is one of the disliked subjects for most elementary school students, but how to find out that the student is able or unable to solve math problems and predict the end of semester grades for mathematics, this can be determined using Machine Learning, using the KNN Algorithm or K-MEANS method, or other methods that are deemed appropriate to the existing case study. In this case study, it is predicted whether a variable affects each other or affects other variables; this is done by doing or drawing relationships between variables. This research successfully concluded from the performance of machine learning in predicting students' understanding of math lessons after teaching and learning activities ended. The parameters that will be used for testing are population and sampling, and then data analysis, validity, and reliability tests are carried out.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

Hia,

Sihombing,

Juledi

2023

IOTA

View full text Add to dashboard Cite

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“…XG boost is an algorithm that utilizes a meta‐learner based on classification and regression trees (CART). It constructs the subsequent tree by considering the training residuals of the previous tree and employs the original weak learner to incrementally enhance the model's predictive power through iterative training, progressively optimizing the objective function to achieve the optimal prediction value 28,29 . XG boost demonstrates excellent training capability on high‐dimensional datasets.…”

Section: Model Trainingmentioning

confidence: 99%

Very high‐cycle fatigue life prediction of high‐strength steel based on machine learning

Liu,

Zhang,

Cong

et al. 2023

Fatigue Fract Eng Mat Struct

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Very high‐cycle fatigue life (VHCF) prediction of high‐strength steel based on machine learning (ML) was investigated in this paper. First, a total of 173 sets of experimental data on the VHCF of high‐strength steel were collected to train the ML model. The sensitivity coefficient analysis indicated that inclusion size and maximum stress were the strongest correlation parameters with fatigue life and selected as the input features for the final model training. The S–N curve predicted by the obtained ML model closely aligns with the actual S–N curve. Among the three algorithm models, namely, random forest, XG boost, and gradient boosting, the gradient boosting model exhibited superior performance and achieved the highest accuracy in predicting the VHCF life of high‐strength steel. A comparison between the Murakami model and the gradient boosting model was conducted. It is indicated that ML exhibits superior predictive performance with high efficiency and excellent accuracy.

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“…Stumps having ineffective regressors are poor learners. Each created stump has its own vote; whereas, the votes are distributed based on the error of various vote weights (w) [32].…”

Section: Ensemble Regressionmentioning

confidence: 99%

Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

Rathore

Khan

Ali³

et al. 2022

Applied Sciences

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Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

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Adaptive Boosting and Bootstrapped Aggregation based Ensemble Machine Learning Methods for Photovoltaic Systems Output Current Prediction

Cited by 7 publications

References 13 publications

The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning

Very high‐cycle fatigue life prediction of high‐strength steel based on machine learning

Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

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