Prediction of learning outcomes via clickstream data using machine learning

Ng, Kelvin Hongrui

doi:10.32657/10356/147631

Cited by 6 publications

(10 citation statements)

References 172 publications

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“…Compared with RF, GBDT adopts a stepwise iteration method; each tree is built based on the residue of the previous tree, which can better fit the complex relationships in the data. In GBDT, as the number of samples increases, the deviation of the cross-validation set has always been far from the deviation of the testing set, and the deviation of the testing set is constantly rising, which means that increasing the data set will optimize our algorithm to a certain extent …”

Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

Li,

Xiao,

Chen

et al. 2024

J. Proteome Res.

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Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

Li,

Xiao,

Chen

et al. 2024

J. Proteome Res.

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“…Mohanraj et al [55] developed a tool condition monitoring system for end milling process using different machine learning algorithms such as Decision Trees, Kernel Bayes, Multilayer Perceptron, and k-Nearest Neighbors (kNN) with accuracies up to 89%. Twardowski et al [56] monitored tool edge conditions using acoustic emission signals and a machine learning approach to Adapted from [63].…”

Section: Machine Learning Approachmentioning

confidence: 99%

“…1 shows how the scale drives machine learning and deep learning progress. In this case, the performance of the model will be given not by the model itself but by the ability to select the right features as inputs, tuning the parameters of the model used [63]. Also remarkable is the fact that Deep Learning algorithms such as Neural Networks are much more complex to adjust because of the number of hyperparameters available for tuning, with a noticeable computational cost.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Domínguez-Monferrer

Fernández-Pérez²,

Santos³

et al. 2022

Journal of Manufacturing Systems

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“…It involves offline creation/learning and testing of models, along with the online real-time operation of those models, and in some cases, real-time updates of models using new data seen in the field. Readers are strongly recommended to read the cited books for a deeper understanding of AI methods [15][16][17].…”

Section: Aimentioning

confidence: 99%

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

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In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

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Prediction of learning outcomes via clickstream data using machine learning

Cited by 6 publications

References 172 publications

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD)

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

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