Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

Betto, Iku; Hatano, Ryo; Nishiyama, Hiroyuki

doi:10.1007/s10015-022-00809-z

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…In traditional parametric methods such as linear regression, each covariate can be interpreted clearly (e.g., for each 1 increase in x, we observe 2 increases in y) [ 17 , 49 ]. However, due to the complexity of the non-parametric algorithms that are common in machine-learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine-learning method works [ 1 , 62 – 65 ]. Thus, using SHAP allows for a similar covariate interpretation as linear regression even if the exact effect-sizes of the covariates cannot be interpreted the way it can in linear regression [ 15 , 22 , 49 , 66 – 68 ].…”

Section: Discussionmentioning

confidence: 99%

“…to the complexity of the non-parametric algorithms that are common in machine-learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine-learning method works [1,[62][63][64][65]. Thus, using SHAP allows for a similar covariate interpretation as linear regression even if the exact effect-sizes of the covariates cannot be interpreted the way it can in linear regression [15,22,49,[66][67][68]…”

Section: Plos Onementioning

confidence: 99%

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Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

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Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

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“…Their application in design can not only inherit the ancient intangible cultural tradition but also provide a broader space for enriching modern design methods [1][2]. Some experts also pointed out that the unique cultural and economic value of traditional ceramic technology is reflected in the development of cultural and creative industries [3][4]. In addition, some experts believe that the use of ceramics not only continues the traditional manufacturing process but also applies ceramic art to modern product design [5][6].…”

Section: Introductionmentioning

confidence: 99%

Creative product design of ceramic technology culture integrated with machine learning

Lin,

Jinwon

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The development of ceramic technology cannot be separated from traditional culture. China is a country with a long history and has left many excellent works in various dynasties. The development of ceramic technology has attracted more and more attention. Ceramic process product design needs more innovative ideas to meet market demand. Therefore, in this paper, machine learning technology is used to carry out data statistics and creative recommendations on product design, improve the design sense of ceramic technology and meet the needs of the masses. Qualitative and quantitative analysis and experimental methods are mainly used in this paper, with the help of machine learning, the related research on the creative design of ceramic craft culture. The experimental data show that in the field of ceramics, the training accuracy and test accuracy of the SVM model are kept above 80%, among which the weighted SVM algorithm has a higher grasp of the product accuracy.

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Design and Implementation of an Intelligent Machine Learning System Based on Artificial Intelligence Computing

Cao

2023

2023 2nd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)

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Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

Cited by 8 publications

References 7 publications

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Creative product design of ceramic technology culture integrated with machine learning

Design and Implementation of an Intelligent Machine Learning System Based on Artificial Intelligence Computing

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