Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom

Wu, Ming-Te

doi:10.1038/s41598-022-07137-z

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…Performance analysis by the confusion matrix. The confusion matrix 42 is a well-established graphical technique to easily assess the reliability of a classifier. This technique reveals the number of correct as well as incorrect identifications of each involved class.…”

Section: Deep Learning-based Paradigmmentioning

confidence: 99%

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Pandey

Gandomkar

Vaferi

et al. 2023

Sci Rep

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High oil prices and concern about limited oil reserves lead to increase interest in enhanced oil recovery (EOR). Selecting the most efficient development plan is of high interest to optimize economic cost. Hence, the main objective of this study is to construct a novel deep-learning classifier to select the best EOR method based on the reservoir’s rock and fluid properties (depth, porosity, permeability, gravity, viscosity), and temperature. Our deep learning-based classifier consists of a one-dimensional (1D) convolutional neural network, long short-term memory (LSTM), and densely connected neural network layers. The genetic algorithm has been applied to tune the hyperparameters of this hybrid classifier. The proposed classifier is developed and tested using 735 EOR projects on sandstone, unconsolidated sandstone, carbonate, and conglomerate reservoirs in more than 17 countries. Both the numerical and graphical investigations approve that the structure-tuned deep learning classifier is a reliable tool to screen the EOR scenarios and select the best one. The designed model correctly classifies training, validation, and testing examples with an accuracy of 96.82%, 84.31%, and 82.61%, respectively. It means that only 30 out of 735 available EOR projects are incorrectly identified by the proposed deep learning classifier. The model also demonstrates a small categorical cross-entropy of 0.1548 for the classification of the involved enhanced oil recovery techniques. Such a powerful classifier is required to select the most suitable EOR candidate for a given oil reservoir with limited field information.

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Section: Deep Learning-based Paradigmmentioning

confidence: 99%

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Pandey

Gandomkar

Vaferi

et al. 2023

Sci Rep

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“…The performance of MARCOV19‐Net and all the other approaches are analyzed and determined using various hyperparameters 64 and performance metrics. Confusion Matrix 65 is a performance measurement for classification which considers two or multiple outputs, and has four dissimilar arrangements of predicted and actual values for a binary classification problem as shown in Table 3.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Mixed attention and regularized COVID‐19 network: An approach to detection of COVID‐19 with chest x‐ray images

Das

Biswas

Bandyopadhyay

2023

Int J Imaging Syst Tech

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Coronavirus Disease 2019 has led to a global pandemic in the year 2020 and the cases are dynamically increasing and active all over the world. COVID-19 is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). It is a human-to-human transmissible disease which has severely affected people especially with weaker immunity, and is detected through Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR

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“…we used confusion matrix to illustrate these indicators. [13] (Table 2). The better the prediction performance of the model, the better the method can improve the classi cation performance.…”

Section: Step2: Handle Missing Valuesmentioning

confidence: 99%

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

Chang

Tong

et al. 2022

Preprint

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Background Handling missing data is indispensable in health care real-world data processing. Deleting or imputing missing data may introduce error or lead to multicollinearity. Therefore, we tried to explore a novel missing data processing method to avoid the above issues. Method By exploring an optimal deleting way of columns and rows with missing data, we developed a missing data processing method which can retain most information of original datasets. Traditionally, the goal can be realized by traversing all possible deleting combinations. But the computational cost is too high to use in large datasets. Therefore, we established an Optimal Intact Subset Method (OIS.Method) by using an indicator containing missing information of both columns and rows to determine an optimal deleting order of columns. OIS.Method can ascertain the optimal deleting way and simplify computing meanwhile. In order to validate the effectiveness of OIS.Method, we compared OIS.Method with five other data-imputation methods in 700 classification datasets (simulated datasets 1) generated by computer. In order to simulate real-world datasets, we generated simulated datasets 2: introducing redundant variables in simulated datasets 1. We also compared OIS.Method with control methods on that. Finally, we validated OIS.Method in two real-world classification tasks: 1. predict the risk of hypotension during dialysis, 2. predict the risk of drug adverse reaction in elderly patients with type 2 diabetes. Results In simulated datasets 1, we found that OIS.Method performed well when the distribution of missing data was unbalanced among columns. In simulated datasets 2, the comprehensive performance of OIS.Method was better in all evaluating dimensions. In two real-world datasets, OIS.Method could acquire better classification performance. We used the area under ROC curve (AUC) to evaluate it: OIS.Method VS Simple Impute VS Random Forest VS Modified Random Forest, 0.8179 ± 0.0005VS0.8116 ± 0.0002VS0.8087 ± 0.0009VS0.8093 ± 0.0014 in task1, and 0.7028VS0.6963VS0.6957VS0.6699 in task2. Conclusions Our study provided a novel method for handing missing data in real-world study. Compared with other existing missing data processing methods, the calculation of OIS.Method is smaller, and OIS.Method can reflect the true data situation of original datasets. Moreover, OIS.Method is well-suited for real-world datasets with large sample size and multiple variables.

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Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom

Cited by 16 publications

References 24 publications

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

Mixed attention and regularized COVID‐19 network: An approach to detection of COVID‐19 with chest x‐ray images

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

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