Classification Model Based Automatic Static Evaluation Method for C Programs

2022 ◽  
pp. 449-456
Author(s):  
Xinfeng Shu ◽  
Fangyao Guo
Keyword(s):  
Evaluation Method ◽  
Classification Model ◽  
C Programs ◽  
Model Based ◽  
Static Evaluation

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

The new chronic lithiasic disease management and risk classification model, based on a Kaiser Permanente Pyramid (KPP) design. The 3-year results

2021 ◽  
Vol 79 ◽  
pp. S346-S347
Author(s):  
F. Gómez Palomo ◽  
D.G. Ordaz Jurado ◽  
A. Budía Alba ◽  
D. Vivas-Consuelo ◽  
P. Bahilo Mateu ◽  
...  
Keyword(s):  
Disease Management ◽  
Risk Classification ◽  
Classification Model ◽  
Kaiser Permanente ◽  
Model Based

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

A distributed traffic classification model based on Federated Learning

2021 ◽  
Author(s):  
Xufeng Zhu ◽  
Nina Shu ◽  
Huaixi Wang ◽  
Tao Wu
Keyword(s):  
Classification Model ◽  
Traffic Classification ◽  
Model Based
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