| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Hiroyuki Sugimori, Kaoruko Shimizu, Hironi Makita, Masaru Suzuki, Satoshi Konn. A Comparative Evaluation of Computed Tomography Images for the Classification of Spirometric Severity of the Chronic Obstructive Pulmonary Disease with Deep Learning. Diagnostics (Basel, Switzerland). vol 11. issue 6. 2021-06-26. PMID:34064240. |
this study aimed to create a classifier trained with and without preprocessing for the global initiative for chronic obstructive lung disease (gold) classification using ct images and to evaluate the classification accuracy of the gold classification by confusion matrix. |
2021-06-26 |
2023-08-13 |
Not clear |
| Sankaran Rajendran, Sobhi Nasir, Khalifa Al Jabr. Mapping and accuracy assessment of siltation of recharge dams using remote sensing technique. Scientific reports. vol 10. issue 1. 2021-06-25. PMID:32587395. |
mapping accuracy for the occurrence of siltation is assessed by a confusion matrix which produced an overall accuracy of 97.88% and kappa coefficient of 0.98 in the maximum likelihood method. |
2021-06-25 |
2023-08-13 |
Not clear |
| Andreas Schwarz, Joana Pereira, Reinmar Kobler, Gernot R Muller-Put. Unimanual and Bimanual Reach-and-Grasp Actions Can Be Decoded From Human EEG. IEEE transactions on bio-medical engineering. vol 67. issue 6. 2021-06-24. PMID:31545707. |
the grand average confusion matrix shows highest true positive rates (tpr) for the rest (63%) condition while tpr for the movement classes varied between 33 to 41%. |
2021-06-24 |
2023-08-13 |
human |
| Suxia Bao, Hong-Yi Pan, Wei Zheng, Qing-Qing Wu, Yi-Ning Dai, Nan-Nan Sun, Tian-Chen Hui, Wen-Hao Wu, Yi-Cheng Huang, Guo-Bo Chen, Qiao-Qiao Yin, Li-Juan Wu, Rong Yan, Ming-Shan Wang, Mei-Juan Chen, Jia-Jie Zhang, Li-Xia Yu, Ji-Chan Shi, Nian Fang, Yue-Fei Shen, Xin-Sheng Xie, Chun-Lian Ma, Wan-Jun Yu, Wen-Hui Tu, Bin Ju, Hai-Jun Huang, Yong-Xi Tong, Hong-Ying Pa. Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm. Medicine. vol 100. issue 24. 2021-06-24. PMID:34128861. |
the screening model was evaluated by the confusion matrix and receiver operating characteristic (roc) analysis in the validation.the rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest x-ray or computed tomography. |
2021-06-24 |
2023-08-13 |
Not clear |
| Suxia Bao, Hong-Yi Pan, Wei Zheng, Qing-Qing Wu, Yi-Ning Dai, Nan-Nan Sun, Tian-Chen Hui, Wen-Hao Wu, Yi-Cheng Huang, Guo-Bo Chen, Qiao-Qiao Yin, Li-Juan Wu, Rong Yan, Ming-Shan Wang, Mei-Juan Chen, Jia-Jie Zhang, Li-Xia Yu, Ji-Chan Shi, Nian Fang, Yue-Fei Shen, Xin-Sheng Xie, Chun-Lian Ma, Wan-Jun Yu, Wen-Hui Tu, Bin Ju, Hai-Jun Huang, Yong-Xi Tong, Hong-Ying Pa. Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm. Medicine. vol 100. issue 24. 2021-06-24. PMID:34128861. |
the confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early covid-19 pneumonia.here, we developed and validated a rapid screening model that could predict early covid-19 pneumonia with high sensitivity and specificity. |
2021-06-24 |
2023-08-13 |
Not clear |
| Mahindra Rautela, J Senthilnath, Jochen Moll, Srinivasan Gopalakrishna. Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning. Ultrasonics. vol 115. 2021-06-15. PMID:33964601. |
the performance of the trained models is evaluated using loss curve, accuracy, confusion matrix, and receiver-operating characteristics curve. |
2021-06-15 |
2023-08-13 |
Not clear |
| Sergio Márquez-Sánchez, Israel Campero-Jurado, Daniel Robles-Camarillo, Sara Rodríguez, Juan M Corchado-Rodrígue. BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces. Sensors (Basel, Switzerland). vol 21. issue 10. 2021-06-04. PMID:34066186. |
where the gmm gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the lstm obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. |
2021-06-04 |
2023-08-13 |
human |
| Jie Zheng, Chen-Lei Ru, Lu Zhang, Wen-Jun Yin, Hui Zhang, Ji-Zhong Ya. [Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica. vol 46. issue 10. 2021-05-31. PMID:34047105. |
then, partial least squares discriminant analysis(pls-da), support vector machine(svm), and random forest(rf) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(roc). |
2021-05-31 |
2023-08-13 |
Not clear |
| Jie Zheng, Chen-Lei Ru, Lu Zhang, Wen-Jun Yin, Hui Zhang, Ji-Zhong Ya. [Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica. vol 46. issue 10. 2021-05-31. PMID:34047105. |
it was consistent with the confusion matrix of the predicted results. |
2021-05-31 |
2023-08-13 |
Not clear |
| Wafaa A Shalaby, Waleed Saad, Mona Shokair, Fathi E Abd El-Samie, Moawad I Dessouk. COVID-19 Classification Based on Deep Convolution Neural Network Over a Wireless Network. Wireless personal communications. 2021-05-18. PMID:33994667. |
the performance metrics are accuracy, loss, confusion matrix, sensitivity, precision, |
2021-05-18 |
2023-08-13 |
Not clear |
| Tianhao Li, Jundong Wang, Hongyan Xie, Pingsheng Hao, Chun Qing, Yuzhen Zhang, Xin Liao, Tao Lian. Study on the related factors of post-herpetic neuralgia in hospitalized patients with herpes zoster in Sichuan Hospital of Traditional Chinese Medicine based on big data analysis. Dermatologic therapy. vol 33. issue 6. 2021-05-14. PMID:33052606. |
following evaluated the prediction model by roc and confusion matrix, variables importance ranking and variables dependency analysis were performed, resulting in the importance ranking of factors for phn and the dependency between factors and phn. |
2021-05-14 |
2023-08-13 |
Not clear |
| Mohammad Pourhomayoun, Mahdi Shakib. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart health (Amsterdam, Netherlands). vol 20. 2021-05-14. PMID:33521226. |
finally, we used a separate dataset of covid-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model. |
2021-05-14 |
2023-08-13 |
Not clear |
| Arushi Agarwal, Purushottam Sharma, Mohammed Alshehri, Ahmed A Mohamed, Osama Alfarra. Classification model for accuracy and intrusion detection using machine learning approach. PeerJ. Computer science. vol 7. 2021-05-06. PMID:33954233. |
also, the classification reports (precision, recall, and f1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach. |
2021-05-06 |
2023-08-13 |
Not clear |
| David C Vogelsang, Bradley J Erickso. Magician's Corner: 6. TensorFlow and TensorBoard. Radiology. Artificial intelligence. vol 2. issue 3. 2021-05-03. PMID:33937828. |
a simple classifier in tensorflow (version 2) is developed and how to use tensorboard to monitor training progress, to recognize overfitting, and to display other useful information like images in the training set and the confusion matrix is demonstrated. |
2021-05-03 |
2023-08-13 |
Not clear |
| Jung Hwan Kim, Alwin Poulose, Dong Seog Ha. The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance. Sensors (Basel, Switzerland). vol 21. issue 6. 2021-04-27. PMID:33809352. |
the confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the fer 2013 dataset to confirm the real-time testing. |
2021-04-27 |
2023-08-13 |
Not clear |
| Ángel F Villarejo-Ramos, Juan-Pedro Cabrera-Sánchez, Juan Lara-Rubio, Francisco Liébana-Cabanilla. Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model. Frontiers in psychology. vol 12. 2021-04-21. PMID:33868130. |
with respect to this non-parametric technique, we found that the multilayer perceptron model (mlp) for the use of big data applications in companies obtains higher auc values, and a better confusion matrix. |
2021-04-21 |
2023-08-13 |
Not clear |
| Luay Fraiwan, Mohanad Alkhodar. Neonatal sleep stage identification using long short-term memory learning system. Medical & biological engineering & computing. vol 58. issue 6. 2021-04-13. PMID:32281071. |
based on the confusion matrix, the overall true positives percentage reached 95.21%. |
2021-04-13 |
2023-08-13 |
Not clear |
| Kamil Książek, Michał Romaszewski, Przemysław Głomb, Bartosz Grabowski, Michał Cholew. Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks. Sensors (Basel, Switzerland). vol 20. issue 22. 2021-04-08. PMID:33233358. |
we have also presented a model evaluation method based on t-sne and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. |
2021-04-08 |
2023-08-13 |
Not clear |
| Jeeyae Choi, Hee-Tae Jung, Anastasiya Ferrell, Seoyoon Woo, Linda Hadda. Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users. Journal of clinical medicine. vol 10. issue 5. 2021-04-08. PMID:33801175. |
performance of prediction models was measured by root mean square error (rmse) and confusion matrix. |
2021-04-08 |
2023-08-13 |
human |
| Muhammad Irfan, Muhammad Aksam Iftikhar, Sana Yasin, Umar Draz, Tariq Ali, Shafiq Hussain, Sarah Bukhari, Abdullah Saeed Alwadie, Saifur Rahman, Adam Glowacz, Faisal Althobian. Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19. International journal of environmental research and public health. vol 18. issue 6. 2021-04-08. PMID:33809665. |
the capability and prediction accuracy of the hdnns were calculated using the confusion matrix. |
2021-04-08 |
2023-08-13 |
human |