| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Álvaro Fernández-Rodríguez, Francisco Velasco-Álvarez, Manon Bonnet-Save, Ricardo Ron-Angevi. Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair. Frontiers in neuroscience. vol 12. 2020-10-01. PMID:30002615. |
then, variables related to the time employed and commands selected by the user or parameters related to the confusion matrix were applied. |
2020-10-01 |
2023-08-13 |
Not clear |
| Faraz Akrim, Tariq Mahmood, Tamara Max, Muhammad Sajid Nadeem, Siddiqa Qasim, Shaista Andlee. Assessment of bias in morphological identification of carnivore scats confirmed with molecular scatology in north-eastern Himalayan region of Pakistan. PeerJ. vol 6. 2020-10-01. PMID:30038872. |
we used a confusion matrix to assess different types of errors associated with carnivore scat identification. |
2020-10-01 |
2023-08-13 |
Not clear |
| Charles Chilaka, Steven Carr, Nabil Shalaby, Wolfgang Banzha. Prediction of normalized signal strength on DNA sequencing microarrays by n-grams within a neural network model. Bioinformation. vol 15. issue 6. 2020-10-01. PMID:31312075. |
pattern recognition results showed high percentage confusion matrix values along the diagonal and receiver operating characteristic curves were clustered in the upper left corner, both indices of good predictive performance. |
2020-10-01 |
2023-08-13 |
Not clear |
| James A Bartholomai, Hermann B Frieboe. Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques. Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology. vol 2018. 2020-10-01. PMID:31312809. |
model accuracy is measured by a confusion matrix for classification and by root mean square error (rmse) for regression. |
2020-10-01 |
2023-08-13 |
Not clear |
| In-Ho Bae, Soo-Geun Wang, Soon-Bok Kwon, Seong-Tae Kim, Eui-Suk Sung, Jin-Choon Le. Clinical Application of Two-Dimensional Scanning Digital Kymography in Discrimination of Diplophonia. Journal of speech, language, and hearing research : JSLHR. vol 62. issue 10. 2020-10-01. PMID:31577518. |
additionally, we compared the diagnostic accuracy of each method using a binary classifier in confusion matrix and convenience of discrimination, based on the time required for interpretation. |
2020-10-01 |
2023-08-13 |
human |
| Zhongheng Zhan. A gentle introduction to artificial neural networks. Annals of translational medicine. vol 4. issue 19. 2020-09-30. PMID:27826573. |
finally, the prediction power of ann is examined using confusion matrix and average accuracy. |
2020-09-30 |
2023-08-13 |
human |
| Danica Schaffer-Smith, Jennifer J Swenson, Blake Barbaree, Matthew E Reite. Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds. Remote sensing of environment. vol 193. 2020-09-30. PMID:29123324. |
our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix. |
2020-09-30 |
2023-08-13 |
Not clear |
| Xue Hu, Zebo Y. Diagnosis of mesothelioma with deep learning. Oncology letters. vol 17. issue 2. 2020-09-30. PMID:30675203. |
a confusion matrix, f-measure and a receiver operating characteristic (roc) curve were used to evaluate the performance of each model. |
2020-09-30 |
2023-08-13 |
human |
| Muhammad Musa Uba, Ren Jiadong, Muhammad Noman Sohail, Muhammad Irshad, Kaifei Y. Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria. Healthcare technology letters. vol 6. issue 4. 2020-09-30. PMID:31531223. |
the dm techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. |
2020-09-30 |
2023-08-13 |
Not clear |
| Syed M S Reza, Randall Mays, Khan M Iftekharuddi. Multi-fractal Detrended Texture Feature for Brain Tumor Classification. Proceedings of SPIE--the International Society for Optical Engineering. vol 9414. 2020-09-29. PMID:27980354. |
quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. |
2020-09-29 |
2023-08-13 |
Not clear |
| Xiashuang Wang, Guanghong Gong, Ni Li, Shi Qi. Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization. Frontiers in human neuroscience. vol 13. 2020-09-29. PMID:30846934. |
experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. |
2020-09-29 |
2023-08-13 |
human |
| Jong-Eun Kim, Na-Eun Nam, June-Sung Shim, Yun-Hoa Jung, Bong-Hae Cho, Jae Joon Hwan. Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs. Journal of clinical medicine. vol 9. issue 4. 2020-09-28. PMID:32295304. |
the accuracy, precision, recall, and f1 score were calculated for each network using a confusion matrix. |
2020-09-28 |
2023-08-13 |
Not clear |
| H M Ravindu T Bandara, K S Priyanayana, A G Buddhika P Jayasekara, D P Chandima, R A R C Gopur. An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation. Applied bionics and biomechanics. vol 2020. 2020-09-28. PMID:32399060. |
accuracy of the intelligent system was determined with the use of confusion matrix. |
2020-09-28 |
2023-08-13 |
human |
| Bomi Jeong, Hyunjeong Cho, Jieun Kim, Soon Kil Kwon, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Seoksu Hong, Tae-Young He. Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data. Diagnostics (Basel, Switzerland). vol 10. issue 6. 2020-09-28. PMID:32570782. |
to solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and f-1 measure for each class. |
2020-09-28 |
2023-08-13 |
Not clear |
| David Miller, Yujia Wang, George Kesidi. When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time. Neural computation. vol 31. issue 8. 2020-09-17. PMID:31260390. |
we propose a purely unsupervised anomaly detector (ad) that, unlike previous works, (1) models the joint density of a deep layer using highly suitable null hypothesis density models (matched in particular to the nonnegative support for rectified linear unit (relu) layers); (2) exploits multiple dnn layers; and (3) leverages a source and destination class concept, source class uncertainty, the class confusion matrix, and dnn weight information in constructing a novel decision statistic grounded in the kullback-leibler divergence. |
2020-09-17 |
2023-08-13 |
Not clear |
| Shervin Minaee, Rahele Kafieh, Milan Sonka, Shakib Yazdani, Ghazaleh Jamalipour Souf. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical image analysis. vol 65. 2020-09-17. PMID:32781377. |
besides sensitivity and specificity rates, we also present the receiver operating characteristic (roc) curve, precision-recall curve, average prediction, and confusion matrix of each model. |
2020-09-17 |
2023-08-13 |
Not clear |
| Herve Nguendon Kenhagho, Georg Rauter, Raphael Guzman, Philippe C Cattin, Azhar Za. Optoacoustic Tissue Differentiation Using a Mach-Zehnder Interferometer. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. vol 66. issue 9. 2020-09-04. PMID:31226071. |
as is seen in the confusion matrix, the experimental-based scores of hard and soft bones, fat, muscles, and skin yielded average classification errors (with leave-one-out cross validation) of 0.11%, 57.69%, 0.06%, 0.14%, and 2.92%, respectively. |
2020-09-04 |
2023-08-13 |
Not clear |
| Andreas Hinterreiter, Peter Ruch, Holger Stitz, Martin Ennemoser, Jurgen Bernard, Hendrik Strobelt, Marc Strei. ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion. IEEE transactions on visualization and computer graphics. vol PP. 2020-08-03. PMID:32746284. |
the confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. |
2020-08-03 |
2023-08-13 |
Not clear |
| Incheol Seo, Hyunsu Le. Predicting transgenic markers of a neuron by electrophysiological properties using machine learning. Brain research bulletin. vol 150. 2020-07-24. PMID:31125599. |
using linear regression, random forest, and an artificial neural network, we assessed the performance of supervised machine learning models by comparing the prediction accuracy or the confusion matrix. |
2020-07-24 |
2023-08-13 |
Not clear |
| Jae-Hong Lee, Seong-Nyum Jeon. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study. Medicine. vol 99. issue 26. 2020-07-08. PMID:32590758. |
the test dataset was used to assess the accuracy, sensitivity, specificity, receiver operating characteristic curve, area under the receiver operating characteristic curve (auc), and confusion matrix compared between deep cnn and periodontal specialist.we found that the deep cnn architecture (auc = 0.971, 95% confidence interval 0.963-0.978) and board-certified periodontist (auc = 0.925, 95% confidence interval 0.913-0.935) showed reliable classification accuracies.this study demonstrated that deep cnn architecture is useful for the identification and classification of dental implant systems using panoramic and periapical radiographic images. |
2020-07-08 |
2023-08-13 |
Not clear |