| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Ling-Li Chen, Ting-Ting Shi, Min-Zhen Yin, Mei Yang, Hua-Sheng Peng, Ming-En Cheng, Lei Li, Xiao-Bo Zhan. [Contrastive analysis of extraction of Polygonatum cyrtonema planting area based on data of "Resource 3"]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica. vol 46. issue 2. 2021-03-02. PMID:33645111. |
in order to determine the accuracy and reliability of classification results, the accuracy of six supervised classification results was evaluated by confusion matrix method, and the advantages and disadvantages of six supervised classification methods for extracting p. cyrtonema field planting area were compared and analyzed. |
2021-03-02 |
2023-08-13 |
Not clear |
| Davide Chicco, Niklas Tötsch, Giuseppe Jurma. The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData mining. vol 14. issue 1. 2021-02-07. PMID:33541410. |
the matthews correlation coefficient (mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. |
2021-02-07 |
2023-08-13 |
Not clear |
| Tripti Goel, R Murugan, Seyedali Mirjalili, Deba Kumar Chakrabartt. Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network. Cognitive computation. 2021-02-02. PMID:33520007. |
the performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), f1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (roc) curves, indicate that it performs better than state-of-the-art methods. |
2021-02-02 |
2023-08-13 |
Not clear |
| Andrea Macarulla Rodriguez, Zeno Geradts, Marcel Worrin. Likelihood Ratios for Deep Neural Networks in Face Comparison. Journal of forensic sciences. vol 65. issue 4. 2021-01-25. PMID:32396227. |
the results show that with low-quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. |
2021-01-25 |
2023-08-13 |
human |
| Aboozar Ghaffar. Image compression-encryption method based on two-dimensional sparse recovery and chaotic system. Scientific reports. vol 11. issue 1. 2021-01-15. PMID:33432027. |
finally, to reduce the correlation between adjacent pixels in the compressed matrix, and obtain a uniform distribution in the encrypted image, a compressed scrambling matrix based on chaotic confusion is used. |
2021-01-15 |
2023-08-13 |
Not clear |
| S Bobadilla-Suarez, C Ahlheim, A Mehrotra, A Panos, B C Lov. Measures of Neural Similarity. Computational brain & behavior. vol 3. issue 4. 2021-01-10. PMID:33225218. |
our technique uses a decoding approach to assess the information present in a brain region, and the similarity measures that best correspond to the classifier's confusion matrix are preferred. |
2021-01-10 |
2023-08-13 |
Not clear |
| Björn Friedrich, Carolin Lübbe, Andreas Hei. Analyzing the Importance of Sensors for Mode of Transportation Classification. Sensors (Basel, Switzerland). vol 21. issue 1. 2021-01-07. PMID:33383854. |
for this analysis, we subtract the baseline confusion matrix from a confusion matrix of a network trained with a left-out sensor modality (difference confusion matrix) and we visualize the low-level features from the lstm layers. |
2021-01-07 |
2023-08-13 |
Not clear |
| Wenda Zhang, Dapeng Wan. Damage identification using deep learning and long-gauge fiber Bragg grating sensors. Applied optics. vol 59. issue 33. 2020-12-29. PMID:33361988. |
the performance of the cnn is evaluated by training history and the confusion matrix. |
2020-12-29 |
2023-08-13 |
Not clear |
| Zhiying Xu, Fatima Rashid Sheykhahmad, Noradin Ghadimi, Navid Razmjoo. Computer-aided diagnosis of skin cancer based on soft computing techniques. Open medicine (Warsaw, Poland). vol 15. issue 1. 2020-12-19. PMID:33336044. |
after image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (sbo) has been adopted and its efficiency has been indicated by the confusion matrix. |
2020-12-19 |
2023-08-13 |
Not clear |
| Qian Sun, Lin Sun, Meiyan Shu, Xiaohe Gu, Guijun Yang, Longfei Zho. Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image. Plant phenomics (Washington, D.C.). vol 2019. 2020-12-16. PMID:33313529. |
classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. |
2020-12-16 |
2023-08-13 |
Not clear |
| Tzu-Kang Lin, Yi-Hsiu Chie. Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy. Entropy (Basel, Switzerland). vol 21. issue 1. 2020-12-07. PMID:33266757. |
a confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. |
2020-12-07 |
2023-08-13 |
Not clear |
| Majid Shadman Roodposhti, Jagannath Aryal, Arko Lucieer, Brett A Brya. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest. Entropy (Basel, Switzerland). vol 21. issue 1. 2020-12-07. PMID:33266794. |
traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. |
2020-12-07 |
2023-08-13 |
Not clear |
| James L Gutmann, Aleksandra Palatyńska-Ulatowsk. Historical Perspectives on Mechanical Teeth Separators - Evolution and Function. Journal of the history of dentistry. vol 68. issue 2. 2020-11-10. PMID:32852272. |
because separators were often intertwined with matrix bands, confusion was present as both the separators and matrix bands were often referred jointly as matrices in textbooks, journal publications and dental catalogues. |
2020-11-10 |
2023-08-13 |
Not clear |
| Poulomi Pal, Sudipta Ghosh, Bhabani Prasad Chattopadhyay, Kalyan Kumar Saha, Manjunatha Mahadevapp. Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2020. 2020-10-27. PMID:33019334. |
ten different performance metrics was studied using the confusion matrix. |
2020-10-27 |
2023-08-13 |
human |
| Yu-Tsung Tsai, Feng-Jung Yang, Hong-Mau Lin, Jiang-Chou Yeh, Bor-Wen Chen. Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients. Scientific reports. vol 9. issue 1. 2020-10-26. PMID:31341234. |
finally, a receiver operating characteristic curve and confusion matrix was used to evaluate the standard of performance of these models. |
2020-10-26 |
2023-08-13 |
Not clear |
| Carlos A Robles-Rubio, Robert E Kearney, Gianluca Bertolizio, Karen A Brow. Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals. PloS one. vol 15. issue 9. 2020-10-26. PMID:32915810. |
performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and f-score. |
2020-10-26 |
2023-08-13 |
Not clear |
| Zeynab Karimzadeh Motlagh, Ali Lotfi, Saeid Pourmanafi, Saeedreza Ahmadizadeh, Alireza Soffiania. Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics. Environmental monitoring and assessment. vol 192. issue 11. 2020-10-13. PMID:33040184. |
the confusion matrix of the 2018 lulc map was built using a total of 525 ground truth points and yielded a kappa coefficient and overall accuracy of 78% and 82%, respectively. |
2020-10-13 |
2023-08-13 |
Not clear |
| Zeynab Karimzadeh Motlagh, Ali Lotfi, Saeid Pourmanafi, Saeedreza Ahmadizadeh, Alireza Soffiania. Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics. Environmental monitoring and assessment. vol 192. issue 11. 2020-10-13. PMID:33040184. |
moreover, the confusion matrix constructed base on the sentinel-2 map, as a reference, to judge the predicted 2018 lulc map with a kappa coefficient of 88%. |
2020-10-13 |
2023-08-13 |
Not clear |
| Varun Tiwari, Vinay Kumar, Mir Abdul Matin, Amrit Thapa, Walter Lee Ellenburg, Nishikant Gupta, Sunil Thap. Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PloS one. vol 15. issue 8. 2020-10-07. PMID:32813701. |
the results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (aug 2018), non-flooded (jan 2018) and previous year's flooded season (aug 2016, aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. |
2020-10-07 |
2023-08-13 |
Not clear |
| Xulei Qin, Guolan Lu, Ioannis Sechopoulos, Baowei Fe. Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features. Proceedings of SPIE--the International Society for Optical Engineering. vol 9034. 2020-10-01. PMID:25426271. |
the dice scores and the confusion matrix are utilized to evaluate the classified results. |
2020-10-01 |
2023-08-13 |
Not clear |