| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Yen-Chi Hsu, Cheng-Yao Hong, Ming-Sui Lee, Davi Geiger, Tyng-Luh Li. ABC-Norm Regularization for Fine-Grained and Long-Tailed Image Classification. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-07-12. PMID:37432822. |
specifically, for each training batch, we construct an adaptive batch prediction (abp) matrix and establish its corresponding adaptive batch confusion norm (abc-norm). |
2023-07-12 |
2023-08-14 |
Not clear |
| Alex Vinicius da Silva Rodrigues, Luciane Silva Martello, Verônica Madeira Pacheco, Edson José de Souza Sardinha, André Levi Viana Pereira, Rafael Vieira de Sous. Thermal signature: A method to extract characteristics from infrared thermography data applied to the development of animal heat stress classifier models. Journal of thermal biology. vol 115. 2023-07-11. PMID:37433235. |
the models based on different ann architectures were compared through metrics of the confusion matrix between predicted and measured data, obtaining better results with 8 ts ranges. |
2023-07-11 |
2023-08-14 |
Not clear |
| Sriraamshanjiev Natarajan, Mohanraj Thangamuthu, Sakthivel Gnanasekaran, Jegadeeshwaran Rakkiyanna. Digital Twin-Driven Tool Condition Monitoring for the Milling Process. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420597. |
the prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a probabilistic neural network (pnn). |
2023-07-08 |
2023-08-14 |
Not clear |
| Haron W Gichuhi, Mark Magumba, Manish Kumar, Roy William Mayeg. A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district. PLOS global public health. vol 3. issue 7. 2023-07-04. PMID:37399173. |
five classification machine learning algorithms, logistic regression (lr), artificial neural networks (ann), support vector machines (svm), random forest (rf), and adaboost were trained, and evaluated by computing their accuracy, f1 score, precision, recall, and the area under the receiver operating curve (auc) through the aid of a confusion matrix. |
2023-07-04 |
2023-08-14 |
Not clear |
| Dezhi Shan, Siyu Wang, Junjie Wang, Jun Lu, Junhong Ren, Juan Chen, Daming Wang, Peng Q. Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability. Frontiers in neurology. vol 14. 2023-07-03. PMID:37396779. |
the confusion matrix, receiver operating characteristic (roc) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. |
2023-07-03 |
2023-08-14 |
Not clear |
| Man-Soo Kim, Ryu-Kyoung Cho, Sung-Cheol Yang, Jae-Hyeong Hur, Yong I. Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs. Bioengineering (Basel, Switzerland). vol 10. issue 6. 2023-06-28. PMID:37370563. |
after processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. |
2023-06-28 |
2023-08-14 |
Not clear |
| Man-Soo Kim, Ryu-Kyoung Cho, Sung-Cheol Yang, Jae-Hyeong Hur, Yong I. Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs. Bioengineering (Basel, Switzerland). vol 10. issue 6. 2023-06-28. PMID:37370563. |
processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. |
2023-06-28 |
2023-08-14 |
Not clear |
| V Sathya Preiya, V D Ambeth Kuma. Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes. Diagnostics (Basel, Switzerland). vol 13. issue 12. 2023-06-28. PMID:37370878. |
to assess the effectiveness of the proposed technique, the study presented simulation results, including a confusion matrix and receiver operating characteristic curve. |
2023-06-28 |
2023-08-14 |
Not clear |
| Yara Zayed, Ahmad Hasasneh, Chakib Tad. Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features. Diagnostics (Basel, Switzerland). vol 13. issue 12. 2023-06-28. PMID:37371002. |
the evaluation of the system using the accuracy, precision, recall, f1-score, confusion matrix, and receiver operating characteristic (roc) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, hr, and gfcc through the deep learning process. |
2023-06-28 |
2023-08-14 |
Not clear |
| Gerardo Ibarra-Vazquez, María Soledad Ramí Rez-Montoya, Hugo Terashim. Gender prediction based on University students' complex thinking competency: An analysis from machine learning approaches. Education and information technologies. 2023-06-26. PMID:37361781. |
in this study, we consider the following data analyses: 1) predict students' gender based on their perception of complex thinking competency and sub-competencies from a 25 items questionnaire, 2) analyze models' performance during training and testing stages, and 3) study the models' prediction bias through a confusion matrix analysis. |
2023-06-26 |
2023-08-14 |
Not clear |
| Gerardo Ibarra-Vazquez, María Soledad Ramí Rez-Montoya, Hugo Terashim. Gender prediction based on University students' complex thinking competency: An analysis from machine learning approaches. Education and information technologies. 2023-06-26. PMID:37361781. |
the confusion matrix analysis revealed partiality in gender prediction among all machine learning models, even though we have applied an oversampling method to reduce the imbalance dataset. |
2023-06-26 |
2023-08-14 |
Not clear |
| Davide Chicco, Giuseppe Jurma. A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index. Journal of biomedical informatics. 2023-06-23. PMID:37352899. |
even if assessing binary classifications is a common task in scientific research, no consensus on a single statistic summarizing the confusion matrix has been reached so far. |
2023-06-23 |
2023-08-14 |
Not clear |
| Jino Mathew, Rohit Kshirsagar, Dzariff Z Abidin, James Griffin, Stratis Kanarachos, Jithin James, Miltiadis Alamaniotis, Michael E Fitzpatric. A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data. Scientific reports. vol 13. issue 1. 2023-06-20. PMID:37336914. |
we analyse the classification performance based on precision, recall, f1-score, specificity, confusion matrix, roc-auc curves, and geometric mean score (gms) measures. |
2023-06-20 |
2023-08-14 |
Not clear |
| Lili Liao, Linying Ye, Litao Huang, Ting Yao, Xiaomin Liang, Ling Chen, Mei She. Changes in the microbial community after vaginal fluid exposure in different simulated forensic situations. Forensic science international. vol 349. 2023-06-20. PMID:37339565. |
both fresh and exposed vaginal samples of the same individuals could mostly be clustered and clearly distinguished from different individuals, showing the potential of individual identification, and the confusion matrix value of body fluid identification for vaginal samples was 1. |
2023-06-20 |
2023-08-14 |
Not clear |
| A Usha Ruby, J George Chellin Chandran, T J Swasthika Jain, B N Chaithanya, Renuka Pati. RFFE - Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus. AIMS public health. vol 10. issue 2. 2023-06-12. PMID:37304588. |
the predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (rocauc). |
2023-06-12 |
2023-08-14 |
Not clear |
| Giuliano Armano, Andrea Mancon. Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks. PloS one. vol 18. issue 5. 2023-05-18. PMID:37200293. |
to this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. |
2023-05-18 |
2023-08-14 |
Not clear |
| Ivan Brkić, Marko Ševrović, Damir Medak, Mario Mile. Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments. Sensors (Basel, Switzerland). vol 23. issue 9. 2023-05-14. PMID:37177608. |
the study also provided the standard performance measure for object recognition, mean average precision (map), as well as the values for the confusion matrix, precision, recall, and f1 score for each class as benchmark values for future studies. |
2023-05-14 |
2023-08-14 |
Not clear |
| Sandy Nohemy Hernández Pérez, Francisco David Pérez Reynoso, Carlos Alberto González Gutiérrez, María De Los Ángeles Cosío León, Rocío Ortega Palacio. EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT. Sensors (Basel, Switzerland). vol 23. issue 9. 2023-05-14. PMID:37177757. |
wavelet transform was used to obtain information in the frequency domain characterizing the eog signal with a bandwidth of 0.5 to 50 hz; training results were obtained with the implementation of k-nearest neighbor (knn) 69.4%, a support vector machine (svm) of 76.9% and decision tree (dt) 60.5%, checking the accuracy through the jaccard index and other metrics such as the confusion matrix and roc (receiver operating characteristic) curve. |
2023-05-14 |
2023-08-14 |
human |
| Arti Tiwari, Micha Silver, Arnon Karniel. A deep learning approach for automatic identification of ancient agricultural water harvesting systems. International journal of applied earth observation and geoinformation : ITC journal. vol 118. 2023-05-14. PMID:37179742. |
a confusion matrix of object-based classification revealed an overall accuracy of 86% and a kappa coefficient of 0.79. |
2023-05-14 |
2023-08-14 |
Not clear |
| Oumer Abdulkadir Ebrahim, Getachew Derbe. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021. Scientific reports. vol 13. issue 1. 2023-05-13. PMID:37179444. |
moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. |
2023-05-13 |
2023-08-14 |
Not clear |