| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Endang Sri Rahayu, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama, Mauridhi Hery Purnom. A Combination Model of Shifting Joint Angle Changes with 3D-Deep Convolutional Neural Network to Recognize Human Activity. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2024-02-29. PMID:38421841. |
model performance was evaluated using the confusion matrix. |
2024-02-29 |
2024-03-03 |
human |
| Heyu Zhang, Yuqiao Zheng, Jieshan L. A novel bearing current signal diagnosis method combining variational modal decomposition and improved random forests. The Review of scientific instruments. vol 95. issue 2. 2024-02-27. PMID:38411468. |
finally, the accuracy rate and confusion matrix are adopted to evaluate the prediction effects of both established and traditional models. |
2024-02-27 |
2024-03-02 |
Not clear |
| Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Arak. Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study. Frontiers in neurorobotics. vol 18. 2024-02-23. PMID:38390525. |
we evaluated the validity of logistic regression analysis by using several indicators, such as the area under the curve (auc), and a confusion matrix. |
2024-02-23 |
2024-02-25 |
human |
| Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Arak. Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study. Frontiers in neurorobotics. vol 18. 2024-02-23. PMID:38390525. |
furthermore, after building a confusion matrix, the calculated binary accuracy, sensitivity (recall), and specificity were 0.80, 0.80, and 0.81, respectively, indicated high accuracy. |
2024-02-23 |
2024-02-25 |
human |
| Zhengyang Lan, Mathieu Lempereur, Gwenael Gueret, Laetitia Houx, Marine Cacioppo, Christelle Pons, Johanne Mensah, Olivier Rémy-Néris, Abdeldjalil Aïssa-El-Bey, François Rousseau, Sylvain Brochar. Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning. Computers in biology and medicine. vol 171. 2024-02-13. PMID:38350399. |
we evaluated the accuracy, sensitivity, specificity, f1 score, area under the curve (auc) score, and confusion matrix of the predictions by resnet, lstm, and inceptiontime deep learning architectures for time series data. |
2024-02-13 |
2024-02-16 |
human |
| Luelue Huang, Miaoling Liu, Bin Li, Bimal Chitrakar, Xu Dua. Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans. Foods (Basel, Switzerland). vol 13. issue 3. 2024-02-10. PMID:38338523. |
the classification effect and misclassification of the model were analyzed via confusion matrix. |
2024-02-10 |
2024-02-12 |
Not clear |
| Yu-Jen Fang, Chien-Wei Huang, Riya Karmakar, Arvind Mukundan, Yu-Ming Tsao, Kai-Yao Yang, Hsiang-Chen Wan. Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer. Cancers. vol 16. issue 3. 2024-02-10. PMID:38339322. |
the evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and f1-score of the trained model. |
2024-02-10 |
2024-02-12 |
Not clear |
| Chitrabhanu B Gupta, Debraj Basu, Timothy K Williams, Lucas P Neff, Michael A Johnson, Nathan T Patel, Aravindh S Ganapathy, Magan R Lane, Fatemeh Radaei, Chen-Nee Chuah, Jason Y Adam. Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data. Scientific reports. vol 14. issue 1. 2024-01-26. PMID:38278825. |
model performance was assessed using the area under the receiver operating characteristic curve (auroc), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. |
2024-01-26 |
2024-01-29 |
Not clear |
| Angela Lii, Jonathan Temte, Shari Barlow, Maureen Goss, Emily Temte, Jen Zaborek, Amra Uzicani. Assessment and comparison of the ILI case definition in clinical and school-based community settings: ORCHARDS/IISP. Annals of family medicine. vol 21. issue Suppl 3. 2024-01-25. PMID:38271207. |
relationships between influenza and each categorical variable were described by the confusion matrix, sensitivity, and specificity. |
2024-01-25 |
2024-01-28 |
human |
| Erum Yousef Abbasi, Zhongliang Deng, Arif Hussain Magsi, Qasim Ali, Kamlesh Kumar, Asma Zubed. Optimizing Skin Cancer Survival Prediction with Ensemble Techniques. Bioengineering (Basel, Switzerland). vol 11. issue 1. 2024-01-22. PMID:38247920. |
the performance was evaluated and interpreted using accuracy, precision, recall, f1 score, confusion matrix, and roc curves, where the voting method achieved a promising accuracy of 99%. |
2024-01-22 |
2024-01-24 |
Not clear |
| Xiyuan Liu, Liying Wang, Hongyan Yan, Qingjiao Cao, Luyao Zhang, Weiguo Zha. Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features. Biomimetics (Basel, Switzerland). vol 9. issue 1. 2024-01-22. PMID:38248606. |
the evaluation indicators include confusion matrix, accuracy, precision, recall rate, f1-score, and accuracy and error rate. |
2024-01-22 |
2024-01-24 |
Not clear |
| Ronpichai Chokesuwattanaskul, Aisawan Petchlorlian, Piyoros Lertsanguansinchai, Paramaporn Suttirut, Narut Prasitlumkum, Suphot Srimahachota, Wacin Buddhar. Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients. Medical sciences (Basel, Switzerland). vol 12. issue 1. 2024-01-22. PMID:38249079. |
the accuracy of the prediction on confusion matrix was used to assess model performance. |
2024-01-22 |
2024-01-24 |
Not clear |
| Yulin Sun, Weisheng Dong, Xin Li, Le Dong, Guangming Shi, Xuemei Xi. TransVQA: Transferable Vector Quantization Alignment for Unsupervised Domain Adaptation. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2024-01-18. PMID:38231815. |
in this paper, we present a novel model named transferable vector quantization alignment for unsupervised domain adaptation (transvqa), which integrates the transferable transformer-based feature extractor (trans), vector quantization domain alignment (vqa), and mutual information weighted maximization confusion matrix (mimc) of intra-class discrimination into a unified domain adaptation framework. |
2024-01-18 |
2024-01-20 |
Not clear |
| Zeyuan Zhang, Zheyuan Chang, Jingyun Huang, Geng Leng, Wenbo Xu, Yuewu Wang, Zhenwei Xie, Jiawei Yan. Enhancing soil texture classification with multivariate scattering correction and residual neural networks using visible near-infrared spectra. Journal of environmental management. vol 352. 2024-01-18. PMID:38237335. |
the resnet model demonstrated a marked superiority in classifying datasets with similar features, as observed by the confusion matrix analysis. |
2024-01-18 |
2024-01-21 |
Not clear |
| Yongtian Cheng, Pablo A Pérez-Díaz, K V Petrides, Johnson L. Monte Carlo simulation with confusion matrix paradigm - A sample of internal consistency indices. Frontiers in psychology. vol 14. 2024-01-15. PMID:38222847. |
monte carlo simulation with confusion matrix paradigm - a sample of internal consistency indices. |
2024-01-15 |
2024-01-17 |
Not clear |
| Yongtian Cheng, Pablo A Pérez-Díaz, K V Petrides, Johnson L. Monte Carlo simulation with confusion matrix paradigm - A sample of internal consistency indices. Frontiers in psychology. vol 14. 2024-01-15. PMID:38222847. |
this present study proposes a design based on a common statistic evaluation procedure in psychology and machine learning, using a confusion matrix with four cells: true positive, true negative, false negative modified, and false positive modified. |
2024-01-15 |
2024-01-17 |
Not clear |
| Coby Penso, Lior Frenkel, Jacob Goldberge. Confidence Calibration of a Medical Imaging Classification System that is Robust to Label Noise. IEEE transactions on medical imaging. vol PP. 2024-01-15. PMID:38224509. |
we draw on the fact that the confusion matrix of the noisy labels can be expressed as the matrix product between the confusion matrix of the clean labels and the label noises. |
2024-01-15 |
2024-01-18 |
Not clear |
| Hongbo Chen, Eldan Cohen, Dulaney Wilson, Myrtede Alfte. Improving Patient Safety Event Report Classification with Machine Learning and Contextual Text Representation. Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting. vol 67. issue 1. 2024-01-12. PMID:38213998. |
furthermore, the confusion matrix of the best classifier exposes latent deficiencies in the pse reports' classification taxonomy, such as the multi-class nature of pse and conceptually related event types. |
2024-01-12 |
2024-01-14 |
Not clear |
| Qingzhen Sun, Jinguang L. A method for extracting small water bodies based on DEM and remote sensing images. Scientific reports. vol 14. issue 1. 2024-01-09. PMID:38191904. |
taking the water bodies in xinyang city as an example, the overall accuracy of the confusion matrix was the highest, with an overall accuracy of 93.0% compared with the traditional index method. |
2024-01-09 |
2024-01-10 |
Not clear |
| Zepeng Wang, Jianping Chen, Zechen Lian, Fei Li, Lu Pang, Yabo Xi. Influence of buffer distance on environmental geological hazard susceptibility assessment. Environmental science and pollution research international. 2024-01-09. PMID:38194173. |
the results show that through the confusion matrix and roc curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. |
2024-01-09 |
2024-01-10 |
Not clear |