| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Chuan-Yi Peng, Yin-Feng Ren, Zhi-Hao Ye, Hai-Yan Zhu, Xiao-Qian Liu, Xiao-Tong Chen, Ru-Yan Hou, Daniel Granato, Hui-Mei Ca. A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins. Food research international (Ottawa, Ont.). vol 158. 2022-07-15. PMID:35840220. |
three machine learning algorithms, namely feedforward neural network (fnn), random forest (rf) and support vector machine (svm), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (roc) and area under the curve (auc). |
2022-07-15 |
2023-08-14 |
Not clear |
| Wael Abdalsalam Hamwi, Muhammad Mazen Almustaf. Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity. Informatics in medicine unlocked. 2022-07-13. PMID:35822170. |
after all, the output was one neuron to classify both cases of 0 or 1, followed by a sigmoid function; in addition, we used the adam optimizer owing to the more improved outcomes than what other optimizers conducted; ultimately, we referred to our findings by using a confusion matrix, classification report (recall & precision), sensitivity and specificity; in this approach, we achieved a classification accuracy of 96%. |
2022-07-13 |
2023-08-14 |
Not clear |
| Wei Jia, Tingting Pei, Kai Le. Research on Land Use Planning Based on Multisource Remote Sensing Data. Computational intelligence and neuroscience. vol 2022. 2022-07-11. PMID:35814598. |
the data are integrated, and then, the cbers data and hpf fusion data are used to extract the land use type information of zhenning county, respectively, and a confusion matrix is built based on the field sample points to verify the accuracy, compare and analyze the relative error of the land use type information extraction before and after data fusion, and evaluate the cbers data. |
2022-07-11 |
2023-08-14 |
human |
| Christine M Fisher, Katherine T Peter, Seth R Newton, Andrew J Schaub, Jon R Sobu. Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods. Analytical and bioanalytical chemistry. 2022-07-07. PMID:35796784. |
qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. |
2022-07-07 |
2023-08-14 |
Not clear |
| Umer Saeed, Syed Yaseen Shah, Adnan Zahid, Jawad Ahmad, Muhammad Ali Imran, Qammer H Abbasi, Syed Aziz Sha. Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron. IEEE sensors journal. vol 21. issue 18. 2022-07-05. PMID:35790093. |
moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, f1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: random forest. |
2022-07-05 |
2023-08-14 |
human |
| Chunmei He, Lanqing Zheng, Taifeng Tan, Xianjun Fan, Zhengchun Y. Multi-attention representation network partial domain adaptation for COVID-19 diagnosis. Applied soft computing. 2022-06-29. PMID:35765302. |
the stability and reliability of the proposed method are validated by the confusion matrix and the performance curves experiments. |
2022-06-29 |
2023-08-14 |
human |
| Po-Chang Ko, Ping-Chen Lin, Hoang-Thu Do, You-Fu Huan. P2P Lending Default Prediction Based on AI and Statistical Models. Entropy (Basel, Switzerland). vol 24. issue 6. 2022-06-24. PMID:35741522. |
to evaluate the models, we adopted the confusion matrix series of metrics, auc-roc curve, kolmogorov-smirnov chart (ks), and student's t-test. |
2022-06-24 |
2023-08-14 |
human |
| Namal Rathnayake, Upaka Rathnayake, Tuan Linh Dang, Yukinobu Hoshin. An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm. Sensors (Basel, Switzerland). vol 22. issue 12. 2022-06-24. PMID:35746183. |
the algorithm was validated using two methods: iterations and confusion matrix. |
2022-06-24 |
2023-08-14 |
Not clear |
| Xinlin Liu, Viktor Krylov, Su Jun, Natalya Volkova, Anatoliy Sachenko, Galina Shcherbakova, Jacek Woloszy. Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology. Mathematical biosciences and engineering : MBE. vol 19. issue 7. 2022-06-22. PMID:35730289. |
the reliability of the classification of the spectral-statistical texture images was confirmed by using the true positive rate (tpr) and false positive rate (fpr) metrics calculated on the basis of the confusion matrix. |
2022-06-22 |
2023-08-14 |
Not clear |
| Kanche Anjaiah, P K Dash, Mrutyunjaya Sahan. Detection of faults and DG islanding in PV-Wind DC ring bus microgrid by using optimized VMD based improved broad learning system. ISA transactions. 2022-06-18. PMID:35717214. |
the effectiveness of the proposed avmd based ibls algorithm is verified by its superiority in terms of relative computation time (rct), classification accuracy (ca) with the confusion matrix, and their performance indices by comparing with other existing methods under different case studies. |
2022-06-18 |
2023-08-14 |
Not clear |
| Deepak Kumar Jain, Kesana Mohana Lakshmi, Kothapalli Phani Varma, Manikandan Ramachandran, Subrato Bharat. Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources. Computational intelligence and neuroscience. vol 2022. 2022-06-07. PMID:35669646. |
confusion matrix gives the actual class and predicted class of tumor in an input image. |
2022-06-07 |
2023-08-14 |
Not clear |
| S Kumar, J Pat. Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India. Journal of water and health. vol 20. issue 5. 2022-05-31. PMID:35635776. |
parameters obtained from the confusion matrix, such as accuracy, precision, recall, and fpr, were used to analyze the performance of models. |
2022-05-31 |
2023-08-13 |
Not clear |
| Miguel de la Fuente, Iñaki Rodríguez-Agirretxe, Elena Vecino, Egoitz Astigarraga, Arantxa Acera, Gabriel Barreda-Góme. Elevation of Tear MMP-9 Concentration as a Biomarker of Inflammation in Ocular Pathology by Antibody Microarray Immunodetection Assays. International journal of molecular sciences. vol 23. issue 10. 2022-05-28. PMID:35628448. |
in addition, a confusion matrix was applied, estimating sensitivity at 60%, specificity at 88%, and accuracy at 68%. |
2022-05-28 |
2023-08-13 |
Not clear |
| Mahsa Dehghan Manshadi, Nima Alafchi, Alireza Tat, Milad Mousavi, Amirhosein Mosav. Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate. Polymers. vol 14. issue 10. 2022-05-28. PMID:35631878. |
in addition to analyzing their composition, the receiver operating characteristic (roc) curve and confusion matrix were implemented to evaluate the algorithm's performance. |
2022-05-28 |
2023-08-13 |
Not clear |
| Shubham Rastogi, Sukesh Chandran Nair, Pandiyan Murugan, Asady Sukanya Sukumar, Joy J Mammen, Saravanan Mulla. Overall equipment effectiveness, efficiency and slide review analysis of high-end hematology analyzers. Practical laboratory medicine. vol 30. 2022-05-23. PMID:35601458. |
confusion matrix highlights the difficulty for dxh 800 and dxh 900 to discriminate left shift or blasts with large hyper-segmented neutrophils. |
2022-05-23 |
2023-08-13 |
Not clear |
| Hector Lopez-Almazan, Francisco Javier Pérez-Benito, Andrés Larroza, Juan-Carlos Perez-Cortes, Marina Pollan, Beatriz Perez-Gomez, Dolores Salas Trejo, María Casals, Rafael Llobe. A deep learning framework to classify breast density with noisy labels regularization. Computer methods and programs in biomedicine. vol 221. 2022-05-20. PMID:35594581. |
the confusion matrix (cm) - cnn network used implements an architecture that models each radiologist's noisy label. |
2022-05-20 |
2023-08-13 |
Not clear |
| Somaieh Amraee, Maryam Chinipardaz, Mohammadali Charoosae. Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects. Visual computing for industry, biomedicine, and art. vol 5. issue 1. 2022-05-09. PMID:35534747. |
then, by examining the resulting confusion matrix, the performances of the hog and lbp approaches are compared for these four classes. |
2022-05-09 |
2023-08-13 |
Not clear |
| Emmanuel Mfateneza, Pierre Claver Rutayisire, Emmanuel Biracyaza, Sanctus Musafiri, Willy Gasafari Mpabuk. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset. BMC pregnancy and childbirth. vol 22. issue 1. 2022-05-05. PMID:35509018. |
evaluation metrics methods specifically confusion matrix, accuracy, precision, recall, f1 score, and area under the receiver operating characteristics (auroc) were used to evaluate the performance of predictive models. |
2022-05-05 |
2023-08-13 |
Not clear |
| Issei Shinohara, Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Shintaro Mukohara, Tomoya Yoshikawa, Ryosuke Kurod. Ultrasound With Artificial Intelligence Models Predicted Palmer 1B TFCC Injuries. Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association. 2022-04-21. PMID:35447195. |
the purpose of this study is to calculate the diagnostic accuracy from the confusion matrix using deep learning (dl) on ultrasound (us) images of palmer 1b triangular fibrocartilage complex (tfcc) injury. |
2022-04-21 |
2023-08-13 |
Not clear |
| Yaokun Xia, Zening Huang, Tingting Chen, Lilan Xu, Gengzhen Zhu, Wenqian Chen, Guanyu Chen, Shuxiang Wu, Jianming Lan, Xu Lin, Jinghua Che. Sensitive fluorescent detection of exosomal microRNA based on enzymes-assisted dual-signal amplification. Biosensors & bioelectronics. vol 209. 2022-04-14. PMID:35421672. |
moreover, the proposed biosensor exhibits an 83.9% accuracy in classifying patients with gc or pc lesions and healthy donors using a confusion matrix. |
2022-04-14 |
2023-08-13 |
Not clear |