| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Rajesh Kumar, Geetha Subbia. Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach. Sensors (Basel, Switzerland). vol 22. issue 7. 2022-04-12. PMID:35408413. |
in this work, the best performing ml model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. |
2022-04-12 |
2023-08-13 |
Not clear |
| Diana Contreras, Sean Wilkinson, Evangeline Alterman, Javier Hervá. Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake. Natural hazards (Dordrecht, Netherlands). 2022-03-29. PMID:35345448. |
we tested the no-code machine learning platform's performance using a confusion matrix. |
2022-03-29 |
2023-08-13 |
Not clear |
| Rui Du, Yanwei Chen, Tao Li, Liang Shi, Zhengdong Fei, Yuefeng L. Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network. Journal of oncology. vol 2022. 2022-03-29. PMID:35345516. |
furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. |
2022-03-29 |
2023-08-13 |
Not clear |
| Xiaochun Ge, Aimin Zhang, Lihui Li, Qitian Sun, Jianqiu He, Yu Wu, Rundong Tan, Yingxia Pan, Jiangman Zhao, Yue Xu, Hui Tang, Yu Ga. Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile. Experimental and therapeutic medicine. vol 23. issue 4. 2022-03-28. PMID:35340868. |
taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (auc) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). |
2022-03-28 |
2023-08-13 |
human |
| Issei Shinohara, Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Kohei Yamaura, Shintaro Mukohara, Tomoya Yoshikawa, Tatsuo Kato, Takahiro Furukawa, Yuichi Hoshino, Takehiko Matsushita, Ryosuke Kurod. Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images. Diagnostics (Basel, Switzerland). vol 12. issue 3. 2022-03-25. PMID:35328185. |
the model was evaluated by analyzing a confusion matrix and the area under the receiver operating characteristic curve. |
2022-03-25 |
2023-08-13 |
human |
| Iftiaz A Alfi, Md Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazi. A Non-Invasive Interpretable Diagnosis of Melanoma Skin Cancer Using Deep Learning and Ensemble Stacking of Machine Learning Models. Diagnostics (Basel, Switzerland). vol 12. issue 3. 2022-03-25. PMID:35328279. |
for evaluation, we calculated the accuracy, f1-score, cohen's kappa, confusion matrix, and roc curves and identified the best model for classifying skin lesions. |
2022-03-25 |
2023-08-13 |
Not clear |
| Mahdi Rezapour, Khaled Ksaibat. Contributory factors to the severity of single-vehicle rollover crashes on a mountainous area, generalized additive model. International journal of injury control and safety promotion. 2022-03-25. PMID:35333700. |
the results highlighted the superiority of the gam compared with the glm in terms of confusion matrix accuracy and akaike information criterion (aic). |
2022-03-25 |
2023-08-13 |
Not clear |
| Mahmoud Ragab, Samah Alshehri, Gamil Abdel Azim, Hibah M Aldawsari, Adeeb Noor, Jaber Alyami, S Abdel-Khale. COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images. Frontiers in public health. vol 10. 2022-03-21. PMID:35309201. |
we assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and f1-measure. |
2022-03-21 |
2023-08-13 |
Not clear |
| Ningbo Xiao, Zuxun Son. An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model. Computational intelligence and neuroscience. vol 2022. 2022-03-21. PMID:35310589. |
the lstm-svm model is compared with the gate recurrent unit-support vector machines (gru-svm) model, and the comparison results are visualized using a confusion matrix. |
2022-03-21 |
2023-08-13 |
Not clear |
| Darlin Apasrawirote, Pharinya Boonchai, Paisarn Muneesawang, Wannacha Nakhonkam, Nophawan Bunch. Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles. Scientific reports. vol 12. issue 1. 2022-03-20. PMID:35306517. |
the results of the confusion matrix of alexnet showed that misclassification was found between c. megacephala and c. (achoetandrus) rufifacies as well as between c. megacephala and l. cuprina. |
2022-03-20 |
2023-08-13 |
Not clear |
| Fajar Yulianto, Dony Kushardono, Syarif Budhiman, Gatot Nugroho, Galdita Aruba Chulafak, Esthi Kurnia Dewi, Anjar Ilham Pambud. Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data. TheScientificWorldJournal. vol 2022. 2022-03-14. PMID:35281749. |
it shows that there has been an increase of 2% in the accuracy based on the confusion matrix calculation. |
2022-03-14 |
2023-08-13 |
Not clear |
| Seyma Toy, Yusuf Secgin, Zulal Oner, Muhammed Kamil Turan, Serkan Oner, Deniz Seno. A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium. Scientific reports. vol 12. issue 1. 2022-03-12. PMID:35277536. |
as a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. |
2022-03-12 |
2023-08-13 |
Not clear |
| Geng Du-Yan, Wang Jia-Xing, Wang Yan, Liu Xuan-Y. Convolutional neural network is a good technique for sleep staging based on HRV: a comparative analysis. Neuroscience letters. 2022-03-01. PMID:35227774. |
only the hrv sequence was used to classify and identify the four sleep stages of the subject's sleep process: wake(w), light sleep (ls), slow-wave sleep (sws) and rapid eye movement (rem), and the confusion matrix was calculated. |
2022-03-01 |
2023-08-13 |
human |
| Ingrid Pelgrims, Brecht Devleesschauwer, Hans Keune, Tim S Nawrot, Roy Remmen, Nelly D Saenen, Isabelle Thomas, Vanessa Gorasso, Johan Van der Heyden, Delphine De Smedt, Eva Clerc. Validity of self-reported air pollution annoyance to assess long-term exposure to air pollutants in Belgium. Environmental research. 2022-02-26. PMID:35218716. |
finally, the performance of the models to classify individuals in three levels of exposure was assessed by means of a confusion matrix. |
2022-02-26 |
2023-08-13 |
human |
| Hsiu-An Lee, Kuan-Wen Chen, Chien-Yeh Hs. Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD. Cancers. vol 14. issue 4. 2022-02-25. PMID:35205630. |
the roc curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. |
2022-02-25 |
2023-08-13 |
Not clear |
| Ming-Te W. Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom. Scientific reports. vol 12. issue 1. 2022-02-24. PMID:35197547. |
confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom. |
2022-02-24 |
2023-08-13 |
Not clear |
| Ming-Te W. Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom. Scientific reports. vol 12. issue 1. 2022-02-24. PMID:35197547. |
in the end, the accuracy of the proposed algorithms is verified by using the confusion matrix based on machine learning and the minimum cross entropy based on neural networks. |
2022-02-24 |
2023-08-13 |
Not clear |
| Eliéder Prates Romanzini, Rafael Nakamura Watanabe, Natália Vilas Boas Fonseca, Andressa Scholz Berça, Thaís Ribeiro Brito, Priscila Arrigucci Bernardes, Danísio Prado Munari, Ricardo Andrade Rei. Modern livestock farming under tropical conditions using sensors in grazing systems. Scientific reports. vol 12. issue 1. 2022-02-17. PMID:35173245. |
the machine learning (ml) methods used were random forest (rf), convolutional neural net and linear discriminant analysis; the metrics used to determine the best method were accuracy, kappa coefficient, and a confusion matrix. |
2022-02-17 |
2023-08-13 |
cattle |
| Chi Zhang, Haijia Wen, Mingyong Liao, Yu Lin, Yang Wu, Hui Zhan. Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China. Sensors (Basel, Switzerland). vol 22. issue 3. 2022-02-15. PMID:35161914. |
finally, the test data are substituted into models, and the models' effects are verified by the confusion matrix. |
2022-02-15 |
2023-08-13 |
Not clear |
| b' Abdulkadir Karac\\xc4\\xb. VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural computing & applications. 2022-01-31. PMID:35095212.' |
model performances were evaluated using fivefold cross-validation according to recall, specificity, precision, f1-score, confusion matrix, and roc analysis performance metrics. |
2022-01-31 |
2023-08-13 |
Not clear |