Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

“…Convolutional neural network, long short term memory network, residual neural network (ResNet), and InceptionV3 show marginally lower performance than the extra tree classifier. …”
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The PediPERForm Learning Network congenital perfusion registry by Mejak Brian L., Reagor James A., Olshove Vince F., Likosky Donald S.

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Dementia and Cognitive Impairment in an Urban Multiethnic Indigenous Community from Amazonas by Camila Carlos Bezerra, Noeli das Neves Toledo, Rebeca Lima Sampaio, Breno José Alencar Pires Barbosa, Sonia Maria Dozzi Brucki, Juliana Nery Souza-Talarico

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Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…The top performing n (n=3,4,5) classifiers were ensembled with meta-learner using stacking strategy. …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…The Extra Trees Classifier algorithm was found to be the best ML technique for predictions, according to the accuracy rates (92.3%) with an Area Under Curve of 0.92. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

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DKA Prediction in Children Using Artificial Intelligence: Improved Emergency Care by Arifa Parveen, Mohsina Riffat, Sarang Shaikh

“…The best-performing model from the models selected in the methods part was found to be the extra trees classifier with overall accuracy, precision, recall, f1-score, and MSE of 81%, 78%, 81%, 73%, and 18%. …”
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Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques by Momotaz Begum, Mehedi Hasan Shuvo, Imran Ashraf, Abdullah Al Mamun, Jia Uddin, Md Abdus Samad

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Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG by Moez Hizem, Mohamed Ould-Elhassen Aoueileyine, Samir Brahim Belhaouari, Abdelfatteh EL Omri, Ridha Bouallegue

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RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms by Hameda A. Sennary, Ghada Abozaid, Ashraf Hemeida, Alexey Mikhaylov

“…The classifiers involved are Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), and the Extra Trees (ET) in addition to the convolutional neural network (CNN). …”
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Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors by Vatsala Anand, Ajay Khajuria, Rupendra Kumar Pachauri, Vinay Gupta

“…Abstract The kidney is an important organ that helps clean the blood by removing waste, extra fluids, and harmful substances. It also keeps the balance of minerals in the body and helps control blood pressure. …”
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Improved anticipatory scheduling algorithm based on workload characteristic and service time evaluation by Jin-zhong CHEN, Nian-min YAO, Shao-bin CAI, Mei-ling SUN

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Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification by Radiah Haque, Hui-Ngo Goh, Choo-Yee Ting, Albert Quek, M.D. Rakibul Hasan

“…Feature selection methods, including Boruta and Extra Tree Classifier (ETC) are employed to identify the optimal feature set, guided by a sliding window algorithm for automatic feature selection. …”
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Fast Detection and Classification of Ink by Ion Mobility Spectrometry and Artificial Intelligence by Matej Petrik, Emanuel Matas, Martin Sabo, Michal Ries, Stefan Matejcik

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Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

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A reliable score-based routing protocol using a fog-assisted intrusion detection system in vehicular ad-hoc networks by Samira Tahajomi Banafshehvaragh, Mani Zarei, Amir Masoud Rahmani

“…These algorithms include the decision tree, random forest, and extra trees. Deploying the IDS in the fog server solves the data diversity problem in the classifier training. …”
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Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction by Bahram Shahri, Ali Tajik, Mohsen Moohebati, Vahid Mahdavizadeh

“…The Extra Trees Classifier performed best (ROC-AUC = 0.82). …”
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Lightweight machine learning framework for efficient DDoS attack detection in IoT networks by Mamoona Nawaz, Shireen Tahira, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…Utilizing the NSL-KDD dataset, the framework employs an Extra Trees Classifier (ETC) for feature selection, reducing dimensionality while retaining critical attributes. …”
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Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques by Eduardo A. Gerlein, Francisco Calderón, Martha Zequera-Díaz, Roozbeh Naemi

“…Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model’s predictive accuracy. …”
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