Decoding Gestures in Electromyography: Spatiotemporal Graph Neural Networks for Generalizable and Interpretable Classification by Hunmin Lee, Ming Jiang, Jinhui Yang, Zhi Yang, Qi Zhao

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Improved cattle farm classification: leveraging machine learning and linked national datasets by Guy-Alain Schnidrig, Guy-Alain Schnidrig, Rahel Struchen, Sara Schärrer, Dagmar Heim, Daniela Hadorn, Gertraud Schüpbach-Regula, Giulia Paternoster

“…The aim of this study was to outline cattle production types of interest and to allow the classification of Swiss cattle farms according to production type in order to optimize surveillance. …”
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Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data by İrem Karakaya

“…Purpose: This study aims to evaluate the performance of various machine learning and ensemble learning models in classifying delivery times using Amazon delivery data. …”
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Concordance of Prenatal Ultrasonography and Magnetic Resonance Imaging in the Classification of Corpus Callosum Dysplasia by Hui Peng, Xi Chen, Xuejin Zou, Yan Song, Chunlan Cheng, Jianli Zhang

“…Background: Prenatal ultrasound has always been difficult to classify fetal corpus callosum abnormalities. This paper aims to evaluate the added value of fetal magnetic resonance imaging (MRI) to ultrasound in detecting fetal corpus callosum anomalies and the consistency of the classification of prenatal ultrasound and magnetic resonance imaging for corpus callosum anomalies. …”
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A Novel Insight into Paraptosis-Related Classification and Signature in Lower-Grade Gliomas by Xi-Feng Qian, Jia-Hao Zhang, Yue-Xue Mai, Xin Yin, Yu-Bin Zheng, Zi-Yuan Yu, Guo-Dong Zhu, Xu-Guang Guo

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Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques by Mahmut Bağcı, Pınar Kaya Soylu

“…Accuracy scores show that random forest, logistic regression, and support vector classifiers perform competitively for the ORF range classification in both short- and long-term applications. …”
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Dual Convolution Neural Networks of Ensemble Learning with Attention Mechanism for Rice Classification by Cheng Linfeng

“…Machine vision has been widely applied across fields. Image classification is one of the most classic fields. The aim of this project is to develop a dual convolutional neural network for ensemble learning based on the initial model and the res network model, and apply the ensemble model to the rice classification problem. …”
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COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms by Nasiba M. Abdulkareem, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dathar A. Hasan

Subjects: “…COVID-19 Vaccine, Machine learning, Classification algorithm, Dataset, weka…”
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Leveraging Partial Labels for Cervical Lesion Classification via a Multilabel Approach by Margaret Dy Manalo, Kota Aoki, Shuqiong Wu, Mariko Shindo, Yutaka Ueda, Yasushi Yagi

Subjects: “…Multilabel classification…”
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Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images by Soe Ni Ni, J. Tian, Pina Marziliano, Hong-Tym Wong

“…Then, the fuzzy k nearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. …”
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Polish Literature for Children and Young Adults Inspired by Classical Antiquity. A Catalogue. by Rafał Wójcik

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On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms by Farid Ablayev, Marat Ablayev, Joshua Zhexue Huang, Kamil Khadiev, Nailya Salikhova, Dingming Wu

Subjects: “…quantum classification…”
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Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning by Yonghua Xie, Yurong Liu, Qingqiu Fu

“…In view of the SVM classification for the imbalanced sand-dust storm data sets, this paper proposes a hybrid self-adaptive sampling method named SRU-AIBSMOTE algorithm. …”
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An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking by Zafar Iqbal, Majid I. Khan, Shahid Hussain, Asad Habib

“…The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. …”
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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning by Paul A. Constable, Javier O. Pinzon-Arenas, Luis Roberto Mercado Diaz, Irene O. Lee, Fernando Marmolejo-Ramos, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Marek Brabec, David H. Skuse, Dorothy A. Thompson, Hugo Posada-Quintero

“…Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. …”
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Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics by James Palmer, Victor S. Sheng, Travis Atkison, Bernard Chen

Subjects: “…classification…”
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Comparison of the Performance of Pegasos and Traditional Models in the Task of Sentiment Classification of Product Reviews by Zhu Di

“…Sentiment classification of a large number of commodity reviews is very important for customer selection and market trend prediction. …”
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Comparison of Gastric Cancer Risk Classifications Using Conventional and New Pepsinogen Criteria by Tae Sasakabe, Yuki Obata, Sayo Kawai, Yingsong Lin, Shogo Kikuchi

“…We sought to improve risk classification for gastric cancer by adopting the new PG criteria with the addition of an H. pylori antibody test. …”
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A Framework for Coxeter Spectral Classification of Finite Posets and Their Mesh Geometries of Roots by Daniel Simson, Katarzyna Zając

“…Following our paper [Linear Algebra Appl. 433(2010), 699–717], we present a framework and computational tools for the Coxeter spectral classification of finite posets J≡(J,⪯). One of the main motivations for the study is an application of matrix representations of posets in representation theory explained by Drozd [Funct. …”
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Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints by Jun Fukae, Yoshiharu Amasaki, Yuichiro Fujieda, Yuki Sone, Ken Katagishi, Tatsunori Horie, Tamotsu Kamishima, Tatsuya Atsumi

“…Objective To study the classification performance of a pre-trained convolutional neural network (CNN) with transfer learning by artificial joint ultrasonography images in rheumatoid arthritis (RA). …”
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