Trying to use temporal and kinematic parameters for the classification in wheelchair badminton. by Ilona Alberca, Bruno Watier, Félix Chénier, Florian Brassart, Mélanie Baconnais, Bryan Le Toquin, Imad Hamri, Jean-Marc Vallier, Arnaud Faupin

“…The clusters results could suggest a potential evolution of the current classification towards three distinct classes of wheelchair badminton players. …”
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An IoT and Machine Learning-based Neonatal Sleep Stage Classification by Awais Abbas, Hafiz Sheraz Sheikh, SaadUllah Farooq Abbasi

“…For this reason, we proposed an algorithm for neonatal sleep-wake classification using machine learning. The proposed research is divided into three steps. …”
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Comprehensive Evaluation and Classification of Interchange Diagrammatic Guide Signs’ Complexity by Yang Li, Xiaohua Zhao, Qing He, Lihua Huang, Jian Rong

“…This study tested 37 types of diagrams on the visual recognition complexity degree in three levels, general level, partial level, and detailed level, and finally seven indexes are selected to evaluation and classification of interchange diagrammatic guide signs’ complexity. …”
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Node Classification Method Based on Hierarchical Hypergraph Neural Network by Feng Xu, Wanyue Xiong, Zizhu Fan, Licheng Sun

“…In addition, the model performs excellently in handling 3D multi-view datasets. Such datasets can be created by capturing 3D shapes and geometric features through sensors or by manual modeling, providing extensive application scenarios for analyzing three-dimensional shapes and complex geometric structures. …”
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Post-variational classical quantum transfer learning for binary classification by Kavitha Yogaraj, Brian Quanz, Tarun Vikas, Arijit Mondal, Samrat Mondal

“…To evaluate generalizability, we tested PVCQTL on three additional binary classification datasets, observing improved accuracy on each. …”
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Golf swing classification with multiple deep convolutional neural networks by Libin Jiao, Rongfang Bie, Hao Wu, Yu Wei, Jixin Ma, Anton Umek, Anton Kos

“…Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network–based classifiers from two perspectives: classification implementations and sensor combinations. …”
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Emotion Classification from Electroencephalographic Signals Using Machine Learning by Jesus Arturo Mendivil Sauceda, Bogart Yail Marquez, José Jaime Esqueda Elizondo

“…This study aimed to evaluate the performance of three neural network architectures—ShallowFBCSPNet, Deep4Net, and EEGNetv4—for emotion classification using the SEED-V dataset. …”
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Angiographic classification of occlusive stenotic lesion of the internal iliac artery by А. А. Moiseev, A. Ya. Bedrov, G. I. Popov, A. E. Shashin, V. A. Kreil, D. V. Ovcharenko, A. V. Biryukov, K. A. Belova, N. A. Yaitskiy

“…An analysis of the branching pattern of the IIA according to the Yamaki classification and an assessment of the degree and prevalence of lesion to its basin, including three arterial segments, were carried out.RESULTS. …”
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Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification by Tales Boratto, Gabriel de Oliveira Costa, Alexsandro Meireles, Anna Klara Sá Teles Rocha Alves, Camila M. Saporetti, Matteo Bodini, Alexandre Cury, Leonardo Goliatt

“…To develop the present study, a dataset of 350 audio files encompassing the three aforementioned registers was constructed. Then, the TSFEL Python library was employed to extract 14 pieces of temporal information from the audio signals for subsequent classification by the employed ML models. …”
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Image Classification of Multimedia Platforms With Texture Algorithms and Neural Networks by Mauricio Sauzameda-Gonzalez, Josue Alvarez-Borrego, Esperanza Guerra-Rosas

“…In this paper, a methodology for real-time image classification on multimedia platforms has been developed. …”
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Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…Abstract Accurate classification of rock masses is an essential task in earth sciences applications. …”
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Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification by Mingquan Hong, Hongcai Zhang, Lihua Wu, Jialiang Chen, Lijin Dai, Lujun Wang, Tengchao Dong, Jinling Yang, Lihua Fang

“…This study utilized 28,421 seismic events and 172,214 waveform data from the Fujian Seismic Network (2010-2022). Three neural network models, VGG, AlexNet, and ResNet, were used to develop a deep-learning model for seismic event classification. …”
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THE GNATOLOGIC CLASSIFICATION OF FACIAL OPTIONS AND ITS VALUE IN CLINIC OF STOMATOLOGY by Dmitry Sergeevich Dmitrienko, Vera Valerievna Timircheva, Vladimir Vyacheslavovich Shkarin, Victoria Otarievna Torokhova, Artyom Vachikovich Mnatsakanyan

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Applying Classification Trees to Stress Patterns of Japanese Loanwords in English by Bradley Lunsford

“…The ctree function of the party package in R is used to create classification trees. The stress pattern is not immediately obvious, so the algorithm is used to discover the significant predictor variables and create a classification tree. …”
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Malware classification method based on static multiple-feature fusion by Bo-wen SUN, Yan-yi HUANG, Qiao-kun WEN, Bin TIAN, Peng WU, Qi LI

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Feature extraction using sparse component decomposition for face classification by Hamid Reza Shahdoosti

“…In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. …”
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Validation of categories of the International Classification of Functioning, Disability and Health for the elderly by Silvana Sidney Costa Santos, Silomar Ilha, Edison Luiz Devos Barlem, Daiane Porto Gautério-Abreu, Bárbara Tarouco da Silva, Inaiá Santos Alves

“…Objective: to validate categories of the International Classification of Functioning, Disability and Health directed to the elderly. …”
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Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…Experimental results on three large malware datasets, MalImg, BIG2015, and BODMAS, demonstrate that the proposed model achieved classification accuracies of 99.68%, 99.45%, and 93.12%, with model sizes of 442 KB, 414 KB, and 423 KB, and prediction times of 14.12 ms, 11.09 ms, and 4.11 ms per image, respectively. …”
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MHANet: A hybrid attention mechanism for retinal diseases classification. by Lianghui Xu, Liejun Wang, Shuli Cheng, Yongming Li

“…In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). …”
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Blood Pressure Classification Using the Method of the Modular Neural Networks by Martha Pulido, Patricia Melin, German Prado-Arechiga

“…The goal is to design the best MNN architecture for achieving an accurate classification. The results of the model show that MNN presents an excellent classification for blood pressure. …”
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