Brain CT image classification based on mask RCNN and attention mechanism by Shoulin Yin, Hang Li, Lin Teng, Asif Ali Laghari, Ahmad Almadhor, Michal Gregus, Gabriel Avelino Sampedro

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Machine state classification of electric track circuit by means of logistic regression by D. V. Borisenko, I. V. Prisukhina, S. A. Lunev

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Feature selection based on Mahalanobis distance for early Parkinson disease classification by Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ahmed M. Mahdi, Ayman Ibaida

“…Significant improvements in classification performance were observed across all models. …”
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Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

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Classification and functional repair and reconstruction of lip and perilabial defects following oncosurgery by CHEN Weiliang

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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

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The JADES Transient Survey: Discovery and Classification of Supernovae in the JADES Deep Field by Christa DeCoursey, Eiichi Egami, Justin D. R. Pierel, Fengwu Sun, Armin Rest, David A. Coulter, Michael Engesser, Matthew R. Siebert, Kevin N. Hainline, Benjamin D. Johnson, Andrew J. Bunker, Phillip A. Cargile, Stephane Charlot, Wenlei Chen, Mirko Curti, Shea DeFour-Remy, Daniel J. Eisenstein, Ori D. Fox, Suvi Gezari, Sebastian Gomez, Jacob Jencson, Bhavin A. Joshi, Sanvi Khairnar, Jianwei Lyu, Roberto Maiolino, Takashi J. Moriya, Robert M. Quimby, George H. Rieke, Marcia J. Rieke, Brant Robertson, Melissa Shahbandeh, Louis-Gregory Strolger, Sandro Tacchella, Qinan Wang, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Yossef Zenati

“…Here, we present the survey, sample, search parameters, spectral energy distributions, light curves, and classifications. Even at z ≥ 2, the NIRCam data quality is high enough to allow SN classification via multiepoch light-curve fitting with confidence. …”
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Classification theorem on irreducible representations of the q-deformed algebra U′q(son) by N. Z. Iorgov, A. U. Klimyk

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Classification of Riau Batik Motifs Using the Convolutional Neural Network (CNN) Algorithm by Dhea Amanda Ramadhan, Dian Ramadhani

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Concept and Classification of Functions of State Policy in the Field of Ensuring Economic Security of the State by D. O. Koshikov

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Reducing Data Volume in News Topic Classification: Deep Learning Framework and Dataset by Luigi Serreli, Claudio Marche, Michele Nitti

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Automated strabismus detection and classification using deep learning analysis of facial images by Mahsa Yarkheir, Motahhareh Sadeghi, Hamed Azarnoush, Mohammad Reza Akbari, Elias Khalili Pour

“…The proposed deep learning model achieved an accuracy of 86.38% for binary classification and 92.7% for multi-class 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

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Anxiety classification in virtual reality using biosensors: A mini scoping review. by Deniz Mevlevioğlu, Sabin Tabirca, David Murphy

“…Accuracy of anxiety classification for two-output models ranged from 75% to 96.4%; accuracy for three-output models ranged from 67.5% to 96.3%; accuracy for four-output models ranged from 38.8% to 86.3%. …”
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Detection of weeds in vegetables using image classification neural networks and image processing by Huiping Jin, Kang Han, Kang Han, Hongting Xia, Bo Xu, Xiaojun Jin

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Vegetation Classification in a Mountain–Plain Transition Zone in the Sichuan Basin, China by Wenqian Bai, Zhengwei He, Yan Tan, Guy M. Robinson, Tingyu Zhang, Xueman Wang, Li He, Linlong Li, Shuang Wu

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Instability of revised Korean Developmental Screening Test classification in first year of life by Ji Eun Jeong, You Min Kim, Na Won Lee, Gyeong Nam Kim, Jisuk Bae, Jin Kyung Kim

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Classifications for Proliferative Vitreoretinopathy (PVR): An Analysis of Their Use in Publications over the Last 15 Years by Salvatore Di Lauro, Mustafa R. Kadhim, David G. Charteris, J. Carlos Pastor

“…Furthermore, 3 authors (2.9%) used modified-customized classifications and 4 (3.8%) classification errors were identified. …”
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Fungicide Resistance Action Committee’s (FRAC) Classification Scheme of Fungicides According to Mode of Action by Frederick M. Fishel, Megan M. Dewdney

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Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach by Yanwei Zhang, Stephen S. Gao

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