Identification and classification of solid–liquid flow patterns in deviated and horizontal annuli by Di Yao, Xiaofeng Sun, Jingyu Qu

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A BERT-Based Classification Model: The Case of Russian Fairy Tales by Валерий Дмитриевич Соловьев, Марина Ивановна Солнышкина, Andrey Ten, Николай Аркадиевич Прокопьев

“…Method: We pre-train BERT using a collection of three classes of documents and fine-tune it for implementation of a specific application task. …”
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SEM Deep Learning Multiclass Noise Level Classification With Data Augmentation by Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan

“…The network uses a dual-head classifier, consisting of both a standard classification head and an ordinal classification head. …”
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Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness by Sreelakshmi Raveendran, Kala S, Ramakrishnan A G, Ramakrishnan A G, Raghavendra Kenchaiah, Jayakrushna Sahoo, Santhos Kumar, Farsana M K, Ravindranadh Chowdary Mundlamuri, Sonia Bansal, Binu V S, Subasree R

“…The results show that MLP model 2 achieves a classification accuracy of 96.3% using AEC features obtained with a window length of 16s, highlighting the effectiveness of AEC. …”
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Utility of artificial intelligence in a binary classification of soft tissue tumors by Jing Di, Caylin Hickey, Cody Bumgardner, Mustafa Yousif, Mauricio Zapata, Therese Bocklage, Bonnie Balzer, Marilyn M. Bui, Jerad M. Gardner, Liron Pantanowitz, Shadi A. Qasem

“…This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. …”
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A novel mesothelioma molecular classification based on malignant cell differentiation by Jun Liu, Yifan Liu, Yuwei Lu, Wei Zhang, Jiale Yan, Bingnan Lu, Yuntao Yao, Shuyuan Xian, Donghao Lyu, Jiaying Shi, Yuanan Li, Xinru Wu, Chenguang Bai, Jie Zhang, Yuan Zhang

“…However, a molecular classification based on differentiated states of tumor cells remains void. …”
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Microscopic Remaining Oil Classification Method and Utilization Based on Kinetic Mechanism by Yuhang He, Xianbao Zheng, Jiayi Wu, Zhiqiang Wang, Jiawen Wu, Qingyu Wang, Wenbo Gong, Xuecong Gai

“…Three carefully selected mechanical characterization parameters were used: oil–water connectivity, oil–mass specific surface and oil–water area ratio. …”
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Identification and Classification of Prescription Errors at a Tertiary Hospital in Southeast Nigeria by Patricia Uche Ogbo, Chioma Assumpta Okonkwo, Collins Chukwuemeka Magbo, Chidimma Maryrose Ononugbo, Cyndi Chinelo Ofolebe, Nathaniel Chinonso Okafor, Charles Chiemela Nwaobilor, Ogochukwu Ifeoma Ugwuede, Chimezie William Nnaji

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Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders by Shobha Jose, S. Thomas George, M. S. P. Subathra, Vikram Shenoy Handiru, Poornaselvan Kittu Jeevanandam, Umberto Amato, Easter Selvan Suviseshamuthu

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Nymphoplasty: classification and technical refinements Ninfoplastia: classificação e refinamentos técnicos by Fábio Inácio da Cunha, Lúcio Marques da Silva, Laudicély de Araújo Costa, Flávia Roberta Paes Vasconcelos, Giuliano Trombetta Amaral

“…Patients were classified into three groups according to the degree and location of labia minora hypertrophy. …”
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Adaptive Spectral Correlation Learning Neural Network for Hyperspectral Image Classification by Wei-Ye Wang, Yang-Jun Deng, Yuan-Ping Xu, Ben-Jun Guo, Chao-Long Zhang, Heng-Chao Li

“…Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. …”
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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

“…We found 79 SNe: 38 at z < 2, 23 at 2 < z < 3, 8 at 3 < z < 4, 7 at 4 < z < 5, and 3 with undetermined redshifts, where the redshifts are predominantly based on spectroscopic or highly reliable JADES photometric redshifts of the host galaxies. …”
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Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8. by Ying Han, Xingkun Li, Gongxiang Cui, Jie Song, Fengyu Zhou, Yugang Wang

“…Secondly, an ALGC3 (aluminum, Ghost, Concentrated-Comprehensive Convolution Block (C3)) module is introduced to optimize the fusion of Ghost convolution and residual connectivity, resulting in a more lightweight model. …”
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New classification of hypertrophy of the labia minora and correlation with indicated surgical techniques by Andre Gonçalves de Freitas Colaneri

“…RESULTS: A new classification has been proposed. Hypertrophy of the labia minora was classified in four grades: grade 0 (< 1 cm), grade 1 (> 1 cm and < 3 cm), grade 2 (> 3 cm and > 5 cm), and grade 3 (> 5 cm). …”
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Advancing Structural Health Monitoring with Deep Belief Network-Based Classification by Álvaro Presno Vélez, Zulima Fernández Muñiz, Juan Luis Fernández Martínez

“…Compared to traditional unsupervised methods like K-means and PCA, DBNs demonstrated a superior performance in feature representation and classification accuracy. Experimental results showed median cross-validation accuracies of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.04</mn><mo>%</mo></mrow></semantics></math></inline-formula> for ambient data and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.96</mn><mo>%</mo></mrow></semantics></math></inline-formula> for train-induced data, with low variability. …”
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Towards automated recipe genre classification using semi-supervised learning. by Nazmus Sakib, G M Shahariar, Md Mohsinul Kabir, Md Kamrul Hasan, Hasan Mahmud

“…The proposed pipeline named 3A2M+ extends the size of the Named Entity Recognition (NER) list to address missing named entities like heat, time or process from the recipe directions using two NER extraction tools. 3A2M+ dataset provides a comprehensive solution to the various challenging recipe-related tasks, including classification, named entity recognition, and recipe generation. …”
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Gas Detection and Classification Using Neural Network Based Gas Sensors by Munaf Ismail, Sri Arttini Dwi Prasetyowati

“…For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. …”
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Evaluation of the practical application of the category-imbalanced myeloid cell classification model. by Zhigang Hu, Aoru Ge, Xinzheng Wang, Cuisi Ou, Shen Wang, Junwen Wang

“…Therefore, developing a reliable automated model for myeloid cell classification is imperative. This study evaluated the performance of five widely-used classification models on the largest publicly available bone marrow cell dataset (BM). …”
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Bisector Surfaces Through A Common Line of Curvatures and Its Classifications by Muhammed Talat Sarıaydın

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Massive rotator cuff tears: geometric classification, complete repair, and prognosis by Liwei Ying, MA, Qingguo Zhang, MA, Li Ying, PhD, Linlin Sha, MA, Dawei Han, MA, Yu Hui, BA, Quan Gan, BA, Huixia Fan, BA, Junbo Liang, PhD, Xiaobo Zhou, PhD

“…Furthermore, postoperative magnetic resonance imaging scans revealed that there was no case of rotator cuff retear (Sugaya types IV and V) in type Ⅰ, 3.23% rotator cuff retear rate in type Ⅱ, and 22.22% rotator cuff retear rate in type Ⅲ. …”
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