MC Classifier: A Classifier for 3D Mechanical Components Based on Geometric Prior Using Graph Neural Network and Attention by Zipeng Lin, Zhenguo Nie

“…We benchmark the performance of MC Classifier against state-of-the-art models and demonstrate its competitive potential in 3D mechanical component classification. Our findings suggest that MC Classifier has significant potential to advance 3D mechanical component classification. …”
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Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity by Vladimir A. Maksimenko, Semen A. Kurkin, Elena N. Pitsik, Vyacheslav Yu. Musatov, Anastasia E. Runnova, Tatyana Yu. Efremova, Alexander E. Hramov, Alexander N. Pisarchik

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Predictive value of sub classification of focal segmental glomerular sclerosis in Oxford classification of IgA nephropathy by Fang Yu, Xuejing Zhu, Shuguang Yuan, Xiaojun Chen, Zheng Li, Zhong Qu, Hong Liu, Lin Sun, Fuyou Liu

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Multi-Feature Fusion-Based Speech Disorder Classification Using MobileNetV3-EfficientNetB7, Linformer-Performer, and SHAP-Aware XGBoost by Abdul Rahaman Wahab Sait, Suresh Sankaranarayanan, P. Gouthaman

“…Traditional speech disorders (SD) detection relies on subjective analysis, resulting in inconsistent outcome. Direct voice classification lacks effective approaches to capture temporal dependencies. …”
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Anoikis classification of lung squamous cell carcinoma reveals correlation with clinical prognosis and immune characteristics by Yixin Zhai, Cheng Li, Xiang He, Wenqi Wu, Donghui Xing, Kaiping Luo, Zhigang Zhao

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Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach by Martina Srutova, Vaclav Kremen, Lenka Lhotska

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Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network by Ammar Jawad Kadhim, Hadi Seyedarabi, Reza Afrouzian, Fadhil Sahib Hasan

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MHAGuideNet: a 3D pre-trained guidance model for Alzheimer’s Disease diagnosis using 2D multi-planar sMRI images by Yuanbi Nie, Qiushi Cui, Wenyuan Li, Yang Lü, Tianqing Deng

“…Methods The study introduces MHAGuideNet, a classification method incorporating a guidance network utilizing multi-head attention. …”
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A rare case of three single rooted permanent second molars: A diagnosis with cone beam computed tomography (CBCT) by Monika koul, Mrinalini Shahi, Ahsan Abdullah, Vinod Upadhyay

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Statistical analysis of Sunspot and total solar irradiance effects on the climate in kirkuk area for three solar cycles (22,23,24) by Heba Abdulla attya, Wafaa H. A. Zaki, Omer sabah ibrahiem Al-Tamimi

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Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods by Saiprasad Potharaju, Swapnali N. Tambe, G. Madhukar Rao, M. V. V. Prasad Kantipudi, Kalyan Devappa Bamane, Mininath Bendre

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A novel deep learning approach to classify 3D foot types of diabetic patients by Pui-ling Li, Qin-feng Xiao, Kit-lun Yick, Qi-long Liu, Li-ying Zhang

“…Abstract Diabetes mellitus is a worldwide epidemic that leads to significant changes in foot shape, deformities, and ulcers. Precise classification of diabetic foot not only helps identify foot abnormalities but also facilitates personalized treatment and preventive measures through the engineering design of foot orthoses. …”
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Traffic Road Sign Detection and Classification by Mehdi Fartaj, Sedigheh Ghofrani

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MSAmix-Net: Diabetic Retinopathy Classification by Jianyun Gao, Shu Li, Yiwen Chen, Rongwu Xiang

“…Our proposed MSAmix-Net achieved an accuracy of 82.3% in the five-class classification task on the combined dataset of APTOS-2019 and Messidor-2, and an accuracy of 93.9% in the three-class classification task on the DRAC-2022 dataset. …”
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Classification of petroleum origin and integrity by FTIR by FJ. Guzmán-Osorio, VI. Domínguez-Rodríguez, RH. Adams, CE. Lobato-García, A. Guerrero-Peña, JR. Barajas-Hernández

“…The canonic functions derived from the discriminate analysis had correlation coefficients of 0.994, 0.900, and 0.867, among the variables studied and the classification factor. The method is efficient for the proposed classifications and can be useful for a range of applications.…”
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Mullerian anomalies: revisiting imaging and classification by Rashmi Dixit, Chitty Suvarna Duggireddy, Gaurav Shanker Pradhan

“…The three phases of embryological development of Mullerian duct structures are described. …”
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Classification of hoarding and comorbid neuropsychiatric symptoms by Sara K. Nutley, Catherine W. Striley, Linda B. Cottler, Joseph Eichenbaum, Rachel L. Nosheny, R. Scott Mackin, Carol A. Mathews

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Data Quality, Semantics, and Classification Features: Assessment and Optimization of Supervised ML-AI Classification Approaches for Historical Heritage by Valeria Cera, Giuseppe Antuono, Massimiliano Campi, Pierpaolo D’Agostino

“…This study analyzes the influence of three key factors—annotator specialization, point cloud density, and sensor type—in the supervised classification of architectural elements by applying the Random Forest (RF) algorithm to datasets related to the architectural typology of the Franciscan cloister. …”
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Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends by Fariha Eusufzai, Aldrin Nippon Bobby, Farzana Shabnam, Saifur Rahman Sabuj

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Classification by production: an alternative criterion to categorization by Luis Yáñez, Nidia Rojas, Ricardo Aparicio, Manuel Mazzei, Eugenio Maldonado, Jairo Fernández, Nicola Fabbozzo, Rafael Rincón, Rafael Urdaneta

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