Differential Classification of Dementia by E. Mohr, P. Brouwers, J. J. Claus, S. E. Purdon, M. Gagnon, T. N. Chase

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A new clinical classification of congenital biliary dilatation – HUAXI CBD classification by Zhenyu Xie, Siyu Pu, Shuguang Jin, Bo Xiang, Jiayin Yang, Lvnan Yan

“…According to the HUAXI CBD classification method, 240 cases were type I, 48 cases were type II, 10 cases were type III, and 2 cases were type IV. …”
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Land-cover classification with an expert classification algorithm using digital aerial photographs by Alberto Perea, José Meroño, María Aguilera, José de la Cruz

“…The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. …”
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Optimizing Imbalanced Data Classification: Under Sampling Algorithm Strategy with Classification Combination by Nauval Dwi Primadya, Adhitya Nugraha, Sahrul Yudha Fahrezi, Ardytha Luthfiarta

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Association rules of fuzzy soft set based classification for text classification problem by Dede Rohidin, Noor A. Samsudin, Mustafa Mat Deris

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Enhancing the efficiency of patent classification: a multimodal classification approach for design patents by Xiaodong Xie, Jie Wu, Mengjia Xiang, Jianting Tang, Yongxiang Sheng

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The BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification by Komang Ayu Triana Indah, I Ketut Gede Darma Putra, I Made Sudarma, Rukmi Sari Hartati, Minho Jo

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Alpha and Omega Classification of β-Lactamase/Transpeptidase-like Superfamily Proteins Based on the Comparison of Their Structural Catalytic Cores by Alexander I. Denesyuk, Konstantin Denessiouk, Mark S. Johnson, Vladimir N. Uversky

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Liver Trauma Management: Three-Year Experience in a Tertiary Trauma Center. by Agron Dogjani, Kastriot Haxhirexha, Basri Lenjani, Kastriot Subashi, Aferdita Ademi, Amarildo Blloshmi, Klevis Doci, Elma Bregaj

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Thalamic pleomorphic xanthoastrocytoma central nervous system World Health Organization grade 3 by Hirotaka Takita, MD, PhD, Taro Shimono, MD, PhD, Kosuke Nakajo, MD, PhD, Tsutomu Ichinose, MD, PhD, Sayaka Tanaka, MD, Junko Hirato, MD, PhD, Hayato Ikota, MD, PhD, Maciej Pas, MD, PhD, Haruna Sakamoto, MD, Shodai Hiramatsu, MD, Yukio Miki, MD, PhD

“…We present a case of thalamic PXA CNS WHO grade 3. A 71-year-old male presented with progressive right hemiparesis over three months. …”
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Segmentation and Classification of Interstitial Lung Diseases Based on Hybrid Deep Learning Network Model by Surendra Reddy Vinta, B. Lakshmi, M. Aruna Safali, G. Sai Chaitanya Kumar

“…Then, we developed a MobileUNetV3 to classify five ILD classes. The ILD database is used to test the proposed approach. …”
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A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects by Zeynep Akkutay-Yoldar, Mehmet Türkay Yoldar, Yiğit Burak Akkaş, Sibel Şurak, Furkan Garip, Oğuzcan Turan, Bengisu Ekizoğlu, Osman Can Yüca, Aykut Özkul, Barış Ünver

“…To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed to detect and classify label-free cytopathic effects (CPEs) induced by SARS-CoV-2, BAdV-1, BPIV3, BoAHV-1, and two strains of BoGHV-4 in Vero and MDBK cell lines. …”
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Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification by Zhiwen Hou, Boyu Wang, Jingrui Liu, Yumeng He, Yuxuan Yao

“…This study proposes a lightweight and efficient classification method for Power Quality Disturbances (PQDs) using the PowerMobileNet model, which combines the S-transform for time-frequency feature extraction and the MobileNetV3-CBAM neural network for enhanced classification performance. …”
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IMPLEMENTATION OF KNOWLEDGE MANAGEMENT SYSTEM IN BUSINESS PROCESSES MANAGEMENT OF 3PL-PROVIDERS by N. Nosyrev

Subjects: “…3pl-провайдер…”
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Mapping of sugarcane crop area in the Paraná State using Landsat/TM/OLI and IRS/LISS-3 images by Clóvis Cechim Junior, Jerry A. Johann, João F. G. Antunes

“…The mappings were conducted through the supervised Maximum likelihood classification (Maxver) achieving, on average, an overall accuracy of 94.13% and kappa index of 0.82. …”
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Laryngeal disease classification using voice data: Octave-band vs. mel-frequency filters by Jaemin Song, Hyunbum Kim, Yong Oh Lee

“…Results: OFSE with 1/3 octave band filters outperformed MFCC in classification accuracy, especially in multi-class classification including laryngeal cancer, benign mucosal disease, and vocal fold paralysis groups (0.9398 ± 0.0232 vs. 0.7061 ± 0.0561). …”
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The fatigue life estimation of the porous AlSi9Cu3 alloy based on the classification of pore geometry using multivariate probability distribution density by Dejan Tomažinčič, Branislav Panić, Marko Nagode, Jernej Klemenc

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Nomogram Incorporating Inflammatory Index, Pathology, and Molecular Classification for Predicting Recurrence in Patients with Stage I-III Endometrial Cancer: A Multi-Institutional Study by Xiao Y, Zheng Y, Tu Y, Tian C, Yu J, Lin H, Wen T, Jiang P, Wang Y

“…Integrating the HALP score can help clinicians identify high-risk patients and tailor personalized treatment strategies.Keywords: endometrial cancer, prognosis, nomogram, HALP scores, adjuvant therapy, molecular classification…”
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Letter to the editor: “Classification of open tibia fractures: the rationale for a new classification system” by Alex Trompeter, PhD, Hugh Furness, MBBS, Nikolaos Kanakaris, PhD, Edward Tozer, MBBS, Matthew Costa, PhD

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Application of Log Lithofacies Classification Model Based on Clustering-Support Vector Classification Method by HE Xianhong, LI Kesai, XU Jiacheng, FU Meiyan, YANG Yufei, SUN Jiaqi

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