Design of Deep Learning-Based Pressure Injury Stage Classification Device by Wahmisari Priharti, Husneni Mukhtar, I Made Prastha Giriwara, I Made Andi Majesta, Wayan Abin Bena Bimantara

“…Testing showed a classification accuracy of 83.3% with an average classification duration of 2.24 s. …”
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Zero-BertXGB: An Empirical Technique for Abstract Classification in Systematic Reviews by Mohammad Shariful Islam, Mohammad Abu Tareq Rony, Md Rasel Hossain, Samah Alshathri, Walid El-Shafai

“…Abstract classification in systematic reviews (SRs) is a crucial step in evidence synthesis but is often time-consuming and labour-intensive. …”
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Financial Solvency of Russian Regions in 2010-2014: Continued Classification Analysis by I. A. Vinyukov, E. V. Maevsky, P. V. Jagodowsky

“…This article is a continuation of the first work done on the State task of the Financial University of 2013 [1-3], in which the classification of the regions of the Russian Federation according to the state statistics for 2005-2011 was proposed. …”
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Densenet Model for Binary Glaucoma Classification Performance Assessment with Texture Feature by Wildan Jameel Hadi, Amal Sufiuh Ajrash, Sahar Muneam Salman, Mays jalal jasim, Mina Taha Ibrahim

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DeepGray: Malware Classification Using Grayscale Images with Deep Learning by Haodi Jiang, Harshitha Polsani, Yuexin Liu

“…The study harnesses the power of deep learning and transfer learning, utilizing established neural network architectures such as VGG16, InceptionV3, Efficientnetv2b0, and Vision Transformers (ViT) for malware classification. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

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Etiological classification of high sedimentation rate and relation with other inflammatory markers by Sultan Gozde Temiz, Baris Sagcan, Helin Tantekin, Kadem Arslan

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Machine learning and transfer learning techniques for accurate brain tumor classification by Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

“…Transfer learning applied to image data using a modified GoogLeNet model further enhanced classification accuracy to 99.3 %. These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. …”
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Hyperspectral Image Classification Based on Two-Branch Feature Fusion Network by Qiongdan Huang, Liang Li, Mengyang Zhao, Jiapeng Wang, Shilin Kang

“…Effective discriminative spectral-spatial feature representation is crucial for hyperspectral image classification (HSIC). Some current methods typically extract spectral and spatial information directly from spectral-spatial 3D patches, without considering the correlation between features, resulting in a high number of misclassifications at the boundaries of land cover classes. …”
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Wood Species Classification in Open Set Using an Improved NNO Classifier by Ke-Xin Zhang, Peng Zhao

“…A wood species classification scheme was developed based on open set using an improved Nearest Non-Outlier (NNO) classifier. …”
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BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification by Yaoyao Zhu, Xiuding Cai, Xueyao Wang, Xiaoqing Chen, Zhongliang Fu, Yu Yao

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RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification by Giacomo Chiarot, Sebastiano Vascon, Claudio Silvestri, Idoia Ochoa

“…For this reason, we propose a Recurrent Autoencoder for Time-series Compression and Classification, termed RAT-CC, that allows to perform any classification task on the compressed representation without needing to reconstruct the original time-series data. …”
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Scenario Identification and Classification to Support the Assessment of Advanced Driver Assistance Systems by Zafer Kayatas, Dieter Bestle

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Classification of Articles from Mass Media by Categories and Relevance of the Subject Area by Vladislav Dmitrievich Larionov, Ilya Vyacheslavovich Paramonov

“…The research is devoted to classification of news articles about P. G. Demidov Yaroslavl State University (YarSU) into 4 categories: “society”, “education”, “science and technologies”, “not relevant”.The proposed approaches are based on using the BERT neural network and methods of machine learning: SVM, Logistic Regression, K-Neighbors, Random Forest, in combination of different embedding types: Word2Vec, FastText, TF-IDF, GPT-3. …”
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Bronchial Lavage in the Treatment of Severe Bronchopulmonary Pathology in Adults. Approaches to Classification by E. U. Bonitenko, A. V. Shchegolev, S. A. Vasilev, N. A. Belyakova, A. I. Kuzmin, E. D. Sokolova

“…In addition, it was not possible to find in the literature a classification of either BL in general or used for therapeutic purposes in particular, which significantly complicates the standardization of procedures for its use in various diseases.Aim of study To determine possible classification characteristics, as well as indications, contraindications for therapeutic BL in adults and possible complications that may arise, based on the analysis of literature data.Results Therapeutic BL can be carried out both as planned and for health reasons. …”
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Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification by Xiaojuan Cai, Ya Zhou, Jie Ren, Jinrong Wei, Shiyu Lu, Hanbing Gu, Weizhe Xu, Xun Zhu

“…We developed five AI models using distinct classification algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine) that integrate demographic data, cytological findings, and an AI-assisted ultrasound diagnostic system for thyroid nodule assessment. …”
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Breast asymmetry: literature review and a new proposal for clinical classification by Gladstone Eustáquio de Lima Faria, Dov Charles Goldenberg, Ricardo Frota Boggio

“…The correct diagnosis, taking into account the existing classification systems, is imperative for achieving the best results. …”
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The Application of ImmunohistoChemical Markers for Diagnosis, Classification and Grading of Neuroendocrine Tumors by Kaswer Musa Al-Turaihi, Roaa hameed Al-Waidh

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SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification by Bin Wang, Gongchao Chen, Juan Wen, Linfang Li, Songlin Jin, Yan Li, Ling Zhou, Weidong Zhang

“…With various corn seed varieties exhibiting significant internal structural differences, accurate classification is crucial for planting, monitoring, and consumption. …”
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Classification of finger movements through optimal EEG channel and feature selection by Murside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Yalcin Isler

“…Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.MethodsIn this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. …”
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