Feature selection algorithm for uncertain text classification by WANG Bo, JIA Yan, YANG Shu-qiang, ZHOU Bin

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Rat models of frozen shoulder: Classification and evaluation by Hezirui Gu, Wenqing Xie, Hengzhen Li, Shuguang Liu, Yusheng Li

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Metatheory and Classification of Digital Human Rights and Freedoms by G. V. Alexeyev

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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

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Zero-Shot Classification of Art With Large Language Models by Tatsuya Tojima, Mitsuo Yoshida

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Dual Model for International Roughness Index Classification and Prediction by Noelia Molinero-Pérez, Laura Montalbán-Domingo, Amalia Sanz-Benlloch, Tatiana García-Segura

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Wanted: A Paradigm Shift in the Classification of Coronary Artery Disease by Thomas George, MD

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Cancer Classification Using Pattern Recognition and Computer Vision Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

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Lightweight decentralized learning-based automatic modulation classification method by Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI

“…In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage.The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method.The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A.Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%).In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning.…”
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Analysis and Classification of Fake News Using Sequential Pattern Mining by M. Zohaib Nawaz, M. Saqib Nawaz, Philippe Fournier-Viger, Yulin He

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An Enhanced Approach Using AGS Network for Skin Cancer Classification by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim, Youjin Shin

“…The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. …”
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A Novel Computer Virus Propagation Model under Security Classification by Qingyi Zhu, Chen Cen

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Research on text sentiment classification based on improved feature selection method by Mingxin LIU, Jing CHEN, Qiyuan WANG

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A geometric approach for accelerating neural networks designed for classification problems by Mohsen Saffar, Ahmad Kalhor, Ali Habibnia

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Semi-Supervised Learning With Wafer-Specific Augmentations for Wafer Defect Classification by Uk Jo, Seoung Bum Kim

“…This approach preserves essential regions crucial for wafer defect pattern classification and thus improving model performance. Our approach achieves a macro F1-score of 0.841 with only 5% labeled data, surpassing state-of-the-art methods by 6.2% compared to WaPIRL and 7.5% compared to Manivannan’s method. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

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Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression by Cristiana Valente, Elisa D’Alessandro, Michele Iester

“…To evaluate the agreement between different methods in detection of glaucomatous visual field progression using two classification-based methods and four statistical approaches based on trend analysis. …”
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An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification by Mohammed Bourennane, Hilal Naimi

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Land cover classification for Siberia leveraging diverse global land cover datasets by Munseon Beak, Kazuhito Ichii, Yuhei Yamamoto, Ruci Wang, Beichen Zhang, Ram C. Sharma, Tetsuya Hiyama

“…The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. …”
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Non-uniform Fourier transform based image classification in single-particle Cryo-EM by ZiJian Bai, Jian Huang

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