Breaking barriers in ICD classification with a robust graph neural network for hierarchical coding by Suyang Xi, Jiesen Shi, Jiachen Yan, MingJing Lin, Xinyi Zhou, Yuan Cheng, Hong Ding, Chia Chao Kang

“…Abstract The accurate classification of International Classification of Diseases (ICD) codes is a complex and critical multi-label task in clinical documentation, involving the assignment of diagnostic codes to medical discharge summaries. …”
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A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment by Yuezhong Wu, Xuehao Shen, Qiang Liu, Falong Xiao, Changyun Li

“…Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. …”
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EFFICIENCY ASSESSMENT OF EUCLIDEAN AND MAKHALANOBIS DISTANCES FOR SOLVING A MAJOR TEXT CLASSIFICATION PROBLEM by Anna V. Glazkova

“…Moreover, there has been little coverage of this issue in the works of Russian researchers. Method A comparison of the relative efficiencies of using Euclid and Mahalanobis distances was carried out within the framework of the implementation of an intelligent system for text automatic classification based on the age category of their recipients. …”
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MCDGMatch: Multilevel Consistency Based on Data-Augmented Generalization for Remote Sensing Image Classification by Pingping Liu, Xiaofeng Liu, Zetong Liu, Haodong Li, Qiuzhan Zhou

“…The exponential growth of remote sensing image data and the high cost of manual annotation have led to insufficient labeled data, limiting classification performance. Semi-supervised methods can address this issue, but most existing approaches lack multilevel consistency constraints in both embedding space and prediction probabilities, resulting in weak feature expressiveness. …”
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Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study by Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy

“…The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. …”
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Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability by Kazuki Fujiwara

“…Machine learning classification models often struggle with imbalanced datasets, leading to poor performance in minority classes. …”
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CIL-LLM: Incremental Learning Framework Based on Large Language Models for Category Classification by WANG Xiaoyu, LI Xin, HU Mianning, XUE Di

“…To enhance classification accuracy in class-incremental learning (CIL) models for text classification and mitigate the issue of catastrophic forgetting, this paper introduces a CIL framework based on a large language model (CIL-LLM). …”
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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI by Sayyed Shahid Hussain, Pir Masoom Shah, Hussain Dawood, Xu Degang, Ahmad Alshamayleh, Muhammad Adnan Khan, Taher M. Ghazal

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Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery by Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ul-Haq, David Blake, Naeem Janjua

“…Recent advances in deep learning for hyperspectral image classification have shown exceptional performance in resource management and environmental planning through land use classification. …”
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Research on Fine-Grained Visual Classification Method Based on Dual-Attention Feature Complementation by Min Huang, Ke Li, Xiaoyan Yu, Chen Yang

“…An increasing number of fine-grained classification models utilize attention mechanisms to extract distinguishable regions to address this issue, yet they overlook other equally distinguishable but less obvious features. …”
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Dual-Domain Multi-Task Learning-Based Domain Adaptation for Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Shizhuo Deng, Tong Jia, Zhaokui Li, Dongyue Chen

“…Enhancing target domain discriminability is a key focus in Unsupervised Domain Adaptation (UDA) for HyperSpectral Image (HSI) classification. However, existing methods overlook bringing similar cross-domain samples closer together in the feature space to achieve the indirect transfer of source domain classification knowledge. …”
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Land use/land cover (LULC) classification using hyperspectral images: a review by Chen Lou, Mohammed A. A. Al-qaness, Dalal AL-Alimi, Abdelghani Dahou, Mohamed Abd Elaziz, Laith Abualigah, Ahmed A. Ewees

“…Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI classification remains a critical and much-debated issue. …”
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Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks by Huong Hoang Luong, Hai Thanh Nguyen, Nguyen Thai-Nghe

“…Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. …”
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Dynamic Impact-Based Heavy Rainfall Warning with Multi-classification Machine Learning Approaches by Anand Shankar

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An improved SMOTE algorithm for enhanced imbalanced data classification by expanding sample generation space by Ying Li, Yali Yang, Peihua Song, Lian Duan, Rui Ren

“…Abstract Class imbalance in datasets often degrades the performance of classification models. Although the Synthetic Minority Over-sampling Technique (SMOTE) and its variants alleviate this issue by generating synthetic samples, they frequently overlook local density and distribution characteristics. …”
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A Novel Deep Learning Model for Motor Imagery Classification in Brain–Computer Interfaces by Wenhui Chen, Shunwu Xu, Qingqing Hu, Yiran Peng, Hong Zhang, Jian Zhang, Zhaowen Chen

“…However, the intricate time–frequency dynamics and inter-channel redundancy of EEG signals remain key challenges, often limiting the effectiveness of single-scale feature extraction methods. To address this issue, we propose the Dual-Branch Blocked-Integration Self-Attention Network (DB-BISAN), a novel deep learning framework for EEG motor imagery classification. …”
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A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification by Manishi Shakya, Ravindra Patel, Sunil Joshi

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Enhancing agricultural research with an Attention-Based Hybrid Model for precise classification of rice varieties by Nuzhat Noor Islam Prova

“…As a staple food feeding over half of the world’s population, rice needs well defined classification techniques to improve agricultural yields, the supply chain, and food safety. …”
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Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey by Shanshan Zhu, Changchun Xiong, Qingshan Zhong, Yudong Yao

“…CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. …”
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Margin weighted robust discriminant score for feature selection in imbalanced gene expression classification. by Sheema Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan

“…High-dimensional gene expression data poses significant challenges for binary classification, particularly in the context of feature selection methods. …”
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