Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…AFIENet achieved an average accuracy of 3.82% and an F1-score of 3.88% improvement across the three datasets when using Transformer as the backbone network. …”
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Machine Learning-Based Weld Classification for Quality Monitoring by Rojan Ghimire, Rajiv Selvam

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Density Classification with Non-Unitary Quantum Cellular Automata by Elisabeth Wagner, Federico Dell’Anna, Ramil Nigmatullin, Gavin K. Brennen

“…The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). …”
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Application of embeddings for multi-class classification with optional extendability by Ф. Смілянець

“…To achieve this, a straight classificational CNN was trained on both datasets using three first classes. …”
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Contrastive learning-driven framework for neuron morphology classification by Yikang Jiang, Hao Tian, Quanbing Zhang

“…Experiments conducted on three public datasets—BIL, JML, and ACT—demonstrate that PRT-net achieves classification accuracies of 78.45%, 67.11%, and 58.95%, respectively, significantly surpassing existing state-of-the-art methods. …”
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Classification of Exoplanetary Light Curves Using Artificial Intelligence by Leticia Flores-Pulido, Liliana Ibeth Barbosa-Santillán, Ma. Teresa Orozco-Aguilera, Bertha Patricia Guzman-Velázquez

“…In this article, we propose a robust star classification methodology leveraging light curves collected from 15 datasets within the Kepler field in the visible optical spectrum. …”
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Size of samples and homogenizers during classification of damaged soybeans by José R. Quirino, Osvaldo Resende, Natalia N. Fonseca, Daniel E. C. de Oliveira, Fatima C. Parizzi, Tiago A. de Souza

“…A 3 x 4 x 5 factorial design was used, meaning three treatments relative to homogenizers (Boerner, 16:1 multichannel splitter, and 4:1 multichannel splitter), four dilutions (4, 8, 12 and 16% damaged grains), and five grain sample sizes (0.025, 0.050, 0.075, 0.100 and 0.125 kg) with nine repetitions. …”
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Comparative Study of Deep Learning-Based Sentiment Classification by Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

“…Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.…”
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CCTV image‐based classification of blocked trash screens by Rory Cornelius Smith, Andrew Paul Barnes, Jingjing Wang, Simon Dooley, Christopher Rowlatt, Thomas Rodding Kjeldsen

“…The performance of a logistic regression for classification of images was investigated using three different subsets of the labelled images: (1) the original dataset, (2) a balanced but under‐sampled dataset with equal number of blocked and unblocked images, and (3) an augmented dataset with an equal number of blocked and unblocked images using Gaussian noise augmentation to increase the number of unblocked images. …”
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Multispectral Semantic Segmentation for Land Cover Classification: An Overview by Leo Thomas Ramos, Angel D. Sappa

“…Land cover classification (LCC) is a process used to categorize the earth's surface into distinct land types. …”
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Convolutional kernel-based classification of industrial alarm floods by Gianluca Manca, Alexander Fay

“…Addressing these significant limitations, this paper introduces a novel three-tier AFC method that uses alarm time series as input. …”
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ICRSSD: Identification and Classification for Railway Structured Sensitive Data by Yage Jin, Hongming Chen, Rui Ma, Yanhua Wu, Qingxin Li

“…However, existing methods for identifying and classifying often suffer from limitations such as overly coarse identification granularity and insufficient flexibility in classification. To address these issues, we propose ICRSSD, a two-stage method for identification and classification in terms of the railway domain. …”
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Research on Emotion Classification Based on Multi-modal Fusion by zhihua Xiang, Nor Haizan Mohamed Radzi, Haslina Hashim

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Multi-criteria classification of spare parts in the steel industry by Nuno Miguel Matos Torre, Valério Antonio Pamplona Salomon, Anna Katarzyna Florek-Paszkowska

“…A hierarchical structure of criteria and sub-criteria, along with alternatives (spare parts), was constructed based on an extensive literature review and validated through input from three maintenance and inventory management experts. …”
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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

“…Results We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE) for multi-omics dimensionality reduction and a densely connected Graph Convolutional Network (GCN) for effective subtype classification. …”
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An Integrated Learning Approach for Municipal Solid Waste Classification by Hieu M. Sondao, Tuan M. Le, Hung V. Pham, Minh T. Vu, Son Vu Truong Dao

“…Initially, four deep learning models—DenseNet161, ResNet152, and MobileNetV3 variants—are explored to determine the most suitable feature extraction method. …”
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Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations by Francesco Di Nardo, Rocco De Marco, Daniel Li Veli, Laura Screpanti, Benedetta Castagna, Alessandro Lucchetti, David Scaradozzi

“…The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. …”
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Meniscal extrusion: Proposal for a novel qualitative classification by Simone Perelli, Pietro Conte, Nicola Pizza, Rodolfo Morales‐Avalos, Elizaveta Kon, Alberto Grassi, Stefano Zaffagnini, Joan Carles Monllau

“…For this reason, a novel qualitative classification for ME is proposed, differentiating between three distinct conditions: a paraphysiological ME, a pathological ME and ME related to degenerative conditions. …”
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Semantic Tokenization-Based Mamba for Hyperspectral Image Classification by Ri Ming, Na Chen, Jiangtao Peng, Weiwei Sun, Zhijing Ye

“…Finally, the fused semantic token is passed into a classifier for classification. Experimental results on three HSI datasets demonstrate that the proposed STMamba outperforms existing state-of-the-art deep learning and transformer-based methods.…”
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Hyperspectral Image Classification Based on Fractional Fourier Transform by Jing Liu, Lina Lian, Yuanyuan Li, Yi Liu

“…The experimental results of three real HRSIs show that the presented mixed feature SF²MF can effectively improve classification accuracy.…”
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