Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer by Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan, Ketian Liu

“…Furthermore, it outperforms traditional methods such as LSTM-NDT, Transformer, OmniAnomaly and MAD-GAN in Overall Accuracy, Average Accuracy, and Kappa coefficient, thereby validating the effectiveness and superiority of the proposed anomaly detection and classification method.…”
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Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization by Taichi Tanaka, Isao Nambu, Yasuhiro Wada

“…Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. …”
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Pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification by Jingpeng Gao, Xiangyu Ji, Geng Chen, Yuhang Huang, Fang Ye

“…However, they all ignore the implicit class distribution information of TD data, which can help the model predict the class with a higher posterior probability. To solve the above issue, we propose pseudo-class distribution guided multi-view unsupervised domain adaptation for hyperspectral image classification (PCDM-UDA). …”
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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

“…Imbalanced data remains a challenge in classification research and significantly influences classifier performance. …”
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Structure-guided decoupled contrastive framework for partial multi-view incomplete multi-label classification by Linqian Yang, Tao Zhuang, Xing Jin, Tianyang Cao, Xiyin Wu

“…However, due to the limitations of data collection and the subjectivity of manual labeling, multi-view multi-label learning often faces both partial views and incomplete labels, substantially impacting the performance of existing classification methods in practical applications. Although existing methods have attempted to address this issue, they struggle to fully exploit the consistency and complementarity of multi-view multi-label data simultaneously. …”
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UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification by Yu Shen, Ge Cao, Huan-Yu Dong, Bo Dong, Chang-Myung Lee

“…To address this issue, this study presents two key contributions. …”
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Novel Automatic Classification Method for Geological Structures in Carbonate Formations Based on Electrical Imaging Logging by FU Yafeng, HUANG Ke, ZHU Hanbin, WANG Hui, ZHANG Xu, ZHAO Jie, XIAO Ni

“…In the processing of electrical imaging logging data for carbonate formations, it is challenging to distinguish mudstone laminae, natural fractures, induced fractures, and vugs due to their similar resistivities and overlapping occurrences. To address this issue, this paper proposes an automatic classification method based on skeleton maps and machine learning. …”
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Artificial Intelligence-Based Segmentation and Classification of Plant Images with Missing Parts and Fractal Dimension Estimation by Ganbayar Batchuluun, Seung Gu Kim, Jung Soo Kim, Tahir Mahmood, Kang Ryoung Park

“…However, limited camera viewing angles can cause parts of the plant to be invisible in the acquired images, leading to an inaccurate classification. However, this issue has not been addressed by previous research. …”
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Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification by Yuefeng Zhao, Jingqi Sun, Nannan Hu, Chengmin Zai, Yanwei Han

“…However, existing few-shot methods ignore the correlation between cross-domain feature channels, and the feature representation ability is insufficient. To address above issue, this paper proposes a novel Residual Channel Attention Based Sample Adaptation Few-Shot Learning for Hyperspectral Image Classification(RCASA-FSL) for hyperspectral image classification (HSIC), which can capture and enhance cross-domain dependencies through multi-layer residual connection and random-based feature recalibration. …”
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BO-CLAHE enhancing neonatal chest X-ray image quality for improved lesion classification by Jiwon Han, Byungmin Choi, Jae Young Kim, Yeonjoon Lee

“…Although attempts have been made to introduce deep learning to address these image quality issues, the poor quality of the images themselves hinders the training of deep learning models, further emphasizing the need for image enhancement. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. …”
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Spiking neural network tactile classification method with faster and more accurate membrane potential representation by Jing Yang, Zukun Yu, Xiaoyang Ji, Zhidong Su, Shaobo Li, Yang Cao

“…However, traditional SNN classification methods often encounter under‐convergence when using membrane potential representation, decreasing their classification accuracy. …”
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A Novel Training Strategy for Deep Learning Model Compression Applied to Automatic Modulation Classification by Mateus A. Goldbarg, Micael Balza, Sergio N. Silva, Lucileide M. D. Silva, Marcelo A. C. Fernandes

“…To tackle this issue, a novel training strategy is proposed in this study. …”
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Machine learning prediction of coal workers’ pneumoconiosis classification based on few-shot clinical data by Jiaqi Jia, Jingying Huang, Yuming Cui, Dekun Zhang, Haiquan Li, Songquan Wang, Wenlu Hang

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High-accuracy combustible gas cloud imaging system using YOLO-plume classification network by Jiani Zhou, Yang Liu, Yong Zhang, Haotian Hu, Zenan Leng, Feng Sun, Chen Chen

“…Due to the irregular shape and ambiguous boundary of the gas, traditional motion detection algorithms are difficult to adapt to the changes in the gas movement state with the environment, resulting in an increased probability of false alarms. To address this issue, this paper proposes a gas plume-constrained YOLOv11 model based on infrared imaging detection technology, named YPCN (YOLO-Plume Classification Network). …”
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An attention based hybrid approach using CNN and BiLSTM for improved skin lesion classification by Ayesha Shaik, Shivanya Shomir Dutta, Ishaan Milind Sawant, Shreyas Kumar, Ananthakrishnan Balasundaram, Kanjar De

“…Abstract Skin lesions remain a significant global health issue, with their incidence rising steadily over the past few years. …”
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SSTMNet: Spectral-Spatio-Temporal and Multiscale Deep Network for EEG-Based Motor Imagery Classification by Albandari Alotaibi, Muhammad Hussain, Hatim Aboalsamh

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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. …”
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Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2 by Adán-Antonio Alonso-Ramírez, Alejandro-Israel Barranco-Gutiérrez, Iris-Iddaly Méndez-Gurrola, Marcos Gutiérrez-López, Juan Prado-Olivarez, Francisco-Javier Pérez-Pinal, J. Jesús Villegas-Saucillo, Jorge-Alberto García-Muñoz, Carlos-Hugo García-Capulín

“…Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. …”
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FFTL-Net: a network for the classification of skin diseases based on feature fusion and transfer learning by Xiaowei Song, Yurong Mei, Zhilei Zhao, Hao Chang, Lina Han, Hui Wang, Xinyi Zhang, Guoqiang Wang

“…The objective is to address the issues of data imbalance, overfitting, and inadequate generalization ability in skin disease datasets and recognition models. …”
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