A multi-faceted review of wind turbine optimization techniques: Metaheuristics and related issues by Hegazy Rezk, Abdul Ghani Olabi, Tabbi Wilberforce, Enas Taha Sayed

“…First, the basics of the wind turbine conversion systems, including the models and classifications, are presented. Then, the main problems related to wind energy are reported. …”
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Plant disease classification in the wild using vision transformers and mixture of experts by Zafar Salman, Abdullah Muhammad, Dongil Han

“…Plant disease classification using deep learning techniques has shown promising results, especially when models are trained on high-quality images. …”
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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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FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer by Wei Wu, Xianbin Hu, Zhu Li, Xueliang Luo

“…To address the issue, we propose a novel RSSC algorithm, dubbed FishermaskFormer, which aggressively decimates features in the convolutional backbone via a novel masking operation with a proposed fisher discriminant analysis criterion, and then designs a lightweight transformer block to drive the classification loss. …”
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Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification by Wenchen Chen, Yanmei Zhang, Jianping Chu, Xingbo Wang

“…Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. …”
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Classification performance improvement in imbalanced circumferential guided wave detection data by Yu-hang Zhang, Xu Zhang, Yuan-hang Gu, Li-min Fu, Xin-ran Su, Jun-dong Yuan, Qiao Wu

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Evaluation of the practical application of the category-imbalanced myeloid cell classification model. by Zhigang Hu, Aoru Ge, Xinzheng Wang, Cuisi Ou, Shen Wang, Junwen Wang

“…Therefore, developing a reliable automated model for myeloid cell classification is imperative. This study evaluated the performance of five widely-used classification models on the largest publicly available bone marrow cell dataset (BM). …”
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Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect by Xinyu Chen, Xiao Luo, Zeyu Xie, Defang Zhao, Zhen Zheng, Xiaodong Sun

“…However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. …”
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A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification by Lilu Wang, Yongqi Li, Haiyuan Liu, Taihui Liu

“…This limitation hinders their deployment on resource-constrained edge devices. To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. …”
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Learning From Natural Images in Few-Shot SAR Target Classification by Songhao Shi, Xiaodan Wang, Yafei Song

“…The intricate imaging attributes of synthetic aperture radar (SAR) present a formidable challenge to the prevailing few-shot target classification. In order to address this issue, we study how to leverage natural images to assist with few-shot SAR learning and propose a model with cross-domain generalization ability, named CDFS-SAR. …”
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Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement by Long Tsang, Ali Ghorbani, Seyed Mohammad Hossein Khatami, Dmitrii Ulrikh

“…Three models of Tree, Adaboost and artificial neural network (ANN) were developed for classification into two categories, stable and unstable. …”
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Information Merging for Improving Automatic Classification of Electrical Impedance Mammography Images by Jazmin Alvarado-Godinez, Hayde Peregrina-Barreto, Delia Irazú Hernández-Farías, Blanca Murillo-Ortiz

“…However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>C</mi><mi>RMS</mi></msub></semantics></math></inline-formula>) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet–PCA fusion. …”
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Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification by Syed Mohammed Shafi, Sathiya Kumar Chinnappan

“…Approximately three million individuals are affected with various types of lung disorders annually. This issue alarms us to take control measures related to early diagnostics, accurate treatment procedures, etc. …”
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Real-time classification of EEG signals using Machine Learning deployment by Swati CHOWDHURI, Satadip SAHA, Samadrita KARMAKAR, Ankur CHANDA

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Mastitis Classification in Dairy Cows Using Weakly Supervised Representation Learning by Soo-Hyun Cho, Mingyung Lee, Wang-Hee Lee, Seongwon Seo, Dae-Hyun Lee

“…Therefore, this study proposed a mastitis classification based on weakly supervised representation learning using an autoencoder on time series milking data, which allows for concurrent milking representation learning and weakly supervision with low-cost labels. …”
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RGB Color Space-Enhanced Training Data Generation for Cucumber Classification by Hotaka Hoshino, Takuya Shindo, Takefumi Hiraguri, Nobuhiko Itoh

“…To address this issue, this study aims to develop a classification system that enables individuals, regardless of their level of expertise, to accurately classify cucumbers. …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…The dataset exhibited a problem of class imbalance, with the majority class (non-mastitis cases) being over-represented. To address this issue, an undersampling algorithm was applied to balance the class distribution by removing a portion of the majority class data. …”
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Phytoplankton group classification by integrating trait information and observed environmental thresholds by Hoang Vuong Dang, Kermode Stephanie, Peisheng Huang, Cayelan C. Carey, Matthew R. Hipsey

“…Third, we applied K-prototype clustering for group classification based on the identified thresholds and associated traits. …”
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Cross-Database Evaluation of Deep Learning Methods for Intrapartum Cardiotocography Classification by Lochana Mendis, Debjyoti Karmakar, Marimuthu Palaniswami, Fiona Brownfoot, Emerson Keenan

“…Efforts to address this issue have focused on data-driven deep-learning methods to detect fetal compromise automatically. …”
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Comparative Analysis of Vision Transformers and CNN Models for Driver Fatigue Classification by Fadhlan Hafizhelmi Kamaru Zaman, Kok Mun Ng, Syahrul Afzal Che Abdullah

“… This study provides a comprehensive evaluation of Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver fatigue classification, a critical issue in road safety. Using a custom driving behavior dataset, state-of-the-art CNN and ViT architectures, including VGG16, EfficientNet, MobileNet, Inception, DenseNet, ResNet, ViT, and Swin Transformer, were analyzed in this study to determine the best model for practical driver fatigue monitoring systems. …”
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