Enhancing wind turbine blade damage detection with YOLO-Wind by Zhao Zhanfang, Li Tuo

“…Cross-dataset evaluations validate the model’s robustness and adaptability, with 15.2–16.3% mAP@0.5 improvements in blade damage detection and a 3.1% accuracy gain in agricultural defect identification. …”
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A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification by V. Sangeetha, L. Agilandeeswari

“…Subsequently, we integrate autoencoders to incorporate non-linear transformations in the feature extraction phase, thereby improving the model’s capacity to learn intricate patterns within the data. …”
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Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment by Zhiyuan Li, Liwei Wang

“…Clinical evaluation by multidisciplinary specialists confirmed the system’s practical utility, with particularly strong performance in complex cases featuring multiple dentofacial abnormalities. …”
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Face Mask Wearing Detection Using Support Vector Machine (SVM) by Muhammad Nur Yasir Utomo, Fajrin Violita

“…There are three main stages: feature selection and preprocessing, model training, and evaluation. …”
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Machine Learning Helps in Prediction of Tobacco Smoking in Adolescents by Hamidreza Roohafza, Elahe Mousavi, Razieh Omidi, Masoumeh Sadeghi, Mohammadreza Sehhati, Ahmad Vaez

“…We then investigated the performance of different ML approaches for the automatic classification of students into smoker/nonsmoker and low-risk/high-risk categories. …”
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Conventional KPCA Approach Applied to Detect Simulated Faults in PV Systems Using Simulated Data by Charlène Bernadette Lema, Steve Perabi Ngoffe, Francelin Edgar Ndi, Grégoire Abessolo Ondoua, Salomé Ndjakomo Essiane

“…By utilizing statistical metrics in a feature space generated by KPCA, potential faults in PV system performance data are flagged. …”
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UNet–Transformer Hybrid Architecture for Enhanced Underwater Image Processing and Restoration by Jie Ji, Jiaju Man

“…This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. …”
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Comprehensive studies of the snow-firn layer in the area of the Russian Antarctic Vostok Station by Aleksei V. Bolshunov, Sergei A. Ignatev, Gleb D. Gorelik, Nikita S. Krikun, Dmitrii A. Vasilev, Ilya V. Rakitin, Vyacheslav S. Shadrin

“…The findings will allow for future improvements to the firn densification model, which is required to understand the evolution of ice grains during the early stages of metamorphism. …”
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Computer-Assisted Hip Arthroscopic Surgery for Secondary Femoroacetabular Impingement After Rotational Acetabular Osteotomy Using Capsular Takedown Techniques by Shota Higashihira, M.D., Ph.D., Yohei Yukizawa, M.D., Ph.D., Ayahiro Kadowaki, M.D., Shu Takagawa, MD., Ph.D., Hyonmin Choe, M.D., Ph.D., Yutaka Inaba, M.D., Ph.D., Naomi Kobayashi, M.D., Ph.D.

“…Computer modeling produces a patient-specific 3-dimensional CT bone model. …”
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Assessment of Optical Properties and Monte Carlo-Based Simulation of Light Propagation in Blackhearted Potatoes by Yalin Guo, Yakai He, Xilong Li, Zhiming Guo, Mengyao Zhang, Xiaomei Huang, Zhiyou Zhu, Huabin Jian, Zhilong Du, Huangzhen Lv

“…Based on transmittance (<i>T</i>t), <i>μ</i>_a and <i>μ</i>’_s, Support Vector Machine Discriminant Analysis (SVM-DA) models demonstrated exceptional performance, achieving 95.83–100.00% accuracy in Cross-Validation sets, thereby confirming the robustness and reliability of the optical features for accurate blackheart detection. …”
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Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms by Paramasivam Alagumariappan, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj, K. Kamalanand, C. Emmanuel, Sarah Allabun, Manal Othman, Masresha Getahun, Ben Othman Soufiene

“…The proposed MH-XGB classifier is compared with the benchmark models namely Random Forest (RF) classifier and conventional Extreme Gradient Boosting (XGBoost) classifier by using performance metrics. …”
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Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

“…Methods This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…Moreover, the C2f module is restructured into a ResNet–RepConv architecture, in which convolution and Batch Normalization layers are fused during inference to reduce model complexity and enhance inference speed. To further optimize performance, a lightweight shared-convolution detection head significantly reduces parameter count and computational cost without compromising detection accuracy. …”
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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). …”
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Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning by Fransiskus Tatas Dwi Atmaji, Jamasri, Hari Agung Yuniarto, I Made Miasa

“…Statistical time-domain feature extraction notably improved the performance of classical models, with KNN achieving a maximum accuracy of 92.9%. …”
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Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study by Sunyoung Kim, Jaeyu Park, Yejun Son, Hojae Lee, Selin Woo, Myeongcheol Lee, Hayeon Lee, Hyunji Sang, Dong Keon Yon, Sang Youl Rhee

“…However, predictive power models for usability and performance are still being determined. …”
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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

“…After retrieving the feature representation of the CNN, a graph model is created and fed to the GNN. …”
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Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machi... by Yuan Liu, Songyun Zhao, Xingchen Shang, Wei Shen, Wenyi Du, Ning Zhou

“…This study aimed to develop a machine learning model to identify preoperative and intraoperative high-risk factors and predict mortality in patients with AL after radical gastrectomy.MethodsFor this investigation, 906 patients diagnosed with gastric cancer were enrolled and evaluated, with a comprehensive set of 36 feature variables collected. …”
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MicroCrystalNet: An Efficient and Explainable Convolutional Neural Network for Microcrystal Classification Using Scanning Electron Microscope Petrography by Mohammed Yaqoob, Mohammed Yusuf Ansari, Mohammed Ishaq, Issac Sujay Anand John Jayachandran, Mohammed S. Hashim, Thomas Daniel Seers

“…MicroCrystalNet: our proposed classification model employs a convolutional neural network architecture, incorporating advanced feature map processing (feature normalization, dimensionality reduction, and sparse feature selection), integrated within a novel Normalized Sparse Reduction block. …”
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The evolutionary dynamics of organellar pan-genomes in Arabidopsis thaliana by Yi Zou, Weidong Zhu, Yingke Hou, Daniel B. Sloan, Zhiqiang Wu

“…Large-scale investigations into the complex structural rearrangements of mitochondrial genomes remain scarce. Results Here, we perform a comprehensive analysis of the dominant conformations and dynamic heteroplasmic variants of organellar genomes in the model plant Arabidopsis thaliana, utilizing high-quality long-read assemblies validated at high resolution from 149 samples. …”
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