Boost-Classifier-Driven Fault Prediction Across Heterogeneous Open-Source Repositories by Philip König, Sebastian Raubitzek, Alexander Schatten, Dennis Toth, Fabian Obermann, Caroline König, Kevin Mallinger

“…We train and tune a gradient boosting model to classify bug-prone commits under realistic class-imbalance conditions, achieving robust predictive performance across diverse repositories. …”
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DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images by Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou

“…The DBSANet model demonstrated superior performance compared to existing models such as UNet, Deeplabv3+, ResUNet, SwinUNet, TransUNet, TransFuse, and UNetFormer on the Bijie and Luding datasets, achieving IoU values of 77.12% and 75.23%, respectively, with average improvements of 4.91% and 2.96%. …”
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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…We then implement the sliding time window approach to handle population drift, and the optimal training window found demonstrates the existence of population drift in fraud detection and the need to address it for improved model performance. Using the best machine learning model and optimal training window, we build general model and segmented models to compare fraud types and industries based on their respective predictive performance via four evaluation metrics and top features using SHAP. …”
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Video watermarking algorithm for resisting collusion attacks by LIU Shao-hui, HAN Lu, YAO Hong-xun

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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…The default XGB model achieved the optimal balance between accuracy (MSE = 0.9940) and training speed (τ = 0.173 s/it), with performance capabilities that enable real-time enhancement of SOFC thermoelectric characteristics during system operation.…”
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Knowledge Extraction via Machine Learning Guides a Topology‐Based Permeability Prediction Model by Jia Zhang, Gang Ma, Zhibing Yang, Jiangzhou Mei, Daren Zhang, Wei Zhou, Xiaolin Chang

“…While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. …”
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Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments by Yi Wang, Xiang Li

“…To strengthen the model&#x2019;s multi-scale fusion capability for ship features of different scales, we incorporate the bidirectional feature pyramid network (BiFPN) to optimize cross-scale feature fusion and add the <inline-formula> <tex-math notation="LaTeX">$S_{2}$ </tex-math></inline-formula> features to further enhance the representation of small-scale ship features. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…Heatmap analysis revealed activation in the regions of interest for positive cases (true and false positives), but unexpected highlights were encountered possibly due to correlated features in different hand regions.ConclusionA combined deep-learning model detecting CPPD at the TFCC and MCP-2/3 joints in hand radiographs provides the highest diagnostic performance. …”
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Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise by Shantaram B. Nadkarni, G. S. Vijay, Raghavendra C. Kamath

“…This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. …”
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Enhancing Kármán Vortex Street Detection via Auxiliary Networks Incorporating Key Atmospheric Parameters by Yihan Zhang, Zhi Zhang, Qiao Su, Chaoyue Wu, Yuqi Zhang, Daoyi Chen

“…To overcome this limitation, this study presents an innovative auxiliary network framework that integrates essential atmospheric physical parameters to bolster the detection performance of Kármán vortex streets. Utilizing reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5), representative atmospheric features are extracted and subjected to feature permutation importance (PFI) analysis to quantitatively evaluate the influence of each parameter on the detection task. …”
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Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm by Wang Y, Ye S, Xu Z, Chu Y, Zhang J, Yu W

“…Yiwen Wang,1 Shuming Ye,2 Zhi Xu,3 Yonghua Chu,1 Jiarong Zhang,4 Wenke Yu5 1Clinical Medical Engineering Department, The Second Affiliated Hospital, Zhejiang University School of Medicine, HangZhou, ZheJiang, People’s Republic of China; 2Department of Biomedical Engineering, Zhejiang University, HangZhou, ZheJiang, People’s Republic of China; 3China Astronaut Research and Training Center, BeiJing, People’s Republic of China; 4Baidu Inc, BeiJing, People’s Republic of China; 5Radiology Department, ZheJiang Province Qing Chun Hospital, HangZhou, ZheJiang, People’s Republic of ChinaCorrespondence: Yiwen Wang; Shuming Ye, Email karenkaren2010@zju.edu.cn; ysmln@vip.sina.comPurpose: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.Methods: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. …”
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Assessing ensemble models for carbon sequestration and storage estimation in forests using remote sensing data by Mehdi Fasihi, Beatrice Portelli, Luca Cadez, Antonio Tomao, Alex Falcon, Giorgio Alberti, Giuseppe Serra

“…However, adding CHM features significantly improves the models' performance for both targets. …”
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YOLO-GML: An object edge enhancement detection model for UAV aerial images in complex environments. by Zhihao Zheng, Jianguang Zhao, Jingjing Fan

“…YOLO-GML also showed good performance improvement in the SODA-A and VisDrone-2019 datasets, demonstrating the generalization of the model.…”
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Efficient combination of deep learning and tree-based classification models for solar panel dust detection by Jad Bassil, Hassan N. Noura, Ola Salman, Khaled Chahine, Mohsen Guizani

“…These features are then used for classification using lightweight tree-based models. …”
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MFF: A Deep Learning Model for Multi-Modal Image Fusion Based on Multiple Filters by Yuequn Wang, Zhengwei Li, Jianli Wang, Leqiang Yang, Bo Dong, Hanfu Zhang, Jie Liu

“…Compared with traditional image fusion models, the MFF network decomposes high-frequency features more finely. …”
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Recommendation Model Based on Higher-Order Semantics and Node Attention in Heterogeneous Graph Neural Networks by Siyue Li, Tian Jin, Hao Luo, Erfan Wang, Ranting Tao

“…However, most existing GNN models do not fully consider the complex relationships between heterogeneous nodes and ignore the high-order semantic information in the interactions between different types of nodes, which limits the recommendation performance. …”
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Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases by Wanshan Ning, Zhicheng Wang, Ying Gu, Lindan Huang, Shuai Liu, Qun Chen, Yunyun Yang, Guolin Hong

“…And the most models of distinguishing various nervous system diseases also had good performance, the model performance of distinguishing neuromyelitis optica from other nervous system diseases was the best (AUC: 0.9095). …”
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Interpretable multi-label classification model for predicting post-anesthesia care unit complications: a prospective cohort study by Guoting Ma, Wenjun Yan, Zunqiang Zhao, Yanjia Li, Lingkai Wang

“…A multi-label classification model was developed on the basis of 16 key features, and a Markov network was embedded to quantify and analyze the association network among these complications. …”
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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis (DCA). …”
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Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. by Heena Kauser Sk, Maria Anu V

“…The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.The proposed model was trained and tested on the Drebin dataset, which includes 129,013 applications (5,560 malware and 123,453 benign).Performance evaluation against NMLA-AMDCEF, MalVulDroid, and LinRegDroid demonstrated that DBN-GRU achieved 98.7% accuracy, 98.5% precision, 98.9% recall, and an AUC of 0.99, outperforming conventional models.In addition, it exhibits faster preprocessing, feature extraction, and malware classification times, making it suitable for real-time deployment.By bridging static and dynamic detection methodologies, the DBN-GRU enhances malware detection capabilities while reducing false positives and computational overhead.These findings confirm the applicability of the proposed model in real-world Android security applications, offering a scalable and high-performance malware detection solution.…”
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