Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering by Ibrahim Mutambik

“…The findings highlight the value of incorporating both spatial and temporal data to enhance emergency response strategies and optimize urban planning efforts. This study contributes to the literature on smart cities by providing a scalable and adaptable method for improving urban resilience in the face of evolving challenges.…”
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Adaptive Feature Selection of Unbalanced Data for Skiing Teaching by Tao Feng

“…Finally, the experimental results on the public imbalanced dataset show that the proposed algorithm effectively improves the classification performance of imbalanced data.…”
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A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding by Zhen Wang, Yongjie Wang, Xinli Xiong, Qiankun Ren, Jun Huang

“…Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this paper models important elements of the network topology for decision-making, which guide the defender to dynamically optimize its strategy based on topology awareness. …”
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Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China by Jieyun Xiao, Wei Zhou, Ting Wang, Yao Peng, Zhan Shi, Saibo Li, Yang Li, Tianxiang Yue

“…Using the RF feature selection (FS) method to select optimal factors as model input variables improved simulation accuracy compared with using all factors or selecting based on Pearson correlation analysis (R² increased by 1.75%–64.71%). …”
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Anti-packet-loss joint encoding for voice-over-IP steganography by Zhan-zhan GAO, Guang-ming TANG, Wei-wei ZHANG

“…Furthermore, the influences of key parameters on the performance of joint coding were studied. The selection algorithm for optimal parameters was also given. Experimental results show that the proposed joint coding can effectively improve steganographic resistance to packet loss, and decrease the number of modifications to the voice stream.…”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…XGBoost emerged as the top performing algorithm, achieving an AUC of 0.94 and an accuracy of 0.875 on the test set, marking substantial improvements over previously reported best results. …”
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Features of single treasury account management in the context of budget funds liquidity management by N. S. Sergienko

“…Successful examples of integration of automated financial flow management systems, use of machine learning algorithms for forecasting and optimization of balances on single treasury account, as well as the interdepartmental interaction mechanisms to improve transparency and efficiency of budget management have been considered. …”
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Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols by Na Kong, Yu Xia, Zhilong Wang, Hui Zhang, Liyan Duan, Yingchun Zhu, Chenyang Huang, Guijun Yan, Jie Mei, Wujun Li, Haixiang Sun

“…Most existing models are limited to static predictions of initial doses and do not support real-time adjustments throughout stimulation. …”
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Dynamic Inversion Analysis of Structural Layer Modulus of Semirigid Base Pavement considering the Influence of Temperature and Humidity by Bei Zhang, Xu Zhang, Yanhui Zhong, Xiaolong Li, Meimei Hao, Jinbo Liu

“…The firefly algorithm was adapted to optimize the selection of support vector machine (SVM) parameters. …”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

“…This design prevents the PS from performing model inversion, as the individual model updates are obfuscated within the transmitted combination. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. …”
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A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim, Jae-Jun Kim

“…In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches.…”
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UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation. by Yuanhong Dan, Jinyan Li, Yu Jin, Yong Ji, Zhihao Wang, Dong Cheng

“…In this study, in order to refine the feature representation and reduce the computational effort to improve the efficiency of the tracker, we perform feature fusion in deep inter-correlation operations and introduce a global attention mechanism to enhance the model's field of view range and feature refinement capability to improve the tracking performance for small targets. …”
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High Precision Navigation and Positioning for Multisource Sensors Based on Bibliometric and Contextual Analysis by Jiayi Wei, Min Song, Yunbin Yuan

“…The study concludes by summarizing existing research findings and identifying gaps, with future research focusing on optimizing multisensor fusion algorithms, enhancing system adaptability, improving error models, and enhancing sensor performance in adverse environmental conditions.…”
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Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications by Dasheng Wu, Na Liu, Rui Ma, Peilong Wu

“…Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. …”
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BedEye: A Bed Exit and Bedside Fall Warning System Based on Skeleton Recognition Technology for Elderly Patients by Liang-Bi Chen, Wan-Jung Chang, Tzu-Chin Yang

“…The proposed BedEye system innovatively utilizes OpenPose-light, which is a lightweight version of the OpenPose model optimized for edge computing. The proposed BedEye system processes real-time images captured by an RGB sensor, which are then fed into a deep learning model running locally on an Nvidia Jetson Xavier-NX edge computing device. …”
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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…Key clinical and imaging parameters, such as age, TNM stage, CRM involvement, and EMVI presence, were recorded. A radiomic model was developed using the LASSO algorithm for feature selection and regularization from 107 extracted radiomic features. …”
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A localization strategy combined with transfer learning for image annotation. by Zhiqiang Chen, Leelavathi Rajamanickam, Jianfang Cao, Aidi Zhao, Xiaohui Hu

“…Experiments on the Corel5k multilabel image dataset verify that CNN-2L improves the labeling precision by 18% and 15% compared with the traditional multiple-Bernoulli relevance model (MBRM) and joint equal contribution (JEC) algorithms, respectively, and it improves the recall by 6% compared with JEC. …”
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Deep coal fluidization mining ropeless hoisting system and its cooperative driving control strategy by Shuai LIU, Jiusheng BAO, Jinhao DAI, Yan YIN, Liang ZHAO, Zhongbin WANG, Shirong GE

“…The research results show that the fuzzy PI loop coupling control algorithm can optimize the motor response characteristics and improve the following and synchronization performance among multiple motors. …”
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A Ship Underwater Radiated Noise Prediction Method Based on Semi-Supervised Ensemble Learning by Xin Huang, Rongwu Xu, Ruibiao Li

“…However, the labeled data available for the training of URN prediction model is limited. Semi-supervised learning (SSL) can improve the model performance by using unlabeled data in the case of a lack of labeled data. …”
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