Multi-Scale Spatiotemporal Feature Enhancement and Recursive Motion Compensation for Satellite Video Geographic Registration by Yu Geng, Jingguo Lv, Shuwei Huang, Boyu Wang

“…Based on the SuperGlue matching algorithm, the method achieves automatic matching of inter-frame image points by introducing the multi-scale dilated attention (MSDA) to enhance the feature extraction and adopting a joint multi-frame optimization strategy (MFMO), designing a recursive motion compensation model (RMCM) to eliminate the cumulative effect of the orbit error and improve the accuracy of the inter-frame image point matching, and using a rational function model to establish the geometrical mapping between the video and the ground points to realize the georeferencing of satellite video. …”
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Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang, Zhiqian Qi

“…This model uses a multi-scale one-dimensional residual neural network (M1D_ResNet) for feature extraction and a genetic algorithm (GA)-optimized support vector data description (SVDD). …”
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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. …”
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Enhancing Wind Turbine Efficiency: An Experimental Investigation of a Sensorless Three-Vector Finite Set Predictive Torque Control Approach for PMSG-Based Systems by Marouane Ahmed Ghodbane, Toufik Mohamed Benchouia, Mohamed Chebaani, Mohamed Becherif, Yassine Himeur, Amar Golea, Abdelmoumen Ghilani, Zakaria Alili, Shadi Atalla, Wathiq Mansoor

“…This approach does not require an anemometer, mechanical parameters, or rotor position sensors, making the system simpler, more reliable, and cost-effective. The 3V FS-PTC algorithm enhances control performance by selecting the three most optimal voltage vectors, two active voltage vectors and one zero voltage vector. …”
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Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control by Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun

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Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach by Gil Cohen, Avishay Aiche, Ron Eichel

“…This study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over the 2020–2025 period. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques by Ding Zhou, Guohua Wei, Xiaojun Yuan

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Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches by Burcu Ramazanli, Oyku Yagmur, Efe Cesur Sarioglu, Huseyin Enes Salman

“…Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. …”
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PolSAR Forest Height Estimation Enhancement With Polarimetric Rotation Domain Features and Multivariate Sensitivity Analysis by Fu-Gen Jiang, Ming-Dian Li, Si-Wei Chen

“…Then, we propose a Bayesian-optimized ensemble learning algorithm to improve the accuracy of forest height estimation. …”
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Machine Learning in the National Economy by Azamjon A. Usmonov

“…Methods of cleaning, normalization, and data transformation were used for data processing to improve model accuracy. The practical part of the study included the development of machine learning algorithms for predicting economic indexes. …”
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Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…The attention mechanism is introduced into the evaluation network to capture interdependencies among agents, enabling potential intent recognition and cooperative behavior perception, thereby improving algorithm convergence. Additionally, noise is added to expand the exploration space, enhancing training stability.Results and Discussions　Using a modified IEEE transmission-distribution joint system as an example, the modeling and solution demonstrate that the multi-agent attention mechanism can strengthen the focus among collaborators, thereby balancing the interests of both parties. …”
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Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines by Pengxiang Li, Aiying Gao, Mingzhou Bai, Shumao Qiu

“…The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. …”
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Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM by Lei ZHANG

“…According to the working principle and characteristics of the train power supply system, the relationship between the fault phenomenon and the origin was analyzed, and the characteristic signals used for fault diagnosis were extracted. A fault diagnosis model based on PSO optimized least squares support vector machine was established, and PCA algorithm was used to extract data characteristics as input of fault diagnosis model, and reduce input dimension. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Twenty-two clinical features available within the first 24 h of admission were selected to develop the prediction models. Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. …”
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Research on Key Technologies of Virtual Coupling Control System for Autonomous-rail Rapid Tram by HUANG Qiang, YUAN Xiwen, HU Yunqing, LI Cheng, HUANG Ruipeng, TANG Xiang

“…A safe braking model of autonomous-rail rapid tram is initiatively used to derive the minimum space headway for operation safety between coupled formations, and the collaborative planning and MPC-based collaborative control algorithms utilizing an optimal control approach are applied to guarantee the safe, punctual, comfortable, and efficient operation of coupled formations. …”
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A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China by Xiaofei Shang, Huawei Zhao, Shengxiang Long, Taizhong Duan

“…The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. …”
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Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China by Quanshan Liu, Zongjun Wu, Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Zhihui Wang, Daozhi Gong, Yaosheng Wang, Lu Zhao, Renjuan Wei

“…The BKA optimization algorithm effectively enhanced the predictive performance of the KELM and ELM models. …”
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Research on the Rapid Detection of Formaldehyde Emission From Wood-Based Panels Based on the AMSHKELM by Yinuo Wang, Huanqi Zheng, Hua Wang, Yucheng Zhou

“…The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). …”
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Privacy protection risk identification mechanism based on automated feature combination by CAI Minchao, YAO Hongwei, WANG Yang, QIN Zhan, CHEN Shaomeng, REN Kui

“…In practice, the anomaly detection (AD) algorithm usually faced technical challenges such as difficulty in optimizing feature combinations, difficulty in improving classifier accuracy, and low model application efficiency. …”
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