Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. …”
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A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

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A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning by Huihui XU, Yunfei TIAN, Yuyang ZHAO, Jing PENG, Qingxin SHI, Rui CHENG

“…Secondly, a multi-agent reinforcement learning (MARL) model is established, and a training algorithm integrating imitation learning and exploratory learning is proposed, enabling the agents to acquire optimal policies through interactive learning with an expert experience model. …”
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Development of PYMUS+ Code for Quantitative Evaluation of Nuclear Material Accounting (NMA) System by Byung Hee Won, Hee-Sung Shin, Se Hwan Park, Seong-Kyu Ahn

“…It is also important to quantitatively evaluate the performance of NMA system including NRTA from the standpoints of Safeguards and Security by Design (SSBD) prior to construction of nuclear-material-handling facilities. Such evaluation improves safeguards effectiveness and efficiency. Modeling and Simulation (M&S) work is a good way to evaluate performance for various NMA systems and to determine the optimal one among different options. …”
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KT-Deblur: Kolmogorov–Arnold and Transformer Networks for Remote Sensing Image Deblurring by Baoyu Zhu, Zekun Li, Qunbo Lv, Zheng Tan, Kai Zhang

“…Supported by the Fast Spatial Feature Module (FSFM), it effectively improves the model’s ability to handle complex blur patterns. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. …”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…We also propose an efficient alternating iterative update algorithm to optimize the proposed model and provide a theoretical analysis of its performance. …”
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Pathophysiology of the Heart and Clinical Cardiac Anesthesiology by Yu. V. Nikiforov, L. A. Krichevsky

“…The main tasks presented to its investigators were to improve electric cardiac defibrillation and cardioversion, to elaborate the optimal infusiotransfusion therapy tactics during cardiac surgery, to optimize intraoperative myocardial protection, to maintain lung functions under extracorporeal circulation, to search for the most effective and safe schemes of general and epidural anesthesia in cardiosurgical patients, and to substantiate algorithms for their postoperative activation. …”
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Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression by Javad Artin, Amin Valizadeh, Mohsen Ahmadi, Sathish A. P. Kumar, Abbas Sharifi

“…This study presented a method based on ensemble learning to predict urban traffic congestion based on weather criteria. We used the NAS algorithm, which in the output based on heuristic methods creates an optimal model concerning input data. …”
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UAV-Based SAR-Imaging of Objects From Arbitrary Trajectories Using Weighted Backprojection by Alexander Grathwohl, Julian Kanz, Christina Bonfert, Christian Waldschmidt

“…Based on this model, the expected signal-to-clutter ratio (SCR) of any point target in a single measurement can be predicted. …”
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MSDP-Net: A YOLOv5-Based Safflower Corolla Object Detection and Spatial Positioning Network by Hui Guo, Haiyang Chen, Tianlun Wu

“…For object detection, we introduce an improved YOLO v5m model, referred to as C-YOLO v5m, which integrates a Convolutional Block Attention Module (CBAM) into both the backbone and neck networks. …”
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Risk Assessment of Constructing Deep Foundation Pits for Metro Stations Based on Fuzzy Evidence Reasoning and Two-tuple Linguistic Analytic Network Process by Jie Jiang, Guangyang Liu, Xi Huang, Xiaoduo Ou

“…Finally, the overall risk grade of the construction project is evaluated by aggregating the risk levels of all risk events through an evidence-reasoning algorithm. The analysis results for a deep foundation pit for a station on Line 5 of Nanning Metro show that the model provides a quantitative basis for determining expert weights and risk loss weights reasonably and improving the reliability of the evaluation system. …”
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A Bilevel Dynamic Pricing Methodology for Electric Vehicle Charging Stations Considering the Drivers’ Charging Willingness by Xin Fang, Bei Bei Wang, Su Yang Zhou, C. C. Chan

“…Meanwhile, the lower-level model represents CS price adjustments as a noncooperative game, solved via a greedy algorithm. …”
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Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement by Chang Li, Quan Zou, Guoqing Li, Wenyang Yu

“…In addition, this study introduces a sliding window algorithm based on Gaussian fusion as a post-processing method, which optimizes the prediction of landslide edge in high-resolution remote sensing images and ensures the context reasoning ability of the model. …”
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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…This multi-module collaborative architecture effectively processes multi-scale features of time series data while providing model interpretability. Through comparative analysis of various optimization algorithms’ convergence performance and prediction accuracy, this study found that the AdamW optimizer, with its effective weight decay mechanism and adaptive learning rate, demonstrated superior performance in training stability and generalization capability, with MSE and R2 metrics outperforming traditional optimizers. …”
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Trajectory Planning for Robotic Manipulators in Automated Palletizing: A Comprehensive Review by Samuel Romero, Jorge Valero, Andrea Valentina García, Carlos F. Rodríguez, Ana Maria Montes, Cesar Marín, Ruben Bolaños, David Álvarez-Martínez

“…By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications.…”
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A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards by Yaning Zhai, Ling Zhang, Xin Hu, Fanghu Yang, Yang Huang

“…By optimizing the network structure, the improved YOLO detection model provides high-quality detection results for subsequent tracking tasks. …”
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Exploration and Construction of Personalized Customization Mode in Modern Fashion Design by Sun Huaiqun

“…By analyzing the advantages of this model in meeting personalized consumer needs, improving production efficiency, and promoting green design and sustainable development, this article further proposes specific suggestions for technology optimization, process simplification, and user experience improvement. …”
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Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang, Robert Kallenbach

“…We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. …”
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