Adjustment of angle error and tolerance allocation methods for RV reducers by ZHANG Bowen, ZHOU Jianxing, CUI Quanwei, LIN Kaihong, ZHOU Yadong, XU Wenqiang

“…Finally, with the minimum total processing cost as the objective function, the angular tolerance allocation of key components was completed by using the genetic algorithm.ResultsThe research results prove the effectiveness of this method in improving the transmission accuracy and stability of RV reducers, but different accuracy weight values should be selected according to actual accuracy requirements to minimize costs.…”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…Additionally, two optimization algorithms, Genetic Programming (GP) and Improved Quantum Particle Swarm Algorithm (IQPSO), were used to enhance model performance and establish a non-linear mapping relationship between the influencing factors and the strength of the gently inclined pillars. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…An attention mechanism is added to focus on the most important features,improving the prediction accuracy of the model. Finally,the improved dung beetle optimization (IDBO) algorithm is used to optimize the hyper-parameters of the model. …”
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A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…The system is designed to integrate advanced AI techniques, providing an innovative approach to pose recognition that leverages several sophisticated machine learning models and algorithms to enhance performance. The pre-processing stage involves applying a Wiener Filter (WF) for effective noise removal, ensuring that the data is clean and ready for analysis. …”
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A Dynamic Precision Evaluation System for Physical Education Classroom Teaching Behaviors Based on the CogVLM2-Video Model by Chao Liu, Fan Yang, Chengyu Ge, Zhiyu Shao

“…The platform layer manages data processing and storage, ensuring integrity and security for long-term evaluation. The model layer focuses on behavior recognition and analysis, employing advanced algorithms for precise interpretation of teaching behaviors. …”
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Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions by Y. Bo, H. Liang, T. Li, F. Zhou, F. Zhou, F. Zhou

“…Here, we propose an advancing framework that addresses these problems by integrating a process-based soil–crop model with vital physiological effects, a novel method for model upscaling, and the non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm at a parallel computing platform. …”
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Automating the Design of Scalable and Efficient IoT Architectures Using Generative Adversarial Networks and Model-Based Engineering for Industry 4.0 by William Villegas-Ch, Jaime Govea, Diego Buenano-Fernandez, Aracely Mera-Navarrete

“…Traditional approaches, such as heuristic and genetic algorithms, have proven insufficient in automating and optimizing large-scale IoT configurations, resulting in a high design and validation time cost. …”
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Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection by Pramodh Krishna D., E. Sandhya, Khaja Shareef Sk, Srihari Varma Mantena, Venkata Subbaiah Desanamukula, Ch Koteswararao, Srinivasa Rao Vemula, Maruthi Vemula

“…This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs. To optimize the model’s efficiency, a unique DECEHGS algorithm combining Differential Evolution and Evolutionary Population Dynamics techniques is employed, enhancing both convergence and performance. …”
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Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials by Anastasios Nikolopoulos, Vangelis D. Karalis

“…This study highlights the potential of WGANs to improve data augmentation and optimize subject recruitment in BE studies.…”
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Three-Dimensional Imaging of High-Contrast Subsurface Anomalies: Composite Model-Constrained Dual-Parameter Full-Waveform Inversion for GPR by Siyuan Ding, Deshan Feng, Xun Wang, Tianxiao Yu, Shuo Liu, Mengchen Yang

“…However, full-waveform inversion (FWI) for GPR data struggles to simultaneously reconstruct high-resolution 3D images of both permittivity and conductivity models. Considering the magnitude and sensitivity disparities of the model parameters in the inversion of GPR data, this study proposes a 3D dual-parameter FWI algorithm for GPR with a composite model constraint strategy. …”
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Force-Driven Model for Automated Clear Aligner Staging Design Based on Stepwise Tooth Displacement and Rotation in 3D Space by Sensen Yang, Yumin Cheng

“…This innovative method substantially improved design efficiency and accuracy, ultimately elevating the efficacy of clear aligner therapy, although further biomechanical analyses and experimental validations are needed to refine the model parameters.…”
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Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data by Kadierye Maolan, Yusufujiang Rusuli, Zhang XuHui, Yimuran Kuluwan

“…The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. …”
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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

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Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones by Shuo Wei, Houming Fan, Xiaoxue Ren, Xiaolong Diao

“…Compared to the genetic neighborhood search algorithm and the hybrid genetic algorithm, the improvement rates are 5.1% and 13.0%, respectively. …”
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Numerical modeling of electromagnetic wave propagation in spatially-varying evaporation duct conditions via 3D parabolic equation method by Hanjie Ji, Hanjie Ji, Lixin Guo, Yan Zhang, Tianhang Nie, Yiwen Wei, Jinpeng Zhang, Qingliang Li, Xiangming Guo, Yusheng Zhang

“…Quantitative analysis reveals that the 3DPE framework delivers over 40% performance improvement compared to the 2D model. This approach significantly enhances predictive accuracy for over-the-horizon radar assessments in maritime environments, providing crucial support for optimizing next-generation communication systems.…”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. …”
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Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies by Fatih Gurcan

“…The implemented resampling methods were designed to improve class balance and enhance model training. …”
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Applying a Four-Way Factorial Experimental Model to Diagnose Optimum kNN Parameters for Precise Aboveground Biomass Mapping by Chinsu Lin, Nova D. Doyog

“…This protocol effectively identifies suitable kNN-AGB models, enhancing the ability to delineate areas with low biomass productivity for precision management, while also supporting forest improvement initiatives and biomass-related studies.…”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

“…Meanwhile, existing automated detection algorithms often struggle with a trade-off between detection accuracy and model size. …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely intervention, optimization of treatment strategies, and improvement of clinical outcomes. …”
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