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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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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Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique by Lijie Jiang, Qi Li, Huiqing Liao, Hourong Liu, Bowen Tan

“…Abstract This study develops and evaluates advanced hybrid machine learning models—ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Regression), and ADA-GPR (AdaBoost on Gaussian Process Regression)—optimized via the Black Widow Optimization Algorithm (BWOA) to predict the density of supercritical carbon dioxide (SC-CO2) and the solubility of niflumic acid, critical for pharmaceutical processes. …”
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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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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Adaptive Temporal Reinforcement Learning for Mapping Complex Maritime Environmental State Spaces in Autonomous Ship Navigation by Ruolan Zhang, Xinyu Qin, Mingyang Pan, Shaoxi Li, Helong Shen

“…The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. …”
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The Forecasting Yield of Highland Barley and Wheat by Combining a Crop Model with Different Weather Fusion Methods in the Study of the Northeastern Tibetan Plateau by Peng Li, Liang He, Xuetong Wang, Mengfan Zhao, Fan Li, Ning Jin, Ning Yao, Chao Chen, Qi Tian, Bin Chen, Gang Zhao, Qiang Yu

“…For HB, sequential selection and an improved KNN algorithm were optimal, while for wheat, sequential selection performed best. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…This model serves as an effective auxiliary tool for clinical decision-making and has the potential to optimize treatment strategies and improve prognostic assessment for pN0-pN2 patients. …”
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Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter by Amira Wali Omer, Taha Hussein Ali

“…These outliers may occur in the dependent variable or both independent and dependent variables, resulting in large residual values that compromise model reliability. Addressing outliers is essential for improving the accuracy and robustness of regression models.  …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Research on Dynamic Performance of Autonomous-rail Rapid Tram by ZHONG Hanwen, LI Xiaoguang, XIAO Lei, YANG Yong, ZHANG Chenlin, HUANG Ruipeng, YUAN Xiwen

“…Through detailed Simpack dynamic model, the simulation research was carried out to provide guidance for optimization and improvement of vehicle dynamic performance. …”
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Enhancing Aerosol Vertical Distribution Retrieval With Combined LSTM and Transformer Model From OCO-2 O2 A-Band Observations by YuXuan Wang, RuFang Ti, ZhenHai Liu, Xiao Liu, HaiXiao Yu, YiChen Wei, YiZhe Fan, YuYao Wang, HongLian Huang, XiaoBing Sun

“…Furthermore, a physics-based, information-driven band selection method was developed to simplify input data and reduce complexity. To enhance the algorithm's applicability, the model was applied across the entire African continent and adjacent water bodies. …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…In this paper, a debris flow susceptibility assessment model is constructed based on RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models with Stacking ensmble learning method, and SPY technique is introduced to optimize the negative sample selection. …”
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Multi-Objective Vibration Control of a Vehicle-Track-Bridge Coupled System Using Tuned Inerter Dampers Based on the FE-SEA Hybrid Method by Xingxing Hu, Qingsong Feng, Min Yang, Jian Liu

“…Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID’s optimal parameters are determined using a multi-objective genetic algorithm. …”
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A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

“…Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). …”
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The development of an intelligent comprehensive detection instrument for circuit breakers in power systems and its key technologies by Weimin Guan, Han Hu, Chao Sun, Jie Ji

“…Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. …”
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A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study by Rui-Tao Sun, Chang-Lei Li, Yu-Min Jiang, Ao-Yun Hao, Kui Liu, Kun Li, Bin Tan, Xiao-Nan Yang, Jiu-Fa Cui, Wen-Ye Bai, Wei-Yu Hu, Jing-Yu Cao, Chao Qu

“…A combination of radiomic and clinical features was selected using the Boruta-LASSO algorithm. Predictive models were constructed using six machine learning algorithms and validated, with model performance evaluated based on the AUC, accuracy, Brier score, and DCA to identify the optimal model. …”
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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…These algorithms were chosen for their strong global search capabilities and adaptability to complex datasets, ensuring optimal parameter selection for improved model performance. …”
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