A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”
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Basic principals of the tiltrotors flight control system architecture and algorithms by M. I. Myasnikov, I. R. Ilyin

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Multi-Objective Optimization of Speed Profile for Railway Catenary Maintenance Vehicle Operations Based on Improved Non-Dominated Sorting Genetic Algorithm III by Bingli Zhang, Gan Shen, Yixin Wang, Yangyang Zhang, Chengbiao Zhang, Xinyu Wang, Zhongzheng Liu, Xiang Luo

“…Subsequently, the enhanced selection strategy of the Non-Dominated Sorting Genetic Algorithm III (ESS-NSGA-III) algorithm is proposed to refine the mating and environmental selection processes. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

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Monitoring and Assessment of Slope Hazards Susceptibility Around Sarez Lake in the Pamir by Integrating Small Baseline Subset InSAR with an Improved SVM Algorithm by Yang Yu, Changming Zhu, Majid Gulayozov, Junli Li, Bingqian Chen, Qian Shen, Hao Zhou, Wen Xiao, Jafar Niyazov, Aminjon Gulakhmadov

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A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

“…This study introduces BaySurcls, a Bayesianoptimised surrogate model integrating deep learning (DL) algorithms to improve the PurpleAir sensor PM2.5 (PAS2.5) measurement accuracy. …”
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An improved observer design approach for autonomous vehicles using error-based ultra-local model by Daniel Fenyes, Tamas Hegedus, Balazs Nemeth, Peter Gaspar

“…The design process combines two approaches: the Linear Parameter Varying framework and the error-based ultra-local model. The main goal of the error-based ultra-local model is to deal with the uncertainties and the nonlinearities of the model, whose effects cannot be taken into account during the modeling process. …”
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Some Improvements on Good Lattice Point Sets by Yu-Xuan Lin, Tian-Yu Yan, Kai-Tai Fang

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Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.Keywords: machine learning, preterm birth, early-onset preeclampsia, clinical prediction model…”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…In order to address the issues of low accuracy and poor interpretability in existing HFMD incidence prediction models, in this paper, we propose an interpretable prediction model, namely, ARIMA–LSTM–XGBoost, which integrates multiple meteorological factors with Autoregressive integrated moving average model (ARIMA), Long short-term memory (LSTM), Extreme gradient boosting (XGBoost), Grey wolf optimizer (GWO), Genetic algorithm (GA) and Shapley additive explanations (SHAP). …”
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Algorithm of passive “Beidou”/INS closed-loop integrated using two level filter by GAO Fa-qin, TAN Zhan-zhong

“…One scheme of integrated navigation Kalman filter positioning algorithm was put forward to apply to “Beidou” and INS（intertial navigation system）.Using the pseudo-range’s velocity of changing as observations,on the basis of the high stable clock,a closed loop Kalman filtering model that can revise the attitude error of INS was put out,its biggest optimism was that it could change between the close-loop method and open-loop method steadily.Finally,by computer simulation,it explain that our scheme improve the positioning precision effectively when the satellites in sight are less then two ones,and it also show that our scheme can revise the attitude error of INS effectively and estimate the user’s velocity in high precision.…”
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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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A model for shale gas well production prediction based on improved artificial neural network by LIN Hun, SUN Xinyi, SONG Xixiang, MENG Chun, XIONG Wenxin, HUANG Junhe, LIU Hongbo, LIU Cheng

“…Moreover, the model exhibits superior prediction accuracy and stability compared to the traditional BP（error backpropagation algorithm） neural network model. …”
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Identification method of canned food for production line sorting robot based on improved PSO-SVM by GAO Haiyan, GAO Jinyang, WANG Weicheng

“…By improving the particle swarm optimization algorithm to optimize support vector machine parameters, an optimized support vector machine classification model was obtained. …”
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An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network by Qingfeng Li, Bo Li, Linzhi Wen

“…In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. …”
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Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet by Zhenyu Zhao, Shucheng Tan, Qinghua Zhang, Hui Chen

“…Furthermore, a new hybrid loss function, adaptive focal and Dice loss (AFD loss), is introduced through the adaptive AdaLoss algorithm by combining focal loss and Dice loss, improving the model’s ability to handle unbalanced samples. …”
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A lightweight weed detection model for cotton fields based on an improved YOLOv8n by Jun Wang, Zhengyuan Qi, Yanlong Wang, Yanyang Liu

“…This study proposes the YOLO-Weed Nano algorithm based on the improved YOLOv8n model. First, the Depthwise Separable Convolution (DSC) structure is used to improve the HGNetV2 network, creating the DS_HGNetV2 network to replace the backbone of the YOLOv8n model. …”
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Improving crop rotation classification using a random forest model incorporating spatial heterogeneity by Xiaomi Wang, Qi Tang, Kang Yang

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Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 by Xianghe Meng, Fei Yuan, Dexiang Zhang

“…Abstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. …”
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