Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance by HUANG Zhenzhen, SUN Jinlin, DING Shihong

“…Building on this, the cost function is integrated with lateral offset and heading offset information, combined with the obstacle avoidance penalty function, and an anti-disturbance model predictive control scheme is constructed by solving the nonlinear constrained optimization problem online. …”
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Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants by Jing Li, Meng-yao Zhou, Yang Li, Xue Wu, Xin Li, Xiao-li Xie, Li-jing Xiong

“…The optimal algorithm was selected to construct the final prediction model.ResultsIn a retrospective cohort study of 693 children diagnosed with FPIAP, the remission rate associated with maternal dietary avoidance was 47.38%. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…To improve model performance, a Gaussian outlier removal technique was applied to eliminate anomalous data points. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. …”
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Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia by Kriengsak Yothapakdee, Yosawaj Pugtao, Sarawoot Charoenkhun, Tanunchai Boonnuk, Kreangsak Tamee

“…This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. …”
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Adaptive control for vehicle formation movement with multiple sensors by TANG Mingzhu, XIANG Guoliang, SHI Hongwei, GUO Shenghui

“…The adaptive motion algorithm based on predetermined motion trajectories is studied in this paper, addressing the problem of vehicle formation with multiple sensors. …”
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Innovative multi-class segmentation for brain tumor MRI using noise diffusion probability models and enhancing tumor boundary recognition by Zengxin Liu, Caiwen Ma, Wenji She, Meilin Xie

“…Addressing the escalating demand for precise diagnostics, this research focuses on the challenges of multi-class segmentation in MRI. The proposed algorithm integrates diffusion models, capitalizing on their efficacy in capturing microstructural details, emphasizing the intricacies of human anatomy and tissue variations that challenge segmentation algorithms. …”
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Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit by Nadea Putri Nur Fauzi, Siti Khomsah, Aditya Dwi Putra Wicaksono

“…This research aims to improve the accuracy of the Random Forest algorithm classification model by implementing parameter tuning and feature engineering. …”
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Feasibility and case studies on converting small hydropower stations to pumped storage by Yangqing Dan, Qingyue Chen, Daren Li, Wenhuan Bai, Weiming Zhou, Anyu Yang, Jia Yang

“…This study utilizes data from small hydropower stations and advanced software algorithms to preliminarily evaluate the feasibility of converting conventional small hydropower stations in Zhejiang Province into pumped storage hydropower stations, with the province serving as the focal research area. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…Among the algorithms tested, the Multilayer Perceptron (MLP) model demonstrated optimal performance with an AUC of 0.823 (95% CI 0.750, 0.874) in the test set, showing consistency between training (AUC = 0.829) and test performance. …”
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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

“…It presented a support vector machines regression model (SVR) with Gauss kernel function (RBF). The best prediction model was obtained by normalization and dimensionality reduction for data and cross-validation for parameter optimization. …”
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RecompGPT: Generative Pre-Trained Transformers-Assisted Interactive Human Gaze Pattern Learning and Distribution Modeling for Scene Recomposition by Wang Shang, Nassiriah Binti Shaari, Nur Sauri Bin Yahaya, Liu Hao

“…Afterward, these GSPs are refined via a multi-layer aggregation algorithm that encodes deep feature representations into a Gaussian Mixture Model (GMM) to model the distribution of human gaze patterns. …”
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Comparing Grid Model Fitting Methodologies for Low-temperature Atmospheres: Markov Chain Monte Carlo versus Random Forest Retrieval by Anna Lueber, Adam J. Burgasser

“…Here, we compare two grid model fitting approaches: a Markov Chain Monte Carlo (MCMC) algorithm interpolating across spectral fluxes, and a random forest retrieval (RFR) algorithm trained on a grid model set. …”
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Protection scheme of flexible MTDC transmission line based on ISSA-BiLSTM by LI Zheng, CHEN Tangxian, ZHANG Yunning, LIU Shuangyang, SUN Peisheng

“…Based on wavelet transform technology, the characteristics of transmission line faults are extracted as model input to train the model; the original sparrow search algorithm is improved by using Sine chaotic mapping, learning particle swarm algorithm strategy, and introducing Gaussian disturbance term. …”
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Soft Computing Techniques to Model the Compressive Strength in Geo-Polymer Concrete: Approaches Based on an Adaptive Neuro-Fuzzy Inference System by Zhiguo Chang, Xuyang Shi, Kaidan Zheng, Yijun Lu, Yunhui Deng, Jiandong Huang

“…Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…The number of EVT attempts has emerged as a key determinant, underscoring the need for optimized procedural timing to improve outcomes.Keywords: machine learning, clinically ineffective reperfusion, predictive model, acute ischemic stroke, online predictive platform…”
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Review and Prospects of Artificial Intelligence Technology in Virtual Power Plants by Xinxing Liu, Ciwei Gao

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A Full-Life-Cycle Modeling Framework for Cropland Abandonment Detection Based on Dense Time Series of Landsat-Derived Vegetation and Soil Fractions by Qiangqiang Sun, Zhijun You, Ping Zhang, Hao Wu, Zhonghai Yu, Lu Wang

“…The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. …”
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Three-dimensional visualization of maize roots based on magnetic resonance imaging by Fang Xiaorong, Wang Nanfei, Zhang Jianfeng, Gong Xiangyang, Liu Fei, He Yong

“…A procedure to obtain root architecture system of maize was developed by computer image graphics technology. The root model was reconstructed with improved volume rendering algorithm in the environment of Visualization Toolkit 5.4. …”
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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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