Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. …”
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Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review by Álex Paz, Broderick Crawford, Eric Monfroy, José Barrera-García, Álvaro Peña Fritz, Ricardo Soto, Felipe Cisternas-Caneo, Andrés Yáñez

“…Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling.…”
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Dry-Type Air-Core Series Reactor Turn-to-Turn Short Circuit Fault Detection Method Based on Multi-Parameter Data Fusion by WANG Yin, WANG Yuanyuan, CAO Chengjun, ZHANG Lizhi, YIN Youpeng, JI Hongzhen, DOU Di

“…Subsequently， the denoised features with high saturation are input into the k-nearest neighbors （KNN） algorithm to construct a fault identification and classification model. …”
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Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening by Zhuo Zhao, Xiaoxu Liu, Yonghui Guan, Chunfang Li, Zheng Wang

“…Purpose This study introduces a Pyramid Scene Parsing Network (PSPNet) model to predict PE, aiming to improve early risk assessment using cfRNA profiles. …”
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Transmission Line Anomaly Detection and Real-Time Monitoring System Combining Edge Computing and EfficientDet by Menghao Lin, Yang Ding, Tianle Wang, Yang Liu, Zewei Li

“…In the target recognition part, an improved EfficientDet algorithm is adopted as the main body, and the aspect ratio of anchor boxes in the algorithm is optimized using K-means clustering algorithm. …”
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Data-driven EV charging infrastructure with uncertainty based on a spatial–temporal flow-driven (STFD) models considering batteries by Talal Alharbi, Ahmed Abdalrahman, Mostafa H. Mostafa

“…The ESS placement is modeled as a multi-objective optimization problem, aiming to enhance voltage stability, reduce power losses, and improve voltage profiles. …”
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Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…In conclusion, constraining the region of interest and optimizing model configurations can improve forest cover mapping. …”
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Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy by Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song, Yanlei Xu

“…Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Results: The optimized LightGBM model demonstrated exceptional performance, achieving an overall accuracy of 89.88 %, which surpassed the other compared ML models. …”
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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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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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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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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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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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Advancing Spike Sorting Through Gradient‐Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering by Mohammad Amin Lotfi, Fatemeh Zareayan Jahromy, Mohammad Reza Daliri

“…In the challenging portion of the dataset, our models demonstrated a 12% improvement in accuracy. …”
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