Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…The main objective of this study was to implement and compare two prominent approaches—regression and machine learning—for modeling whole-tree volume and stem volume in two Pinus species in community forests of southern Mexico. …”
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Neuro-VGNB: Transfer Learning-Based Approach for Detecting Brain Stroke by Muhammad Usama Tanveer, Kashif Munir, Bharati Rathore, Abdulatif Alabdulatif, Rutvij H. Jhaveri, Maham Fatima

“…These extracted features were then enhanced and transferred using the Gaussian Naive Bayes (GNB) model, integrated with non-negative matrix factorization to optimize feature representation. …”
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Curing simulation and data-driven curing curve prediction of thermoset composites by Chenchen Wu, Ruming Zhang, Pengyuan Zhao, Liang Li, Dingguo Zhang

“…Then, the temperature–time and the resulting degree-of-cure-time curves obtained from finite element simulations were created for training the prediction models using machine learning approaches of support vector regression (SVR), back propagation (BP) neural network and BP neural network optimized by genetic algorithm (GA-BP). …”
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Programmed cell death signatures-driven microglial transformation in Alzheimer’s disease: single-cell transcriptomics and functional validation by Mi-Mi Li, Ying-Xia Yang, Ya-Li Huang, Shu-Juan Wu, Wan-Li Huang, Li-Chao Ye, Ying-Ying Xu

“…Seventy-seven PCD-related genes were identified, with 70 genes used to construct the PCDS model. The optimal model, combining Stepglm and Random Forest, achieved an average AUC of 0.832 across five cohorts. …”
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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features by Ning Yan, Yasen Qin, Haotian Wang, Qi Wang, Fangyu Hu, Yuwei Wu, Xuedong Zhang, Xu Li

“…In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices and textural features to realize the estimation of the SPAD value of pear leaves, this study used the UAV multispectral remote sensing images and ground measurements to extract the vegetation indices and textural features, and analyze their correlation with the SPAD value of leaves during the fruit expansion period of the pear tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. …”
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Revival of Muslin by Phuti Karpas plant identification with convolution neural network by Redwan Ahmed Rizvee, Omar Farrok, Mahamudul Hasan, Faisal Farhan, Md Hafanul Islam, Md Khalid Hasan, Abidur Rahman, Maheen Islam, Md Sawkat Ali, Taskeed Jabid, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam

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The calculation algorithm of oil and gas production enterprise energy efficiency indicators by A. Yu. Arestova, V. N. Ulyanov, M. Yu. Frolov

“…The equipment parameters identification method based on actual measurements is used for designing a mathematical model. To calculate the steady-state, the nodal-voltage method is used in couple with an addressing matrix for tracing power flows.RESULTS. …”
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IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD by Ni Kadek Emik Sapitri, Umu Sa'adah, Nur Shofianah

“…The optimal CART in the best SIFS-CART model only needs four genes from 1000 selected genes to build it. …”
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Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication by Min Zeng, Mohd Sani Mohamad Hashim, Mohd Nasir Ayob, Abdul Halim Ismail, Qiling Zang

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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. …”
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Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data by Meijuan Hu, Lianying Zhang, Xiao Wang, Xuehua Xiao

“…We constructed and compared multiple machine learning models, including Linear Regression, Random Forest, and XGBoost, with deep learning models such as CNNs (ResNet101, VGG19) and Transformer technology. …”
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UAV-based multitier feature selection improves nitrogen content estimation in arid-region cotton by Fengxiu Li, Fengxiu Li, Chongqi Zhao, Chongqi Zhao, Yingjie Ma, Yingjie Ma, Ning Lv, Yanzhao Guo, Yanzhao Guo

“…Nevertheless, because high-dimensional remote-sensing data are inherently complex and redundant, accurately estimating cotton plant nitrogen concentration (PNC) from unmanned aerial vehicle (UAV) imagery remains problematic, which in turn constrains both model precision and transferability.MethodsAccordingly, this study introduces a hierarchical feature-selection scheme combining Elastic Net and Boruta–SHAP to eliminate redundant remote-sensing variables and evaluates six machine-learning algorithms to pinpoint the optimal method for estimating cotton nitrogen status.ResultsOur findings reveal that five critical features (Mean_B, Mean_R, NDRE_GOSAVI, NDVI, GRVI) markedly enhanced model performance. …”
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Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM by Boshi Xu, Wenbiao Ma, Wenyan Wu, Yang Wang, Yang Yang, Jun Li, Xun Zhu, Qiang Liao

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Deep learning-driven approach for cataract management: towards precise identification and predictive analytics by Shuaixin Lu, Lingling Ba, Jie Wang, Min Zhou, Peiyao Huang, Xiaohua Zhang, Simo Pan, Xinmiao Zhou, Kai Wen, Jing Sun

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Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation by Huihui Wang, Xiaoxue Che, Jiaxuan Nan, Yuyuan Miao, Yaqi Wang, Wuping Zhang, Fuzhong Li, Jiwan Han

“…Four machine learning models were employed in this study: Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), and Partial Least Squares Linear Discriminant Analysis (PLS-LDA).Results and DiscussionThe conditional WGAN with the gradient penalty was trained for a range of epochs: 1000, 2000, 8000, 10,000, and 20,000. …”
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Development and validation of a nomogram for predicting lung cancer based on acoustic–clinical features by Zhou Lu, Zhou Lu, Jiaojiao Sha, Xunxia Zhu, Xiaoyong Shen, Xiaoyu Chen, Xin Tan, Rouyan Pan, Shuyi Zhang, Shi Liu, Tao Jiang, Jiatuo Xu

“…Furthermore, the nomogram model was compared with predictive models that were developed using six additional machine-learning (ML) methods.ResultsOur acoustic–clinical nomogram model demonstrated a strong discriminative ability, with AUCs of 0.774 (95% confidence interval [CI], 0.716–0.832) and 0.714 (95% CI: 0.616–0.811) in the training and test sets, respectively. …”
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Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the Car... by Jeremy Petch, Juan Pablo Tabja Bortesi, Tej Sheth, Madhu Natarajan, Natalia Pinilla-Echeverri, Shuang Di, Shrikant I Bangdiwala, Karen Mosleh, Omar Ibrahim, Kevin R Bainey, Julian Dobranowski, Maria P Becerra, Katie Sonier, Jon-David Schwalm

“…The AI-based decision support tool was developed using referral information from over 37,000 patients and uses a light gradient boosting machine model to predict the probability of obstructive CAD based on 42 clinically relevant predictors, including patient referral information, demographic characteristics, risk factors, and medical history. …”
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Initial production prediction for horizontal wells in tight sandstone gas reservoirs based on data-driven methods by Jian Sun, Jianwen Gao, Kang Tang, Long Ren, Yanjun Zhang, Zhipeng Miao, Zhe Zhang

“…Second, on the basis of the IPHTSG database, prediction models for the IPHTSG are developed by employing various machine learning algorithms. …”
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Rain-Induced Shallow Landslide Susceptibility Under Multiple Scenarios Based on Effective Antecedent Precipitation by Chuanmei Cheng, Ying Li, Dong Zhu, Yu Liu, Yongqiu Wu, Degen Lin, Hao Guo

“…In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. …”
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WiPy-RT: A Fast Ray Tracing Modeling Platform for RIS-Assisted Wireless Channels by Mohammadreza Farashahi, Boon-Chong Seet, Xue Jun Li

“…This complex propagation environment demands sophisticated modeling techniques to accurately capture and predict channel behavior, essential for the design and optimization of next-generation wireless systems. …”
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