Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…Therefore, accurately predicting bridge deformation is essential for analyzing its causes and detecting potential safety hazards in a timely manner. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Current machine learning (ML) models demonstrate suboptimal predictive performance in KD treatment response prediction, primarily due to their limited ability to effectively process categorical variables and interpret tabular clinical data. …”
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Predicting soil organic matter using corrected field spectra and stacking ensemble learning by Yu Wang, Xuhui Yan, Rongyanting Huo, Longcai Zhao, Jie Peng, Yongsheng Hong, Jing Liu

“…The field prediction of SOM using spectra correction algorithms in conjunction with ensemble learning remains a significant and unresolved challenge. …”
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Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Departing from the prevalent reliance on regression analysis in previous studies, this research demonstrates the limitations of multiple linear regression for accurately predicting transportation costs. Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…This approach represents a significant advancement over previous efforts in the literature, offering more accurate and reliable predictions for oil production forecasting.…”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…<h4>Background</h4>Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. …”
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Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…Additionally, we analyzed the immune microenvironment and enriched pathways across different subtypes using multiple algorithms. Finally, the “oncoPredict” R package was used to assess potential drug sensitivities in high-risk and low-risk groups.ResultsSeventeen polyamine metabolism genes were identified. …”
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Optimization of the Canopy Three-Dimensional Reconstruction Method for Intercropped Soybeans and Early Yield Prediction by Xiuni Li, Menggen Chen, Shuyuan He, Xiangyao Xu, Panxia Shao, Yahan Su, Lingxiao He, Jia Qiao, Mei Xu, Yao Zhao, Wenyu Yang, Wouter H. Maes, Weiguo Liu

“…Accurate early-stage yield prediction of intercropped soybeans is essential for the rapid screening and breeding of high-yield soybean varieties. …”
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Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity by Jian Yu, Jian Yu, Jiangtao Wang, Xinya Liu, Xinya Liu, Cancan Wang, Cancan Wang, Li Wu, Yuanming Zhang, Yuanming Zhang

“…Constructed an mRNA-miRNA-lncRNA network diagram, and performed immune cell composition analysis using CIBERSORT. Finally, predicted potential drugs for key genes using the DSigDB database.ResultsWe obtained 119 genes after intersecting 1380 Differentially Expressed Genes (DEGs) with Ferroptosis-Related Genes (FRGs). …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. …”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Abstract Background Postoperative respiratory failure following cardiac surgery (CS-PRF) remains a critical complication with substantial morbidity and mortality. Current risk prediction models are limited by static assessments and suboptimal accuracy. …”
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Predicting the Risk of Preterm Birth Throughout Pregnancy Based on a Novel Transcriptomic Signature by Yang Pan, Yuxin Ran, Dongni Huang, Nanlin Yin, Yanqing Wen, Yan Jiang, Yamin Liu, Hongbo Qi

“…This study focused on the prediction of preterm birth (PTB). It aimed to identify the transcriptomic signature essential for the occurrence of PTB and evaluate its predictive value in early, mid, and late pregnancy and in women with threatened preterm labor (TPTL). …”
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Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients by Wenyuan Dong, Hongcheng Jiang, Yu Li, Luo Lv, Yuxin Gong, Bao Li, Hongjie Wang, Hesong Zeng

“…This study evaluated the predictive value of ten immunoinflammatory indexes for CHD risk in NAFLD patients using an interpretable machine learning framework. …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. …”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…Optimizing the hyperparameters, evaluating the prediction accuracy, and comparing the performance of these AI models reveal that the gradient boosting regressor (GBR) is the superior method for this simulation. …”
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A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease by Yuhan Xia, Yuezhong Huang, Min Gong, Weirong Liu, Yuanhui Meng, Huiyang Wu, Hui Zhang, Hao Zhang, Luyi Weng, Xiao-Li Chen, Huixian Qiu, Xing Rong, Rongzhou Wu, Maoping Chu, Xiu-Feng Huang

“…Summary: Accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial for the effective treatment of Kawasaki disease(KD). …”
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Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis by John P. Coetzee, John P. Coetzee, Xiaojian Kang, Xiaojian Kang, Victoria Liou-Johnson, Victoria Liou-Johnson, Victoria Liou-Johnson, Ines Luttenbacher, Srija Seenivasan, Elika Eshghi, Daya Grewal, Siddhi Shah, Frank Hillary, Emily L. Dennis, Emily L. Dennis, Maheen M. Adamson, Maheen M. Adamson, Maheen M. Adamson

“…This may be driven by underlying biological changes resulting from the injury. Machine learning algorithms can use structural MRIs to give a predicted brain age (pBA). …”
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Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach by Xingyu Liu, Jiali Mo, Zuting Liu, Yanqiu Ge, Tian Luo, Jie Kuang

“…This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability. …”
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Rapid Damage Assessment and Bayesian-Based Debris Prediction for Building Clusters Against Earthquakes by Xiaowei Zheng, Yaozu Hou, Jie Cheng, Shuai Xu, Wenming Wang

“…Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. …”
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