Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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Modified tree-based selection in hierarchical mixed-effect models with trees: A simulation study and real-data application by Asrirawan, Khairil Anwar Notodiputro, Budi Susetyo, Sachnaz Desta Oktarina

“…These methods utilize the classification and regression trees (CART) algorithm to select the best tree through a backfitting algorithm. …”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…All models were externally validated on 1,334 ECGs from another hospital, with performance assessed by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and predictive values. Results: In external validation, the transfer learning model achieved the highest AUROC (0.903; 95% CI: 0.887-0.918), closely followed by the general model (0.899; 95% CI: 0.883-0.915); the difference was not significant. …”
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Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods by Weiping Kong, Weiping Kong, Lingling Ma, Huichun Ye, Huichun Ye, Jingjing Wang, Chaojia Nie, Binbin Chen, Xianfeng Zhou, Wenjiang Huang, Zikun Fan

“…We concluded that the nonlinear Gaussian process regression model with the VIs and TFs-PC1 combination selected by maximal information coefficient as input achieved the highest accuracy in LCC prediction for banana, with the highest R2 of 0.776 and lowest RMSE of 2.04. …”
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A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Jianxing He, Ying Huang

“…Subsequently, innovative implementation of the principal component analysis (PCA), Weighted Quantile Sum (WQS) regression, and Bayesian Kernel Machine Regression (BKMR) approaches were employed to assess the joint impact of the various intake of FAs, as well as total saturated, monounsaturated, and polyunsaturated FAs on PRISm. To facilitate the prediction of PRISm, six distinct machine learning algorithms were constructed, followed by the application of SHAP analysis to elucidate the contribution of individual predictors. …”
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Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation by Yoonsung Kwon, Asta Blazyte, Yeonsu Jeon, Yeo Jin Kim, Kyungwhan An, Sungwon Jeon, Hyojung Ryu, Dong-Hyun Shin, Jihye Ahn, Hyojin Um, Younghui Kang, Hyebin Bak, Byoung-Chul Kim, Semin Lee, Hyung-Tae Jung, Eun-Seok Shin, Jong Bhak

“…We further developed a robust diagnostic risk prediction system to classify anxiety disorders from healthy controls using the 17 biomarkers. …”
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Digital-twin driven alignment control method for marine shafting with air spring vibration isolation system by Song Liu, Liang Shi, Wei Xu, ZeChao Hu

“…First, we design a digital twin prediction model based on the neural network to describe the data mapping relationship between the air spring pressures and shafting alignment state. …”
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Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science by Uddalak Das

“…Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…It also focused on finding the optimal hyperparameter settings for training the model, as balancing prediction accuracy, training time, and computational demands is crucial for real-world implementation. …”
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Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility by Tingjin Wu, Deqing Lin, Yi Chen, Jinxiu Wu

“…On this basis, restorative benefit evaluation models were established, including the explanatory and predictive models. The explanatory model used Pearson’s correlation and multiple linear regression analysis to identify the key indicators affecting restorative benefits, and the predictive model used the XGBoost 1.7.3 algorithm to predict the restorative benefit scores on the campus scale. …”
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Prognostic Value of a Classification and Regression Tree Model in Patients with Open-Globe Injuries by Danica T. Esteban, MD, Karlo Marco D. Claudio, MD, Cheryl A. Arcinue, MD

“…Purposive sampling of hospital medical records was done to collect data from both in- and out-patient cases. The CART algorithm was utilized to determine the predicted visual outcome for each case, and the accuracy of prognostication was measured by computing for sensitivity, specificity, positive predictive value, and negative predictive value. …”
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Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy by Xueming Ma, Xueming Ma, Xueming Ma, Dongnuan Yao, Dongnuan Yao, Dongnuan Yao, Weitao Yu, Weitao Yu, Weitao Yu, Gongping Wu, Gongping Wu, Gongping Wu, Chengwei Fan, Chengwei Fan, Chengwei Fan, Junqiang Tian, Junqiang Tian, Junqiang Tian

“…The constructed prognostic model showed excellent predictive performance, and the areas under the curves of survival rates at different times were all high in both the training set and the test set. …”
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Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation by Xia Liu, Guowei Zheng, Iman Beheshti, Shanling Ji, Zhinan Gou, Wenkuo Cui

“…<b>Results:</b> Our prediction model achieved a desirable prediction accuracy on the independent test samples, demonstrating its robust performance. …”
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Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks by Rasendram Muralitharan, Upul Jayasinghe, Roshan G. Ragel, Gyu Myoung Lee

“…Our results show that the Random Forest algorithm achieves the highest prediction accuracy, while a convolutional neural network demonstrates the best mitigation performance with accuracy. …”
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Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…In MACE prediction, our model outperformed the traditional score-based models, with 8.2% higher AUC than Systematic COronary Risk Evaluation (SCORE), 3.5% for SCORE 2 and 7.1% for the Framingham Risk Score (with p value&lt;0.05 for all three comparisons).Conclusions Our algorithm estimates the 5-year risk of MACE based on retinal images, while explicitly presenting which risk factors should be checked and intervened. …”
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Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma by Zhen Wang, Zhen Wang, Zhen Wang, Gangchen Zhou, Gangchen Zhou, Rongchuan Cao, Rongchuan Cao, Guolin Zhang, Guolin Zhang, Yongxu Zhang, Yongxu Zhang, Mingyue Xiao, Longbi Liu, Longbi Liu, Xuesong Zhang

“…To identify distinct molecular subtypes, a multi-omics data integration approach was employed, utilizing 10 distinct clustering algorithms. Survival analysis, immune infiltration profiling and drug sensitivity predictions were then used to evaluate the prognostic significance and therapeutic responses of these subtypes. …”
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A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery by John B. Kilbride, Robert E. Kennedy

“…However, this improvement came at the cost of increased model prediction bias.…”
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PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts by Weiwei Wang, Bingqing Liu, Song Gao, Jiang Li, Yueling Zhou, Songyang Zhang, Zhi Ding

“…The proposed PhA-MOE for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>a</mi><mrow><mi>p</mi><mi>h</mi><mi>y</mi></mrow></msub></semantics></math></inline-formula> prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. …”
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Advanced classification of optical water types and ensemble learning models for Chl-a inversion in Dongting and Poyang lakes using Sentinel-2 remote sensing: assessing the impact o... by Kai Xiong, Bin Deng, Jiang Liu, Zhixin Guan, Weizhi Lu, Changbo Jiang, Wei Luo, Han Rao, Longbin Yin, Kang Yang

“…The results demonstrated the superior stability and predictive accuracy of the Voting strategy under low Chl-a conditions in Dongting Lake, achieving a maximum MAPE reduction of 84.76 %. …”
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Improving Accuracy and Calibration of Deep Image Classifiers With Agreement-Driven Dynamic Ensemble by Pedro Conde, Rui L. Lopes, Cristiano Premebida

“…Possible strategies to tackle this problem are two-fold: (i) models need to be highly accurate, consequently reducing this risk of failure; (ii) facing the impossibility of completely eliminating the risk of error, the models should be able to inform the level of uncertainty at the prediction level. As such, state-of-the-art DL models should be <italic>accurate</italic> and also <italic>calibrated</italic>, meaning that each prediction has to codify its confidence/uncertainty in a way that approximates the true likelihood of correctness. …”
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