Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation by Xuqian Bai, Shuailong Fan, Ruiqi Li, Tianjin Dai, Wangye Li, Sumeng Ye, Long Qian, Lu Liu, Zhitao Zhang, Haorui Chen, Haiying Chen, Youzhen Xiang, Junying Chen, Shikun Sun

“…The model is developed based on the soil water balance equation and incorporates multi-source remote sensing data. A random forest algorithm is employed as the core predictive framework. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

“…While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.…”
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Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. …”
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Detection of Wormhole Attack in Vehicular Ad-hoc Network over Real Map using Machine Learning Approach with Preventive Scheme by Shahjahan Ali, Parma Nand, Shailesh Tiwari

“…The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…Its integration into diagnostic algorithms may improve clinical management and outcomes in complex IE cases.…”
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Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors by Lirong Zhang, Shaocong Zhao, Wei Yang, Zhongbing Yang, Zhi’an Wu, Hua Zheng, Mingxing Lei, Mingxing Lei, Mingxing Lei

“…Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).ConclusionsThe XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.…”
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Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations by Da-Chun Qiang, Jie Zheng, Zhi-Qiang You, Sheng Yang

“…However, observational limitations may result in misclassifications, potentially leading to a higher proportion of repeaters than currently identified. …”
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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing by Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio, Paolo Poggio

“…Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. Sanger sequencing served as the reference for performance validation. …”
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Effects of respiratory viruses on febrile neutropenia attacks in children by Bilge Aldemir-Kocabaş, Ergin Çiftçi, Gülsan Yavuz, Zümrüt Uysal, Elif İnce, İştar Dolapçı, Zeynep Ceren Karahan, Esra Pekpak, Adem Karbuz, Erdal İnce

“…Identifying viral agents may help to constitute individualized infection-management algorithms in these patients. …”
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Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study by Xiaoli Yuan, Jianbo Qing, Wenqiang Zhi, Feng Wu, Yan Yan, Yafeng Li

“…We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. …”
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Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy by Young Kwang Chae, Emma Yu, Na Hyun Kim, Min Jeong Kim, Leeseul Kim, Hyung-Gyo Cho, Yeonggyeong Park, Yoonhee Choi, Seung Pyo Daniel Hong

“…We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. …”
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Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis by Chengyuan Xu, Chengyuan Xu, Siqi Zhang, Bin Sun, Zicheng Yu, Hailong Liu

“…However, the metabolic and prognostic regulators governing this process remain largely undefined.MethodsWe constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). …”
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Challenges, Difficulties, and Delayed Diagnosis of Multiple Myeloma by Tugba Zorlu, Merve Apaydin Kayer, Nazik Okumus, Turgay Ulaş, Mehmet Sinan Dal, Fevzi Altuntas

“…Educational campaigns to raise awareness of the disease, algorithms dedicated to routine care and novel technologies, including AI and big data analytics, and new biomarkers may serve this purpose, as well as genomic approaches to the premalignant MGUS stage.…”
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Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance by Jian Feng, Zhe Chen, Gaoming Wang, Yu Yao, Xuewen Min, Jing Luo, Kai Xie

“…Differences in immune infiltration and potential mechanisms in LUAD were explored using seven algorithms. The relationship between signature genes, chemotherapy sensitivity, and potential targeted therapies was evaluated. …”
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Enhancing drinking water safety: Real-time prediction of trihalomethanes in a water distribution system using machine learning and multisensory technology by Antonio J. Aragón-Barroso, David Ribes, Francisco Osorio

“…Prolonged exposure to high concentrations of trihalomethanes (THMs) may generate human health risks due to their carcinogenic and mutagenic properties. …”
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Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images by Eric Coy, William Santo, Bonnie Jue, Helen Betts, Francisco Ramos-Gomez, Stuart A. Gansky

“…Average and dominant hue, saturation, and brightness values were features for training plaque-scoring algorithms.Results Best performing models were: Support Vector Machine-Gaussian for image selection, 5-CV AUC-ROC of 0.99 and 0.76s of training time; Gradient-Boosting classification and regression models for individual teeth (5-CV AUC-ROC of 0.99 with 105s training); and mean plaque-scoring algorithms (5-CV R2 of 0.72 with 1415s training).Conclusions Accurate automated plaque-scoring is attainable without the high computational and financial costs of deep learning (DL) models. …”
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Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, E... by Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle

“…This study used different machine learning classification algorithms to predict the features that cause virological failure in HIV-positive patients. …”
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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients. by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

“…The constructed logistic regression model, along with SHAP interpretation, may serve as a clinically useful tool for identifying high-risk NVAF patients. …”
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Costs of testing sick children in primary care with pulse oximetry: Evidence from four countries, both with and without electronic clinical decision support. by Susan Horton, Ulrich Adombi, Fenella Beynon, Mira Emmanuel-Fabula, Tara Herrick, Sandeep Kumar, Suzan Makawia, Mercy Mugo, Michael Onah, Michael Ruffo, Shally Awasthi, Maymouna Ba, Leah F Bohle, Silvia Cicconi, Hélène Langet, Papa Moctar Faye, Honorati Masanja, Andolo Miheso, Deusdedit Mjungu, James Machoki M'Imunya, Ousmane Ndiaye, Kovid Sharma, Valérie D'Acremont, Kaspar Wyss, TIMCI Collaborator Group

“…Adding clinical decision support algorithms (CDSA) can improve adherence to Integrated Management of Childhood Illness guidelines. …”
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