Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China by Chen Chen, Mimi Peng, Mahdi Motagh, Xinxin Guo, Mengdao Xing, Yinghui Quan

“…In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (Root Mean Square Error = 3.53 mm) and susceptibility (Area Under the Curve = 0.97). …”
Get full text

Health extension workers job satisfaction and associated factors in Ethiopia: a systematic review and meta-analysis by Amlaku Nigusie Yirsaw, Gebeyehu Lakew, Eyob Getachew, Mulugeta Desalegn Kasaye, Ermias Bekele Enyew, Demis Getachew, Abiyu Abadi Tareke

“…Potential publication bias and heterogeneity in the results between studies were assessed using Egger's test, forest plot, and I2 statistic, respectively. Results The pooled level of job satisfaction among health extension workers in Ethiopia was 46% (95%CI: 32%–60%). …”
Get full text

Study on the driving factors of watershed runoff change in Zuli River by Budyko hypothesis and soil and water assessment tool model by Yun Zhao, Rui Zhang, Heping Shu, Yaxian Li, Zhi Xu, Qiang Wang

“…The sensitivity of land use to runoff changes was the most pronounced from 2000 to 2020, the land use categories most sensitive were grassland, cultivated land, forest land, and building land. The primary factor contributing to decrease in runoff was human activities, and the verification and rate of SWAT models are regularly acceptable. …”
Get full text

SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence by G. Sathish Kumar, E. Suganya, S. Sountharrajan, Balamurugan Balusamy, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…Three benchmark datasets and the classifier models logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine and Naïve Bayes are used for experimentation. …”
Get full text

Rapid identification of high-temperature Daqu Baijiu with the same aroma type by UV-VIS sensor of HBT combined with Zn2+ by Yanmei Zhu, Yuanyuan Su, Yipeng Cang, Hengye Chen, Wanjun Long, Wei Lan, Xue Jiang, Haiyan Fu

“…The specific mechanism of light signal change was mainly based on the competitive coordination effect of pyrazines and other nitrogen-containing compounds in high-temperature Daqu Baijiu and small molecular probe HBT on Zn2+ and the excited state intramolecular proton transfer (ESIPT) mechanism of HBT itself. Results: The random forest results showed that the prediction set classification accuracy was improved from 62.37% to 100%. …”
Get full text

Nest site selection during the second breeding attempt in Japanese tits (Parus minor): effects of nest site characteristics by Xudong Li, Jiangping Yu, Li Zhang, Dake Yin, Keqin Zhang, Mingju E, Haitao Wang

“…We compared nest site features of the Japanese tit’s nest boxes used for the first brood, those used for the second brood, and control nest boxes (which were unoccupied and located in the same forest patch as the nest boxes for the second brood during the corresponding year). …”
Get full text

Pollen contamination and mating structure in maritime pine (Pinus pinaster Ait.) clonal seed orchards revealed by SNP markers by Bouffier, Laurent, Debille, Sandrine, Alazard, Pierre, Raffin, Annie, Pastuszka, Patrick, Trontin, Jean-François

“…Maritime pine (Pinus pinaster Ait.) is a major forest tree species in south-western Europe. In France, an advanced breeding program for this conifer species has been underway since the early 1960s. …”
Get full text

Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. …”
Get full text

Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…We assessed the performance of six widely used empirical models (Hargreaves Samani, modified Hargreaves Samani, Romanenko, Schendel, Priestley-Taylor, and Makkink) and four ML models (random forest, extreme gradient boosting, deep neural network, and long short-term memory) to estimate ET0 with limited climatic variables in Nepal. …”
Get full text

Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study by Ping Zheng, Ting Pan, Ya Gao, Juan Chen, Liren Li, Yan Chen, Dandan Fang, Xuechun Li, Fei Gao, Yilei Li

“…Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. …”
Get full text

Wide-ranging and Violent Volcanic History of a Quiet Transborder Area: Volcanic Geoheritage of the Novohrad–Nógrád UNESCO Global Geopark by Szabolcs Harangi, Imre Szarvas, Réka Lukács

“…The Ipolytarnóc Site, the gateway of the geopark and possessor of a European- Diploma for Protected Areas, documents when one of these devastating eruption events buried a subtropical-forested area with thick pyroclastic deposits and preserved vertebrate footprints. …”
Get full text

Male involvement in antenatal care follow-up and its determinants in Ethiopia: a systematic review and meta-analysis by Gossa Fetene Abebe, Desalegn Girma, Melsew Setegn Alie, Lidiya Gutema Lemu, Yilkal Negesse, Zewditu Alelign

“…We assessed publication bias using a funnel plot and Begg’s test. The forest plot presented the combined proportion of male involvement and OR, along with a 95% CI.Results The pooled proportion of male involvement in ANC follow-up in Ethiopia was 43.3% (95% CI 31.7% to 54.8%). …”
Get full text

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder by Cyrus Su Hui Ho, Jinyuan Wang, Gabrielle Wann Nii Tay, Roger Ho, Hai Lin, Zhifei Li, Nanguang Chen

“…Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. …”
Get full text

Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). …”
Get full text

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

“…The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774–0.821). …”
Get full text

Unravelling spatiotemporal propagation processes among meteorological, soil, and evaporative flash droughts from a three-dimensional perspective by Chen Hu, Dunxian She, Gangsheng Wang, Liping Zhang, Zhaoxia Jing, Zhihong Song, Jun Xia

“…Additionally, we utilized the random forest model to identify critical factors influencing flash drought propagation. …”
Get full text

Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. …”
Get full text

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants by Xiangjun Qi, Shujing Wang, Caishan Fang, Jie Jia, Lizhu Lin, Tianhui Yuan

“…Models constructed within the mlr3 framework included recursive partitioning and regression trees, random forest, kernel k-nearest neighbors, naïve bayes, and light gradient boosting machine (LightGBM). …”
Get full text

Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection by Yang Dai, Yingbao Yang, Xin Pan, Penghua Hu, Xiangjin Meng, Fanggang Li, Zhenwei Wang

“…We compared the accuracy of the proposed method with the Holmes, multichannel, and Random Forest algorithms. Results showed that the proposed method had lowest RMSE, with the value of 3.28 K (1.95 K), 2.69 K (1.65 K), and 3.71 K (2.22 K) on grassland, cropland, and barren land at daytime (nighttime), respectively. …”
Get full text

Seasonal investigation of ultrafine-particle organic composition in an eastern Amazonian rainforest by A. E. Thomas, H. S. Glicker, A. B. Guenther, R. Seco, O. Vega Bustillos, J. Tota, R. A. F. Souza, J. N. Smith

“…Here, we present measurements of the composition of ultrafine particles collected in the Tapajós National Forest (2.857° S, 54.959° W) during three different seasonal periods: 10–30 September 2016 (SEP), 18 November–23 December 2016 (DEC), and 22 May–21 June 2017 (JUN). …”
Get full text