Heterogeneidad ambiental y alteraciones antrópicas en comunidades de manglar en el pacífico sur de México by Ameyali Moreno-Martínez, Gustavo Álvarez-Arteaga, María Estela Orozco-Hernández

“…[Resultados]: El inventario forestal mostró la presencia de las especies Rhizophora mangle, Laguncularia racemosa y Conocarpus erectus, cuya distribución está relacionada, principalmente, con el régimen hídrico del suelo, pH y salinidad. …”
Get full text

Distribution and Main Influencing Factors of Net Ecosystem Carbon Exchange in Typical Vegetation Ecosystems of Southern China by Yike Wang, Xia Liu, Weijia Lan, Shuxian Yin, Liya Fan, Boru Mai, Xuejiao Deng

“…This study investigated the NEE characteristics of typical evergreen coniferous forest ecosystems (ECFEs), tree-and-crop mixed ecosystems (TCMEs), and coastal crop ecosystems (CCEs) in southern China. …”
Get full text

The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context by M. Staudinger, M. Kauzlaric, A. Mas, G. Evin, B. Hingray, D. Viviroli

“…After routing the simulated runoff, we analyzed the important patterns and drivers of extreme flooding at the outlet of the Aare River basin using a random forest. The different return period classes had distinct key predictors and showed specific spatial patterns of antecedent conditions in the sub-catchments, leading to different degrees of extreme flooding. …”
Get full text

Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
Get full text

Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods by Mengni Zhang, Shipeng Zhang, Xudong Ao, Lisha Liu, Shunlin Peng

“…However, cumulative incidences of secondary malignant neoplasms were comparable between the two groups (P = 0.100). The random forest (RF) model demonstrated the highest concordance index of 0.701 among all models. …”
Get full text

Glaucoma detection and staging from visual field images using machine learning techniques. by Nahida Akter, Jack Gordon, Sherry Li, Mikki Poon, Stuart Perry, John Fletcher, Thomas Chan, Andrew White, Maitreyee Roy

“…Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%.…”
Get full text

Alpine vegetation community patterns in the Khumbu region, Nepalese Himalaya by Ruolin Leng, Stephan Harrison, Elizabeth A. Byers, Mahesh Magar, Harkrei Rai, Ram Raj Rijal, Karen Anderson

“…Field data captured during in situ surveys in the Gokyo valley, Nepal, were used to drive and then test a random forest classifier. Grassy meadows and dwarf shrubs belonging to the Rhododendron and Juniperus families dominate the ecology of the alpine zone in this region, so we created three vegetation classes for mapping indicative major plant communities dominated by these species. …”
Get full text

Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery by Edward N. Yap, Jie Huang, Joshua Chiu, Robert W. Chang, Bradley Cohn, Judith C. F. Hwang, Mary Reed

“…Patients’ EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance. …”
Get full text

Climate variability, population growth, and globalization impacting food security in Pakistan by Sohail Abbas, Ahtisham Haider, Shazia Kousar, Heli Lu, Siqi Lu, Fang Liu, Huan Li, Changhong Miao, Wanfu Feng, Muhammad Irfan Ahamad, Muhammad Sajid Mehmood, Rana Muhammad Zulqarnain

Get full text

Multimedia: multimodal mediation analysis of microbiome data by Hanying Jiang, Xinran Miao, Margaret W. Thairu, Mara Beebe, Dan W. Grupe, Richard J. Davidson, Jo Handelsman, Kris Sankaran

“…The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. …”
Get full text

Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs by Shenghan Zhang, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang, Liming Zhou

“…Additionally, to highlight the validating effect, three sophisticated predictors, including k-nearest neighbors (KNN), support vector regression (SVR), and random forest (RF), are introduced as competitors to implement a contrast. …”
Get full text

Combined Liver Stiffness and Α-fetoprotein Further beyond the Sustained Virologic Response Visit as Predictors of Long-Term Liver-Related Events in Patients with Chronic Hepatitis... by Sheng-Hung Chen, Hsueh-Chou Lai, Wen-Pang Su, Jung-Ta Kao, Po-Heng Chuang, Wei-Fan Hsu, Hung-Wei Wang, Tsung-Lin Hsieh, Hung-Yao Chen, Cheng-Yuan Peng

“…Cox regression and random forest models identified the key factors, including longitudinal LS and noninvasive test results, that could predict LREs, including hepatocellular carcinoma, during prespecified follow-ups from 2010 to 2021. …”
Get full text

Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…Radiomics features were extracted using the Python radiomics feature extractor and DL features were obtained using a 3D ResNet model. A Random Forest machine learning algorithm was employed to train four models using: (1) clinical features only; (2) clinical and radiomics features; (3) clinical and DL features; and (4) clinical, radiomics, and DL features. …”
Get full text

Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…The effectiveness of GCN was demonstrated through comparisons with the support vector machine, random forest, and multilayer perceptron across four classification tasks (normal control (NC) vs. …”
Get full text

Prevalence of prolonged transitional neonatal hypoglycemia and associated factors in Ethiopia: A systematic review and meta-analysis. by Solomon Demis Kebede, Amare Kassaw, Tigabu Munye Aytenew, Kindu Agmas, Demewoz Kefale

“…Heterogeneity among the studies was assessed using a forest plot, I2 statistics, and Egger's test. Data extraction was conducted from May 20 to May 27, 2023, for studies published since 2020. …”
Get full text

Soil carbon-food synergy: sizable contributions of small-scale farmers by Toshichika Iizumi, Nanae Hosokawa, Rota Wagai

“…Methods We applied random forest machine learning models to global gridded datasets on crop yield (wheat, maize, rice, soybean, sorghum and millet), soil, climate and agronomic management practices from the 2000s (n = 1808 to 8123). …”
Get full text

Spatial-temporal pattern and driving mechanism of urban land use eco-efficiency in mountainous counties based on multi-source data: a case study of Zhejiang province, China by Li Fan, Lindong Ma, Lindong Ma, Zhongwei Huang

“…On the local scale, the cold spot significant area was mainly distributed in the north and south of Zhejiang province, and significant spatial and temporal variations were observed within the hot spot significant area. (3) The results of factor detection showed that altitude (X1), topographic relief (X2), and forest cover (X3) always played a strong role in affecting ULUEE. …”
Get full text

Microbiota analysis of perimenopausal women experiencing recurrent vaginitis in conjunction with urinary tract infection by Yingying Bi, Yuezhu Wang, Wu Li, Yuhang Chen, Jinlong Qin, Huajun Zheng

“…A total of 147 predicted pathways were significantly different between patients and healthy controls, with the microbiota of the anus exhibiting the greatest number of functional changes, followed by the urine microbiota. A random forest model composed of 16 genera in the lower vaginal end had the highest discriminatory power (AUC 81.48%) to predict RV/UTI. …”
Get full text

Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping by Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo

“…In this study, we evaluated the performance and feasibility of three CNN models (1-D CNN, 2-D CNN, and 3-D CNN), and U-net for coarse-resolution satellite image classification and compared them to a random forest (RF) classifier. We utilized time-series, coarse resolution (1 km) composite imageries acquired by FengYun-3C visible and infrared radiometer. …”
Get full text

An Automated Compliance Framework for Critical Infrastructure Security Through Artificial Intelligence by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Rafi Us Shan

“…We evaluate the performance of the model using 3-ML classifiers including Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). …”
Get full text