Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning by A. W. Smith, I. J. Rae, C. Forsyth, D. M. Oliveira, M. P. Freeman, D. R. Jackson

“…Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. …”
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RiskTree: Decision trees for asset and process risk assessment quantification in big data platforms by Zhan Haomou, Yang Jiawei, Guo Zhenyang, Cao Jin, Zhang Dong, Zhao Xingwen, You Wei, Li Hui

“…Finally, we apply a random forest algorithm to compute risk index weights, risk values, and risk levels, enabling the quantification of risks on big data platforms. …”
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Towards Fault Detection in Industrial Equipment through Energy Consumption Analysis: Integrating Machine Learning and Statistical Methods by Baddou Nada, Dadda Afaf, Rzine Bouchra, Hmamed Hala

“…In our case study, we began by evaluating the prediction model to confirm the performance of LSTM, comparing it with several machine learning models commonly used in the literature, such as Random Forest, Support Vector Machines (SVM), and GRU. After assessing different loss functions, the LSTM model achieved the strongest prediction accuracy, with an RMSE of 0.07, an MAE of 0.0188, and an R2 of 92.7%. …”
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Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments by Mohammad Arbabpour Bidgoli, Arian Behmanesh, Navid Khademi, Phromphat Thansirichaisree, Zuduo Zheng, Sara Saberi Moghadam Tehrani, Sajjad Mazloum, Sirisilp Kongsilp

“…Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. …”
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Kurgans of the Sargat Culture in the Middle Irtysh River Basin: New Approaches in Estimation of Social Landscape by Maxim A. Grachev, Oksana Yu. Zimina, Nickolay V. Prikhodko, Svetlana V. Sharapova

“…The selected scanning area covers imposing “Princely kurgans” situated on a plowed field and small mounds inside birch forest. The scanning results were processed with various filters for further creation of a digital terrain model (DTM), and then based on DTM, a topographical relief of the area under study was presented with a step of 1.0 m and of 0.1–0.2 m for sites of kurgan groups’ locations. …”
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Description of the New Species <i>Laccaria albifolia</i> (Hydnangiaceae, Basidiomycota) and a Reassessment of <i>Laccaria affinis</i> Based on Morphological and Phylogenetic Analys... by Francesco Dovana, Roberto Para, Gabriel Moreno, Edoardo Scali, Matteo Garbelotto, Bernardo Ernesto Lechner, Luigi Forte

“…<i>Laccaria</i> is a diverse and widespread genus of ectomycorrhizal fungi that form symbiotic associations with various trees and shrubs, playing a significant role in forest ecosystems. Approximately 85 <i>Laccaria</i> species are formally recognised, but recent studies indicate this number may be an underestimation, highlighting the need for further taxonomic studies to improve our understanding of species boundaries. …”
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Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery by Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding

“…To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. …”
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Revealing the fireworks-set-off pattern of spatial multi-function expansion across cities leveraging big geodata – a case of the Greater Bay Area, China by Ku Gao, Xiaomei Yang, Zhihua Wang, Yueming Liu, Huifang Zhang, Xiaoliang Liu, Qingyang Zhang

“…We found that (1) across-cities SMFE exhibited a fireworks-set-off pattern, including sprawling along river in the plains between coastal port cities and inland core cities and diffusing from inland core cities on the plains to inland node cities in the mountains; (2) social-living, business-trade, and industry-production functions were sequentially primary expanding functions; (3) paddy and forest were two major land cover types encroaching upon inland cities, while coastal cities primarily suffered from losses of water. …”
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Multispecies occupancy models unravel reduced colonization probabilities in plants from the unique Amazonian cangas by Rafael de Fraga, Luiz Gustavo Rodrigues Oliveira Santos, Valeria da Cunha Tavares, Leonardo Carreira Trevelin, Maurício Takashi Coutinho Watanabe, Leandro Maioli, Samir Rolim, Carolina da Silva Carvalho

“…We used a set of 22 plant species distributed in a globally unique ecosystem, the eastern Amazonian mosaics of forests, iron-rich open “cangas” and iron mine lands, as a model to test whether dynamic multispecies occupancy models can be used to assess population decline. …”
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Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network by Qing Xu, Guiying Yang, Xiaobin Yin, Tong Sun

“…Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. …”
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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

“…Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. …”
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Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach by Fajar Yulianto, Gatot Nugroho, Galdita Aruba Chulafak, Suwarsono Suwarsono

“…Three digital classification approaches, namely, maximum likelihood (ML), random forest (RF), and the support vector machine (SVM), were applied to test the improvement in the accuracy of LULC postclassification using the MaSegFil approach, based on annual cloud-free Landsat 8 satellite imagery data from 2019. …”
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Monitoring Agricultural Land Changes in Peri-Urban Oran, Algeria: A Mixed Methods Analysis by Rabia Samah Choukri, Tarik Ghodbani, Muhammad Salem

“…The methodology encompasses five main tasks: (1) data collection and identification of significant temporal markers, (2) implementation of a Random Forest classification using medium resolution Landsat imagery, (3) assessment of the extent and patterns of agricultural land changes, (4) Evaluation of relevant planning documents, (5) field work, including stakeholders interviews and focus groups. …”
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The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin by Sarang Yi, Daeil Hyun, Seokmoo Hong

“…A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. …”
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AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques by Hesham Allam, Chris Davison, Faisal Kalota, Edward Lazaros, David Hua

“…Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. …”
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Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models by Zhe Yang, Mengjie Qu, Yun Pan, Ruohong Huan

“…A novel training strategy for decision-tree-based methods is also proposed in this paper, resulting in an improvement on the random forest model which achieves competitive performance at an average F1-score (accuracy) of 94.49&#x0025; (95.09&#x0025;), 91.64&#x0025; (92.21&#x0025;), and 92.70&#x0025; (93.29&#x0025;) on the three datasets, compared with state-of-the-art solutions for cross-subject HAR.…”
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Divergence of alpine plant populations of three Gentianaceae species in the Qinling sky Island by Peng-Cheng Fu, Bing-Jie Mo, He-Xin Wan, Shu-Wen Yang, Rui Xing, Shan-Shan Sun

“…The redundancy and gradient forest analyses revealed that several temperature- and precipitation-related variables mainly contributed to shaping the genetic differentiation among the Qinling populations and others, indicating that the three species exhibited a similar pattern of adaptations to local environments. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…In pursuit of these objectives, the CNN GRU model was rigorously tested and compared against three additional models: CNN with bidirectional long short-term memory (BiLSTM), extreme gradient boosting (XGBoost), and random forest (RF). Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. …”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…Logistic Regression accomplishes an accuracy of 99.5% in validating with the discrete dataset, whereas for validating with the ranged dataset, both Random Forest and SVM achieve 100% accuracy. Finally, to interpret and understand the behavior and prediction of the model, a LIME explainer has been utilized.…”
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Tick species, tick-borne pathogen distribution and risk factor analysis in border areas of China, Russia and North Korea by Pengfei Min, Jianchen Song, Shaowei Zhao, Zhen Ma, Yinbiao Meng, Zeyu Tang, Zhenyu Wang, Sicheng Lin, Fanglin Zhao, Meng Liu, Longsheng Wang, Lijun Jia, Lijun Jia

“…I. persulcatus was the main species in the forest environment, while H. longicornis was the main species in grasslands and animal surface. …”
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