Machine learning techniques for estimating high–temperature mechanical behavior of high strength steels by C. Yazici, F.J. Domínguez-Gutiérrez

“…Various ML models—including Lasso Regression, Gradient Boosting, Random Forest, Extreme Gradient Boosting, Support Vector Regression, and Adaptive Boosting were rigorously evaluated to determine their predictive capabilities. …”
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A Dynamic Analysis of the Role of the Planetary- and Synoptic-Scale in the Summer of 2010 Blocking Episodes over the European Part of Russia by Anthony R. Lupo, Igor I. Mokhov, Merseid G. Akperov, Alexander V. Chernokulsky, H. Athar

“…The excessive heat resulted in forest and peat fires, impacted terrestrial ecosystems, greatly increased pollution in urban areas, and increased mortality rates in the region. …”
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The role of EIP-AGRI Operational Groups as a driver for innovation in viticulture by Chiara Mignani, Annapia Ferrara, Sabrina Tomasi, Michele Moretti, Alessio Cavicchi

“…Operational Groups (OGs) within the Partnership provide “interactive innovation” platforms in which research institutions work with farmers, advisors, businesses, NGOs and other interest groups to co-create innovative solutions for agriculture and forestry as well as rural communities; the rationale is that when farmers and foresters are engaged in the process, the solutions are more likely to be based on their concrete reality and thus relevant.  …”
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The Assessment of the Green Development of the Tobacco Industry Using a Multicriteria Method by Giedrė Lapinskiene, Martynas Blazaitis, Dainora Gedvilaite, Neringa Slavinskaite

“…According to the views of experts, the most significant sub-criteria for the green development of the tobacco industry are increasing energy efficiency; safeguarding against hazardous wastewater in the environment; reducing the content of hazardous materials used in products; improving air, land, and water quality where economic activity takes place; sustainable forest management; eco-design, especially for efficient material use, biodegradability, and recyclability; and collaboration with suppliers. …”
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Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction by Van Nhanh Nguyen, Nghia Chung, G.N. Balaji, Krzysztof Rudzki, Anh Tuan Hoang

“…Indeed, five different MLs were employed including linear regression, decision tree, random forest, XGBoost, and Gradient Boosting Regression. …”
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Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits by Peng Chen, Xutong Shao, Guangyu Wen, Yaowu Song, Rao Fu, Xiaoyan Xiao, Tulin Lu, Peina Zhou, Qiaosheng Guo, Hongzhuan Shi, Chenghao Fei

“…To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. …”
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Genomic insights into ecological adaptation of oaks revealed by phylogenomic analysis of multiple species by Tian-Rui Wang, Xin Ning, Si-Si Zheng, Yu Li, Zi-Jia Lu, Hong-Hu Meng, Bin-Jie Ge, Gregor Kozlowski, Meng-Xiao Yan, Yi-Gang Song

“…Quercus is a keystone genus in Northern Hemisphere forests, and its wide distribution in diverse ecosystems and long evolutionary history make it an ideal model for studying the genomic basis of ecological adaptations. …”
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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

“…The proposed ExAIRFC-GSDC model integrates machine learning algorithms, particularly a Random Forest Classifier, with explainable artificial intelligence (XAI) techniques to enhance interpretability. …”
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A Novel Method to Forecast Nitrate Concentration Levels in Irrigation Areas for Sustainable Agriculture by Halil Karahan, Müge Erkan Can

“…The proposed model, although based on an artificial neural network (ANN), also has the potential to be adapted for methods used in machine learning and artificial intelligence, such as Support Vector Machines, Decision Trees, Random Forests, and Ensemble Learning Methods.…”
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Identification of Malignancy-Associated Changes in Histologically Normal Tumor-Adjacent Epithelium of Patients with HPV-Positive Oropharyngeal Cancer by James Jabalee, Anita Carraro, Tony Ng, Eitan Prisman, Cathie Garnis, Martial Guillaud

“…Measurements of 89 nuclear features were used to train a random forest-based classifier to discriminate between normal and tumor nuclei. …”
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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

“…The proposed supervised machine learning algorithms comprise support vector machines, random forest, boosting, and long short-term memory (LSTM) recurrent neural network types. …”
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Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics by Md. Ashraful Haque, Jamal Hossain Nirob, Kamal Hossain Nahin, Noorlindawaty Md Jizat, M. A. Zakariya, Redwan A. Ananta, Wazie M. Abdulkawi, Khaled Aljaloud, Samir Salem Al-Bawri

“…Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. …”
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Temperatures and treeline elevation in the Sierra Nevada de Mérida of the Venezuelan Andes by Vicente Marcano, Rafael Navarro-González, Christopher P. McKay

“…Below 3,500 to 2,500 m elevation in the cloud forest zone the topographic rate decreases to 0.9°C/km. …”
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Watershed: a more efficient sampling unit for mountain camera traps by Jun-Jie Li, Yi-Hao Fang, Ji-Cong Zhan, Xue-Jun Yang, Can-Bin Huang, Yan-Peng Li, Kun Tan, Zhi-Pang Huang, Liang-Wei Cui, Wen Xiao

“…In southwest China’s mountain forests, under comparable sampling intensities, we contrasted the capture rate (CR), species richness, and relative abundance index (RAI) of dominant species among watershed, 1 × 1 km² grid, and elevation gradient patterns. …”
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An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning by S. M. Taslim Uddin Raju, Amlan Sarker, Apurba Das, Md. Milon Islam, Mabrook S. Al-Rakhami, Atif M. Al-Amri, Tasniah Mohiuddin, Fahad R. Albogamy

“…This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. …”
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Amphibians and reptiles from Leyte Sab-a Basin Peatland: A unique wetland habitat in Leyte, Philippines by Syrus Cesar P. Decena, Francis Ian C. Banado, Libertine Agatha F. Densing

“…The majority (69%) of the amphibian species occurred within the peatland area (peat swamp forests, grassland, and agricultural areas), while almost all (92%) of the reptile species were at least utilizing peatland edge/ecotone habitats. …”
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Cropland non-agriculturization and agricultural green development: Evidence from the Yangtze River Economic Belt, China by Chunxia Zhu, Yangyang Sun, Haolun Xu

“…The results reveal that the CLN in the YEB is predominantly manifested through the conversion of cropland to forest and construction land, exhibiting a decelerating trend and pronounced spatial disparities. …”
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Structural Characterization of Prosopis africana Populations (Guill., Perrott., and Rich.) Taub in Benin by Towanou Houètchégnon, Dossou Seblodo Judes Charlemagne Gbèmavo, Christine Ajokè Ifètayo Nougbodé Ouinsavi, Nestor Sokpon

“…The structural characterization of Prosopis africana of Benin was studied on the basis of forest inventory conducted in three different vegetation types (savannah, fallow, and field) and three climate zones. …”
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Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction by Waqar A. Sulaiman, Charithea Stylianides, Andria Nikolaou, Andria Nikolaou, Zinonas Antoniou, Ioannis Constantinou, Lakis Palazis, Anna Vavlitou, Theodoros Kyprianou, Theodoros Kyprianou, Efthyvoulos Kyriacou, Antonis Kakas, Antonis Kakas, Marios S. Pattichis, Andreas S. Panayides, Constantinos S. Pattichis, Constantinos S. Pattichis

“…Using machine learning models, including Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP), we identified GB as the best performing model outperforming the other models with an AUC of 0.730, accuracy of 69.93%, sensitivity of 88.20%, and specificity of 40.95% on the original dataset. …”
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Associations between Ozone and Emphysema: A Systematic Review and Meta-analysis by Amja Manullang, Yueh-Lun Lee, Vincent Laiman, Jer-Hwa Chang, Hsiao-Chi Chuang

“….: particulate matter, nitrogen oxides and ozone), and the results were visualized in forest plots. We observed that a 1-ppb rise in O3 was associated with a 0.30 increase in the percent emphysema progression (95% CI: 0.02, 0.57, p < 0.05). …”
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