Spatiotemporal analysis of urban expansion and its impact on farmlands in the central Ethiopia metropolitan area by Kalid Hassen Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete

“…The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. …”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. …”
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Evolution of SARS-CoV-2 in white-tailed deer in Pennsylvania 2021-2024. by Andrew D Marques, Matthew Hogenauer, Natalie Bauer, Michelle Gibison, Beatrice DeMarco, Scott Sherrill-Mix, Carter Merenstein, Ronald G Collman, Roderick B Gagne, Frederic D Bushman

“…Prevalence was higher in WTD in regions with crop coverage rather than forest, suggesting an association with proximity to humans. …”
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Les bois de construction du boulevard Dr Henri-Henrot à Reims/Durocortorum by Willy Tegel

“…The exceptional number of wooden elements recovered on site not only provides information on the state of the forest at that time, but also on its management, both of which may be related to changing environmental conditions and socio-economic processes. …”
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Effects of Vegetation Restoration on Soil Infiltration and Runoff in the Gully Regions on the Loess Plateau by MA Xueyan, MU Xingmin, WANG Shuangyin, BAI Yungang, NIU Fangpeng

“…[Results] (1) Vegetation restoration significantly increased the values of soil infiltration characteristics and capacity, with the order of artificial forest > natural grassland > corn farmland. (2) Compared to bare ground, grassland increased the transformation of rainfall into soil storage, reduced surface runoff, and led to the appearance of multiple layers of interflow. (3) Compared to bare ground, grassland showed more rapid changes in soil moisture content, richer runoff components, and less runoff volume. (4) When the intensity of rainfall on bare ground was much greater than the infiltration capacity of the land, surface runoff would be formed rapidly, and the amount of infiltration would be small, and there was a shallow and relatively impermeable layer in naturally restored grassland, and there was a big difference between the infiltration capacity of the upper and lower soil layers, so as to form a loamy mid-stream. …”
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Automated potato tuber mass estimation and grading with multiangle 2D images by Ayush K. Sharma, Lincoln Zotarelli, Alina Zare, Lakesh K. Sharma

“…In the second step, a random forest classification model was developed to grade the potato tubers based on image-extracted tuber width dimensions from the top, side, and both angles. …”
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Utilization of Sanitized Human Excreta and Wood Ash for Establishing Multipurpose <i>Ficus Thonningii</i> Blume in a Degraded Tantalum Technosol by D. Neina

“…Hardwood cuttings of ficus from homesteads in  Western Rwanda were planted in 20.4 × 19 cm diameter pots containing 5 kg forest soil (FS) and 6 kg Technosol. Five treatments including No amendment; HUF alone (100 mL/pot); HUF+WA (100 mL + 60 g/pot); FM (200 g/pot); and FM+WA (200 g + 60 g/pot) were prepared in ten replicates each. …”
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Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection by Nazime Tokgöz, Ali Değirmenci, Ömer Karal

“…The classification methods employed include Decision Tree, Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbor, coupled with the Mutual Information (MI) feature selection algorithm. …”
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Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review by Filip Orzan, Ştefania D. Iancu, Laura Dioşan, Zoltán Bálint

“…The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics.ResultsThe analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. …”
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Machine Learning-Integrated Usability Evaluation and Monitoring of Human Activities for Individuals With Special Needs During Hajj and Umrah by Ghadah Naif Alwakid

“…The proposed study used two machine learning models, i.e., random forest and sequential neural networks, both with 93% accuracy. …”
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Genetic diversity of the earthworm Eisenia nordenskioldi (Lumbricidae, Annelida) by S. V. Shekhovtsov, D. I. Berman, E. V. Golovanova, S. E. Peltek

“…It inhabits a wide range of habitats, from tundra to forest steppe, and is characterized by high morphological, ecological, and karyotypic diversity. …”
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Zika, a Mosquito-Transmitted Virus by L. Phil Lounibos, Barry W. Alto, Nathan D. Burkett-Cadena, Cynthia C. Lord, Chelsea T. Smartt, C. Roxanne Rutledge Connelly, Jorge R. Rey

“…In its native range in West Africa and Uganda, the Zika virus stays in the forest for the most part, and human infections are considered incidental and medically inconsequential. …”
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Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding by Milan Lazic, Earl Woodruff, Jenny Jun

“…Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…These data were collected from the Urban Resilience Project of Rajdhani Unnayan Kartripakkha (RAJUK), which is the development authority of Dhaka. Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. …”
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Cold Hardiness and Physio-Biochemical Responses of Annual Branches in Five Early-Fruiting Walnut Varieties (<i>Juglans regia</i> L.) Under Simulated Low-Temperature Stress by Zitong Ni, Haifang Hu, Guiqing Xu

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Value Chain Analysis of Highland Bamboo (Yushania alpina) in Banja District, Awi Zone, Ethiopia by Mulat Mengstu, Asmamaw Alemu, Chernet Worku, Sosina Bezie

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Limites de la cooperación internacional ambiental: el caso del Programa Piloto para la Protección de los Bosques Tropicales de Brasil by Elimar Pinheiro do Nascimento, Joseph S. Weiss

“…Beyond its many achievements, an analysis of the green alliances of the Pilot Program for the Protection of the Tropical Forests of Brazil (PPG7, Portuguese acronym), as well as the conflicts present in its creation and implementation, of the results achieved, and of the circumstances in which it came to an end, demonstrate that the PPG7 contributed little to the conservation of the forest, the institutional strengthening of the Amazon region, or its sustainable development. …”
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Prediction of end-point phosphorus content of molten steel in BOF with machine learning models by Kang Y., Ren M.-M., Zhao J.-X., Yang L.-B., Zhang Z.-K., Wang Z., Cao G.

“…Four machine learning regression models (Lasso, Random Forest, Xgboost, and Neural Network) were established to predict the end-point phosphorus content of molten steel in the BOF based on raw and auxiliary material data, process parameters, and production quality data. …”
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Land Use Change Detection Between Tarsus - Karataş in Lower Seyhan Plain with Spectral Angle Mapper Technique by Mamadou Traore, Senem Tekin, Tolga Çan

“…According to the results obtained, growth of 192%, 37%, 7% and 8% growth in settlement, non-cultivated agriculture, forest and semi-natural and lagoon / lakes areas between 1985-2019, and 43% and 21% in bare and cultivated agricultural areas Decreases in rates of 21 have occurred. …”
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Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction by Ming Wei, Xiaopeng Du

“…This paper innovatively PM2.5proposes a high-accuracy prediction model: RF-ICHOA-CNN-LSTM-Attention. First, the Random Forest (RF) model is utilized to evaluate the importance of air pollution and meteorological features and select more suitable input features. …”
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