Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass by Kensuke Kawamura, Tsuneki Tanaka, Taisuke Yasuda, Shoji Okoshi, Masaaki Hanada, Kazuya Doi, Toshiya Saigusa, Takanori Yagi, Kenji Sudo, Kenji Okumura, Jihyun Lim

“…We propose a UAV-based LC (LCUAV) estimation and mapping method using a land cover map from a simple linear iterative clustering (SLIC) algorithm and a random forest (RF) classifier. The SLIC-RF classification achieved a high accuracy level for four different ground cover types (grasses, legumes, weeds, and background) in seven distinct meadows with an overall accuracy of 91.4% and an F score of 91.5%. …”
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Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study by Pascal Frédéric Deschwanden, Isabel Hotz, Susan Mérillat, Lutz Jäncke

“…Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors. …”
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IDMPF: intelligent diabetes mellitus prediction framework using machine learning by Leila Ismail, Huned Materwala

“…The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework. …”
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Application of a scoring evaluation of the state of preservation of historic residential and garden sites. case study: Wicimice and Iglice (Zachodniopomorskie Voivodeship) by Michał Uruszczak

“…Surroundings of the objects were given the best scores (3 and 2 points for the parks in Wicimice and Iglice, respectively), which may encourage potential buyers to invest in the sites. Natural landscapes, forests, and listed cultural heritage undoubtedly deserve recognition. …”
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Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China by Fan Zhang, Xiaohong Shi, Shengnan Zhao, Ruonan Hao, Biao Sun, Guohua Li, Shihuan Wang, Hao Zhang

“…Study focus: After implementing the optimal noise reduction strategies based on wavelet transform for the high-frequency monitoring data, hybrid models coupling random forests, support vector machines, and artificial neural networks were employed to simulate the dissolved oxygen in the lake during both the open-water and ice-covered periods. …”
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Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy by Teng Zhan, Ke Xu, Zhipeng Fan, Hanlin Xiang, Congchang Xu, Tianjiao Mei, Yuanyuan Wei, Wentao Chen, Luoxing Li

“…Therefore, four parameters of applied stress and the projected area, location, and morphology of the critical defects were trained using an extreme gradient boosting model (XGBoost) and random forest (RF). The XGBoost model is 95.7 % more accurate than the RF model in predicting fatigue life. …”
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Novel transfer learning approach for hand drawn mathematical geometric shapes classification by Aneeza Alam, Ali Raza, Nisrean Thalji, Laith Abualigah, Helena Garay, Josep Alemany Iturriaga, Imran Ashraf

“…We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. …”
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Predictive value of preoperative pan-immune-inflammation value index in the prognosis of oral cancer patients undergoing radical resection by Weihai Huang, Yulan Lin, Enling Xu, Yanmei Ji, Jing Wang, Fengqiong Liu, Fa Chen, Yu Qiu, Bin Shi, Lisong Lin, Baochang He

“…Univariate and multivariate Cox regression was used to assess the prognostic value of PIV, and propensity score matching (PSM) analysis was used to adjust for potential confounders. Randomized survival forest (RSF) was used to assess the relative importance of preoperative PIV in prognostic prediction. …”
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The development of chemical approaches to fossil hominin ecology in South Africa by Julia Lee-Thorp, Matt Sponheimer

“… When Dart recognised the fossilised skull of the Taung Child as a hominin ancestor, he also observed that its “sere environment” produced few foods preferred by African apes in equatorial forests. He thus set in motion an inquiry into the dietary and environmental proclivities of fossil hominins. …”
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Comparison and Estimation of Surface Albedo of Various Levels of Land use by SEBAL and METRIC Methods by Mehdi Asadi, Khalil Valizadeh khamran, Mohammad Baaghdeh, Hamed Adab

“…The amount of albedo was also examined in agricultural (0.240 based on SEBAL method and 0.247 based on METRIC method) and forest lands (0.149 based on SEBAL method and 0.225 based on METRIC method). …”
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Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system by K. C. Onyelowe, Ahmed M. Ebid, Rosa Belén Ramos Jiménez, Viroon Kamchoom, M. Vishnupriyan, Krishna Prakash Arunachalam

“…Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. …”
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Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation by Ruixin Wu, Ruixin Wu, Sihao Chen, Sihao Chen, Yi He, Yi He, Ya Li, Song Mu, Aishun Jin, Aishun Jin

“…Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.ResultsIn this prognostic analysis, the data from 714 participants with HCNEC were evaluated. …”
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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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Assessment of soil erosion and sediment yield in response to land use and land cover changes using geospatial techniques in Dumuga Watershed, Ethiopia by Zenebe Reta Roba, Mitiku Badasa Moisa, Sanju Purohit, Kiros Tsegay Deribew, Dessalegn Obsi Gemeda

“…The findings highlight a dramatic shift in land use, with cultivated land increasing from 62.3% (915.3 km2) to 77.0% (1,132.0 km2) and forest cover declining sharply from 13.3% (196.0 km2) to 3.8% (56.2 km2). …”
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Estimation of daily groundwater evapotranspiration from diurnal variations of lysimeter experiments data in an arid zone by Peng Yao, Fengzhi Shi, Yuehui Wang, Ningze Dai, Chengyi Zhao

“…Principal component analysis and a random forest model were used to assess meteorological influences. …”
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Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning by Lucijano Berus, Jernej Hernavs, David Potocnik, Kristijan Sket, Mirko Ficko

“…Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. …”
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Evolutionary Genomics Provides Insights Into Endangerment and Conservation of a Wild Apple Tree Species, Malus sieversii by Jian Zhang, Fang‐Yuan Zhao, Hong‐Xiang Zhang

“…Malus sieversii, a relict broad‐leaf forest tree found in arid Central Asian mountains, has a narrow and fragmented distribution and is classified as an endangered species in China. …”
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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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Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation by Yara A. Sultan, Abdelfattah A. Eladl, Mohamed A. Hassan, Samah A. Gamel

“…Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. …”
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Bedrock modulates the elevational patterns of soil microbial communities by Xianjin He, Ruiqi Wang, Daniel S. Goll, Laurent Augusto, Naoise Nunan, M.D. Farnon Ellwood, Quanzhou Gao, Junlong Huang, Shenhua Qian, Yonghua Zhang, Zufei Shu, Buhang Li, Chengjin Chu

“…We therefore investigated soil microbial communities (bacterial and fungal) along two adjacent elevational transects with different bedrocks (granite vs. slate) in a subtropical forest. Our findings reveal that soil microbial communities are shaped by complex interactions between bedrock type and environmental factors along elevational gradients. …”
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