Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring by Xiaokai Chen, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, Kang Yu

“…This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. …”
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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

“…The performance of the proposed model is benchmarked against state-of-the-art methods, including Decision Tree and Random Forest classifiers. Additionally, the robustness of the proposed technique is confirmed under conditions of DG uncertainty and in the presence of noise. …”
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iFLOW: A Framework and GUI to Quantify Effective Thermal Diffusivity and Advection in Permeable Materials From Temperature Time Series by A. Bertagnoli, C. Luce, R. vanKampen, U. Schneidewind, M. vanBerkel, A. W. Tranmer, G. Vandersteen, S. Krause, D. Tonina

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Predicting task performance in robot-assisted surgery using physiological stress and subjective workload: a case study with interpretable machine learning by Kaiqi Wei, Chika Kimura, Megumi Shimura, Yoshihiro Shimomura, Xue Zhao, Takaaki Tamura, Shinichi Sakamoto

“…Several classification models, including CatBoost, random forest, logistic regression, and support vector machines, were trained to predict task performance. …”
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Enhancing environmental models with a new downscaling method for global radiation in complex terrain by A. Druel, J. Ruffault, H. Davi, A. Chanzy, O. Marloie, M. De Cáceres, A. Olioso, F. Mouillot, C. François, K. Soudani, N. K. Martin-StPaul

“…Finally, by using two different PBMs (CASTANEA, a PBM simulating tree growth, and SurEau, a plant hydraulic model simulating hydraulic failure risk), we showed that accounting for fine-resolution radiation can have a great impact on predictions of forest functions.…”
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Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context by Xiaoqing Song, Yi Long, Ling Zhang, David G. Rossiter, Fengyuan Liu, Wei Jiang

“…In this study, we built a linear evaluation model based on geographical weighted regression (GWR) and a nonlinear evaluation model based on a random forest (RF) to quantify the relationship between geographical factors and the positioning bias of MPL data. …”
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Hydrological Modelling and Remote Sensing for Assessing the Impact of Vegetation Cover Changes by Ángela M. Moreno-Pájaro, Aldhair Osorio-Gastelbondo, Dalia A. Moreno-Egel, Oscar E. Coronado-Hernández, María A. Narváez-Cuadro, Manuel Saba, Alfonso Arrieta-Pastrana

“…The analysis revealed a significant loss of natural vegetation: dense forest cover declined dramatically from 14.38% in 2000 to 0% in 2020, and clean pastures were reduced by 46%. …”
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From Land Conservation to Famers’ Income Growth: How Advanced Livelihoods Moderate the Income-Increasing Effect of Land Resources in an Ecological Function Area by Xinyu Zhang, Yiqi Zhang, Yanjing Yang, Wenduo Wang, Xueting Zeng

“…Using an integrated Fixed-Effects–SVM–Genetic Algorithm framework, we quantify nonlinear policy-livelihood interactions and simulate multi-scenario governmental interventions (e.g., ecological investment, returning farmland to forest/RFF) across Beijing’s EFA, which can obtain the key findings as follows: (a) Ecological land resources have a significant positive effect on farmers’ incomes due to production-manner adjustment guided by governmental green strategy and corresponding TSP in an ecological restoration area of an EFA, while they have a non-significant impact in the core ecological reserve areas on account of the strict environmental protection restrictions on economic activities. …”
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Robust County-Level Corn Yield Estimation Using Ensemble Machine Learning and Multisource Remote Sensing by Alireza Vafaeinejad, Alireza Sharifi, Shahid Nawaz Khan

“…Two ensemble models, random forest (RF) and extreme gradient boosting (XGBoost), are trained and evaluated under both clean and simulated degraded data conditions. …”
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AI-Driven Ensemble Classifier for Jamming Attack Detection in VANETs to Enhance Security in Smart Cities by Walid El-Shafai, Ahmad Taher Azar, Saim Ahmed

“…Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). …”
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A Practical Method for Red-Edge Band Reconstruction for Landsat Image by Synergizing Sentinel-2 Data with Machine Learning Regression Algorithms by Yuan Zhang, Zhekui Fan, Wenjia Yan, Chentian Ge, Huasheng Sun

“…Three machine learning algorithms (ridge regression, gradient boosted regression tree (GBRT), and random forest regression) were then employed to build the red-edge reconstruction model for different vegetation types. …”
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Balancing Monitoring and Management in the Adaptive Management of an Invasive Species by Brielle K. Thompson, Julian D. Olden, Sarah J. Converse

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Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. …”
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Hypertension a Predictive Risk Factor on Progression to Alzheimer’s Disease Using APOEε4 as a Benchmark by Mingfei Li, Ying Wang, Lewis Kazis, Weiming Xia

“…This significant association is validated through a Random Forest method, a machine learning approach with bootstrap simulations. …”
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The influence of jittering DHS cluster locations on geostatistical model-based estimates of malaria risk in Cameroon by Salomon G. Massoda Tonye, Romain Wounang, Celestin Kouambeng, Penelope Vounatsou

“…Among the important predictors identified in the true data, distance to water bodies and presence of forest were mostly influenced by the jittering. Altitude and vegetation index were the least affected predictors. …”
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Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems by Francesco Russo, Antonio Comi, Giovanna Chilà

“…International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning when a disaster can occur involves studying the risk reduction. …”
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The Development Path and Carbon-Reduction Method of Low-Carbon Pilot Urban Areas in China by Lining Zhou, Qingqin Wang, Haizhu Zhou, Yiqiang Jiang, Rongxin Yin, Tong Lu

“…At the same time, after comparing models, such as random forest and support vector machine, the XGBoost algorithm is adopted for short-term prediction (R² = 0.984, MAE = 0.195). …”
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Hybrid AI and semiconductor approaches for power quality improvement by Ravikumar Chinthaginjala, Asadi Srinivasulu, Anupam Agrawal, Tae Hoon Kim, Sivarama Prasad Tera, Shafiq Ahmad

“…In contrast, traditional ML models like SVM and Random Forest had difficulties with class imbalance, resulting in lower precision and recall. …”
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Impacts of vegetation greening and climate change on trend and interannual variability in vegetation productivity in the Wuyi Mountain region by Juan He, Mengmiao Yang, Jane Liu, Jing M. Chen, Xinyao Xie

“…In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. …”
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Adapting Semi-Supervised Segmentation methods to Multimodal Remote Sensing Data by I. Hernandez-Sequeira, D. Ibanez, R. Fernandez-Beltran, F. Pla

“…Remote sensing (RS) imagery is important for applications ranging from land cover and land use (LCLU) mapping to agriculture and forest monitoring. However, there is a limited availability of high-quality labeled data to use as a reference to train supervised learning (SL) models. …”
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