A Method for the 3D Reconstruction of Landscape Trees in the Leafless Stage by Jiaqi Li, Qingqing Huang, Xin Wang, Benye Xi, Jie Duan, Hang Yin, Lingya Li

“…Three-dimensional models of trees can help simulate forest resource management, field surveys, and urban landscape design. …”
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Artificial intelligence for herbicide recommendation: Case study for the use of clomazone in Brazilian soils by Hamurábi Anizio Lins, Matheus de Freitas Souza, Lucrecia Pacheco Batista, Luma Lorena Loureiro da Silva Rodrigues, Francisca Daniele da Silva, Bruno Caio Chaves Fernandes, Stefeson Bezerra de Melo, Paulo Sergio Fernandes das Chagas, Daniel Valadão Silva

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Recensement d'éléphants dans la Réserve Communautaire du Lac Télé, République du Congo by Fortuné Iyenguet, Guy-Aimé Malanda, Bola Madzoke, Hugo Rainey, Catherine Schloeder, Michael Jacobs

“…We estimated that the reserve holds low densities of elephants in seasonally flooded and swamp forest. Elephants are present in the terra firma forest in the high-water season only. …”
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Climatic characteristics of snow water equivalent in the Perm Krai area by N. A. Kalinin, A. D. Kryuchkov, I. A. Sidorov, R. K. Abdullin, A. N. Shikhov

“…In general, the ERA5-Land reanalysis reproduces SWE in the Perm region satisfactorily. Mean relative error for SWE in March does not exceed 15 %. The average correlation coefficient between the reanalysis data and the same from the observations is 0.72 for non-forest locations and 0.83 for locations in forest. …”
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FOLU-Net: A novel framework using long short-term memory networks to predict future forestry and other land use by Sanchali Banerjee, Paige T. Williams, Randolph H. Wynne

“…The objective of this study is to predict future tropical forest cover presence and types using multitemporal imaging spectroscopy data. …”
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Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content by Xuguang Sun, Baoyuan Zhang, Menglei Dai, Cuijiao Jing, Kai Ma, Boyi Tang, Kejiang Li, Hongkai Dang, Limin Gu, Wenchao Zhen, Xiaohe Gu

“…Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. …”
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Hybrid Tree-Based Machine Learning Models for State-of-Charge and Core Temperature Estimation in EV Batteries by Aya Haraz, Khalid Abualsaud, Ahmed M. Massoud

“…Among the combinations tested, the Extra Trees Regressor-Random Forest (ETR-RF) model delivered the highest estimation accuracy, while the Decision Tree-LightGBM (DT-LGBM) model exhibited the fastest training time. …”
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Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado, João Manuel R. S. Tavares

“…The proposed method accurately classifies various land cover (LC) types in multispectral satellite images, including Pastures, Other Built-Up Areas, Water Bodies, Urban Areas, Grasslands, Forest, Farmland, and Others. The post-processing scheme includes a spectral bag-of-words model and K-medoids clustering to refine the Deeplab v3+ outputs and correct possible errors. …”
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Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In this sense, a modeling evaluation of the inhibition of Pin1 using quinazoline, benzophenone, and pyrimidine derivatives was performed by using multilinear, random forest, SMOreg, and IBK regression algorithms on a dataset of 51 molecules, which was divided randomly in 78% for the training and 22% for the test set. …”
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Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques by Bailin Yue, Yong Jin, Shangrong Wu, Jieyang Tan, Youxing Chen, Hu Zhong, Guipeng Chen, Yingbin Deng

“…Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. …”
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Machine Learning to Retrieve Gap-Free Land Surface Temperature from Infrared Atmospheric Sounding Interferometer Observations by Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Carmine Serio, Italia De Feis

“…Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. …”
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Hybrid deep learning framework for real-time DO prediction in aquaculture by Longqin Xu, Wenjun Liu, Cai Chengqing, Tonglai Liu, Xuekai Gao, Ferdous Sohel, Murtaza Hasan, Mansour Ghorbanpour, Shahbaz Gul Hassan, Shuangyin Liu

“…However, off-the-shelf models, such as Random Forest (RF) and Back Propagation (BP) have demonstrated poor performance due to intricate interactions in aquatic ecosystems, which leads to complex data patterns. …”
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Determining water status of walnut orchards using the crop water stress index and canopy temperature measurements by Lian Mao, Sen Lu, Linqi Liu, Zhipeng Li, Baoqing Wang, Dong Pei, Yongchao Bai

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Improving the quantification of peak concentrations for air quality sensors via data weighting by C. Frischmon, J. Silberstein, A. Guth, E. Mattson, J. Porter, M. Hannigan

“…When compared to unweighted colocation data, we demonstrate significant reductions in both error (root mean square error, RMSE) and bias (mean bias error, MBE) for pollutant peaks across all three datasets when data weighting is employed. …”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. …”
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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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An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications by Shuchismita Biswas, Tianzhixi Yin, Syed Ahsan Raza Naqvi, Jim Follum, Antos Cheeramban Varghese, Tawsif Ahmad, Pavel Etingov

“…In the first use case, a random forest (RF) classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. …”
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Validity of a Single Inertial Measurement Unit to Measure Hip Range of Motion During Gait in Patients Undergoing Total Hip Arthroplasty by Noor Alalem, Xavier Gasparutto, Kevin Rose-Dulcina, Peter DiGiovanni, Didier Hannouche, Stéphane Armand

“…Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. …”
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Predictive modeling of burnout dimensions based on basic socio-economic determinants in health service managers and support personnel in a resource-limited health center by Grey Castro-Tamayo, Mario Hernandez-Tapia, Ivan David Lozada-Martinez, Ivan Portnoy, Jessica Manosalva-Sandoval, Tobías Parodi-Camaño

“…Statistical analyses included correlation tests and predictive models using random forest models to identify significant associations and cast predictions.ResultsA total of 76 participants were included. …”
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Modelling and Optimisation of Hysteresis and Sensitivity of Multicomponent Flexible Sensing Materials by Kai Chen, Qiang Gao, Yijin Ouyang, Jianyong Lei, Shuge Li, Songxiying He, Guotian He

“…First, multifactor experiments were conducted to obtain experimental data for the prediction models; the prediction models for the hysteresis and sensitivity performance of sensing materials were constructed using response surface methodology (RSM), Random Forest (RF), long short-term memory (LSTM) network, and HKOA-LSTM. …”
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