Use of machine learning techniques for modeling of snow depth by G. V. Ayzel

“…In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France), Sodankyla (Finland), and Snoquamie Pass (USA).Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. …”
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Combing GOME-2B and OMI Satellite Data to Estimate Near-Surface NO₂ of Mainland China by Donghui Li, Kai Qin, Jason Cohen, Qin He, Shuo Wang, Ding Li, Xiran Zhou, Xiaolu Ling, Yong Xue

“…Third, the estimated NS-NO₂ is consistent with surface observations in spatial distribution, and successfully represent both inter-annual changes and seasonal characteristics. …”
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Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN by Seokchan Kim, Yeong-Jae Kim, Dongwook Lee, Hanmin Lee

“…In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. …”
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Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces by Yaopeng Ji, Shengyuan Song, Jianping Chen, Jingyu Xue, Jianhua Yan, Yansong Zhang, Di Sun, Qing Wang

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Contents and ecological stoichiometry characteristics of soil carbon, nitrogen and phosphorus in wetlands of Ningxia plain by BO Xiaoyan, MI Wenbao, XU Hao, ZHANG Xueyi, MI Nan, SONG Yongyong

“…Ecological stoichiometry is a comprehensive and effective method to learn the relationship and regularity of the elements in the biogeochemical cycle and the ecological process. …”
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Machine Learning Approach on Time Series for PV-Solar Energy by S. Sivakumar, B. Neeraja, M. Jamuna Rani, Harishchander Anandaram, S. Ramya, Girish Padhan, Saravanakumar Gurusamy

“…In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. …”
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An Integrated Method for Inverting Beach Surface Moisture by Fusing Unmanned Aerial Vehicle Orthophoto Brightness with Terrestrial Laser Scanner Intensity by Jun Zhu, Kai Tan, Feijian Yin, Peng Song, Faming Huang

“…Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. …”
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Meta‐Attention Deep Learning for Smart Development of Metasurface Sensors by Yuan Gao, Wei Chen, Fajun Li, Mingyong Zhuang, Yiming Yan, Jun Wang, Xiang Wang, Zhaogang Dong, Wei Ma, Jinfeng Zhu

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AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context by Juan Chen, Jiahui Yao, Wei Chen, Feng Zhang, Heyuan Wang, Xiaoying Xu, Huan Ge, Hongmei Zhou, Jin Cen, Dan Li, Bengui Jiang, Li He, Tingting Fu, Zhengxian Xu, Lei Chu, Shuxia Zhang, Dongmei Yao, Linyi Wei, Liu Huang, Anjing Ge, Cuiping Jin, Zimu Fu, Qin Liu, Xuefeng Yu, Chengmao Zhao, Tengjiao Wang, Lan Zhu

“…Summary: Background: Pelvic floor surface electromyography (sEMG) is widely used to evaluate and treat pelvic floor dysfunctions (PFDs). …”
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Unveiling multiscale drivers of wind speed in Michigan using machine learning by Carson Evans, Laiyin Zhu, Kathleen Baker, Lei Meng

“…The Shapley Additive Values (SHAP) analysis reveals that local climate variables, including the proximity to the nearest Great Lake, surface roughness, and surface temperature, are the most influential predictors and are most important in the model. …”
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Predicting Performance of Hall Effect Ion Source Using Machine Learning by Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe

“…Accurate performance prediction methods are essential for the development of high‐efficiency Hall effect ion sources, which are employed in industries ranging from material surface treatment to spacecraft electric propulsion (known as Hall thrusters). …”
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Application of Deep Learning in Forest Fire Prediction: A Systematic Review by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…Commonly used metrics include accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Key meteorological features, such as Temperature, Humidity, and Wind speed, have been extensively studied using the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Moisture Index (NDMI), the most commonly used satellite-derived features. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…Hyper-parameter tuning was conducted using Bayesian optimization and cross-validation, while the performance of random forests was evaluated in comparison to gradient boosting, extreme gradient boosting, artificial neural networks, and the generalized linear model.FINDINGS: The random forests models, particularly those utilizing under sampling and cost-sensitive learning, demonstrated superior performance, achieving sensitivity of 0.81, Specificity of 0.85, accuracy of 0.85, and an area under the receiver operating characteristic curve of 0.89 on an independent test set. …”
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Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses by Visakh Sivaprasad, Mehdi Rahmati, Anne Springer, Harry Vereecken, Carsten Montzka

“…Surface soil moisture (SSM) is a crucial climate variable of the Earth system that regulates water and energy exchanges between the land and atmosphere, directly influencing hydrological, biogeochemical, and energy cycles. …”
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Channel estimation for reconfigurable intelligent surface-aided millimeter-wave massive multiple-input multiple-output system with deep residual attention network by Xuhui Zheng, Ziyan Liu, Shitong Cheng, Yingyu Wu, Yunlei Chen, Qian Zhang

“…We first model the channel estimation in sixth-generation (6G) systems as a super-resolution problem and adopt a deep residual attention approach to learn the nontrivial mapping from the received measurement to the reconfi-gurable intelligent surface (RIS) channel. …”
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Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie, Li Yan

“…The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. …”
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Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction by Bon H. Koo, Ho Chit Siu, Dava J. Newman, Ellen T. Roche, Lonnie G. Petersen

“…To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. …”
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Identification of People in a Household Using Ballistocardiography Signals Through Deep Learning by Karin Takahashi, Yoshinobu Tanno, Hitoshi Ueno

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Tribological performance of graphene oxide reinforced PEEK nanocomposites with machine learning approach by Yagnik Patel, Unnati Joshi, Prince Jain, Anand Joshi, Sanketsinh Thakor, Swapnil Parikh

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