Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions by Zeyad T. Aklah, Hussein T. Hassan, Amean Al-Safi, Khalid Aljabery

“…Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. …”
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Minimizing Delay in UAV-Aided Federated Learning for IoT Applications With Straggling Devices by Mudassar Liaq, Waleed Ejaz

“…We also propose a deep reinforcement learning (DRL)-based solution to improve runtime complexity. Simulation results show the effectiveness of the proposed framework compared to existing approaches.…”
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Data-driven modeling for evaluating deformation of a deep excavation near existing tunnels by Fengwen Lai, Songyu Liu, Jim Shiau, Mingpeng Liu, Guojun Cai, Ming Huang

“…A dataset from numerical simulation is created for training and testing four machine learning models (i.e., artificial neural network (ANN), support vector machines (SVM), random forest (RF), and light gradient boosting machine (LightGBM)), which predict the maximum wall deflection, ground surface settlement, horizontal and vertical displacements of the tunnel. …”
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Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles by Janjhyam Venkata Naga Ramesh, C. Dastagiraiah, Suraya Mubeen, W. Deva Priya, M. Kameswara Rao, B. H. K. Bhagat Kumar

“…Abstract Autonomous Aerial Vehicles (AAVs) play a significant role in emergency response and disaster management, such as during forest fires, earthquakes, and tsunamis. However, navigating through high-density obstacle environments poses substantial challenges due to process overheads, dynamic environmental conditions, and scalability issues. …”
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Empirical ecology to support mechanistic modelling: Different objectives, better approaches and unique benefits by Steven F. Railsback, Cara A. Gallagher, Volker Grimm, Matthew A. McCary, Bret C. Harvey

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Risk Assessment of Hip Fracture Based on Machine Learning by Alessio Galassi, José D. Martín-Guerrero, Eduardo Villamor, Carlos Monserrat, María José Rupérez

“…Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.…”
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Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance by Bala Subramanian C, Bharathi ST, Shanmugapriya S

“…To evaluate its performance, we conducted a series of simulations comparing it with established methods such as Decision Trees, Random Forests, and Gradient Boosting. …”
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Response of nitrogen emissions to land use changes and driving forces analysis in Jiangxi province under multiple scenarios by Chun Fu, Libin Xiang, Yezhong Liu, Tianming Zhou

“…Results revealed that 2,588 km2 of cultivated and forest land was converted to construction land during 2000–2020. …”
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A Geomechanical Modeling Method for Shale Oil Reservoir Cluster Well Area Based on GridSearchCV by HUANG Lei, QI Yin, CHEN Weihua, DU Xianfei, MA Bing, TANG Jizhou

“…The reliability of the three-dimensional geomechanical model is validated by comparing numerical simulation results with well logging interpretation results. …”
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Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change by Xiaomeng Yin, Guoyong Leng, Jiali Qiu, Xiaoyong Liao, Shengzhi Huang, Jian Peng

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Optimising Control Device Luring Strategies for Invasive Predator Control: A Modelling Approach by Giorgia Vattiato, Patrick M. Garvey, Rachelle N. Binny, Michael J. Plank, Andrew M. Gormley, Graham J. Hickling

“…We first use a Bayesian estimation method to quantify trap‐shyness in a population of brushtail possum (Trichosurus vulpecula) in a New Zealand forest; the resulting estimated parameters are then used to calibrate a stochastic, individual‐based model simulating the outcomes of different luring scenarios. …”
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Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections by Ahmad Almatroudi

“…Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. …”
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Potential risks of soil erosion in North-Central Vietnam using remote sensing and GIS by Nguyen T T. Ha, Tran T. Tuyen, Astarkhanova T. Sarzhanovna, Hoang T. Thuy, Vu V. Luong, Tran D. Du, Dau K. Tai, Hoang A. The, Nguyen N. Thanh, Phung T. Duong, Vo T. T. Ha, Vo T. N. Khanh

“…It was verified that the forest area gradually decreased from 2010_2021, and the average annual soil loss was approximately 25 t per year. …”
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Soil quality improvement on Qinghai-Tibet Plateau induced by soil hydrothermal changes from 1980s to 2020s by Kuan Chen, Jingyao Suo, Xiaodong Song, Yiyao Liu, Xiang Xiang, Yingping Pan, Jiarui Wang, Liang Ren, Xiaodong Ge, Xia Xu, Xiuchen Wu, Lei Duan, Yongmei Huang

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Wear fault diagnosis in journal bearings using vibration analysis and AI by Jebur Nazik A., Soud Wafa A.

“…Gradient Boosting performed best overall and had the highest accuracy (99.5%). Random Forest and AdaBoost also showed high classification accuracy. …”
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Human driven climate change increased the likelihood of the 2023 record area burned in Canada by Megan C. Kirchmeier-Young, Elizaveta Malinina, Quinn E. Barber, Karen Garcia Perdomo, Salvatore R. Curasi, Yongxiao Liang, Piyush Jain, Nathan P. Gillett, Marc-André Parisien, Alex J. Cannon, Aranildo R. Lima, Vivek K. Arora, Yan Boulanger, Joe R. Melton, Laura Van Vliet, Xuebin Zhang

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Isolation by distance versus landscape resistance: Understanding dominant patterns of genetic structure in Northern Spotted Owls (Strix occidentalis caurina). by Mark P Miller, Raymond J Davis, Eric D Forsman, Thomas D Mullins, Susan M Haig

“…The second hypothesis could indicate that forest management practices mandated by the Northwest Forest Plan succeeded with one of its primary goals. …”
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Adaptation Strategies for Hemp in Alkaline Salt Environments: Fertilizer Management for Nutrient Uptake and Optimizing Growth by Yunshu Ye, Haoyu Wang, Panpan Zhang, Yuhong Zhang

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Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots by Hang Li, Ichchha Thapa, Shuang Xu, Peisi Yang

“…Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. …”
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A two-leaf daily GPP model based on a rectangular hyperbolic model adjusted for air temperature and vegetation type by Qiuxiang Yi, Fumin Wang, Fumin Wang, Fumin Wang

“…The performance of the TL-RHM is evaluated using data from 21 CO2 eddy-covariance flux sites, covering four vegetation types: evergreen needleleaf forest, deciduous broadleaf forest, grassland, and evergreen broadleaf forest. …”
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