Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation by Peiqi Kang, Shuo Jiang, Peter B. Shull, Benny Lo

“…The regression estimation error is 4.16 degrees, and the R² is 0.81. …”
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Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers by Krzysztof Przybył, Daria Cicha-Wojciechowicz, Natalia Drabińska, Małgorzata Anna Majcher

“…In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). …”
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High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach by Sunny Md. Saber, Kya Zaw Thowai, Muhammad Asifur Rahman, Md. Mehedi Hassan, A.B.M. Mainul Bari, Asif Raihan

“…To address these challenges and fill substantial deficiencies in existing prediction models, we have introduced a novel hybrid tree-based stacking machine learning framework integrating Extra Trees, AutoGluon Tabular, and LightGBM, with Random Forest Regressor (RFR) as the meta-learner. Utilizing Automatic Identification System (AIS) data from vessels in the Baltic Sea, our model significantly improves ETA predictions, achieving a mean absolute percentage error (MAPE) of 0.25 %. …”
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Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces by K. Balamurugan, G. Sudhakar, Kavin Francis Xavier, N. Bharathiraja, Gaganpreet Kaur

“…The dynamic gesture recognition system uses Random Forest as its lightweight machine learning model to achieve 93.4 % accuracy in mapping gestures to command sequences which represents a 14.6 % enhancement above conventional static models. …”
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Physics-informed modeling and process optimization of friction stir welding of AA7075-T6 with a zinc interlayer by Dejene Alemayehu Ifa, Dame Alemayehu Efa, Naol Dessalegn Dejene, Sololo Kebede Nemomsa

“…The ANN model yielded an extremely low prediction error of 0.973 %, while the validation through FEA showed an accuracy with only 1.79 % deviation. …”
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Novel conditional tabular generative adversarial network based image augmentation for railway track fault detection by Ali Raza, Rukhshanda Sehar, Abdul Moiz, Ala Saleh Alluhaidan, Sahar A. El-Rahman, Diaa Salama AbdElminaam

“…We developed five advanced neural network techniques for comparison with railway track image classification. The random forest approach surpasses state-of-the-art studies with a high accuracy score of 0.99 for railway track fault detection. …”
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Research on Motion Transfer Method from Human Arm to Bionic Robot Arm Based on PSO-RF Algorithm by Yuanyuan Zheng, Hanqi Zhang, Gang Zheng, Yuanjian Hong, Zhonghua Wei, Peng Sun

“…To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. …”
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MICROBOCENOSIS OF THE RHIZOSPHERE OF SOFT WHEAT WHEN USING BIOLOGICAL PRODUCTS by Natalya N. Shuliko, Elena V. Tukmacheva, Irina A. Korchagina, Alina A. Kiseleva, Olga F. Khamova, Artem Yu. Timokhin, Yuri Yu. Parshutkin, Ekaterina V. Kubasova

“…The research was carried out on spring soft wheat in field experiments of the Omsk Agricultural Scientific Center in the southern forest-steppe of Western Siberia in order to determine the effect of the use of biological products of associative diazotrophs on the biological (ecological) properties of the rhizosphere of spring soft wheat and to determine their relationship with crop yield. …”
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AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete by Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda

“…These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…We compared Extreme Gradient Boosting, Support Vector Regression (SVR) and Random Forest algorithms to assess community structure based on FP raw data. …”
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Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat by Sehijpreet Kaur, Vijaya Gopal Kakani, Brett Carver, Diego Jarquin, Aditya Singh

“…The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. …”
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Retrieval of Dissolved Organic Carbon Storage in Plateau Lakes Based on Remote Sensing and Analysis of Driving Factors: A Case Study of Lake Dianchi by Yufeng Yang, Wei Gao, Yuan Zhang

“…The mixed-layer depth (2 m) was determined through error minimization analysis of 16 vertical profiles. …”
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Actual Truck Arrival Prediction at a Container Terminal with the Truck Appointment System Based on the Long Short-Term Memory and Transformer Model by Mengzhi Ma, Xianglong Li, Houming Fan, Li Qin, Liming Wei

“…The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. …”
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Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios With Foliage by Donggu Lee, Ozgur Ozdemir, Ram Asokan, Ismail Guvenc

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A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones by Mohamed Abdellatief, Mahmoud E. Abd-Elmaboud, Mohamed Mortagi, Ahmed M. Saqr

“…The CNN’s performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN’s superiority, achieving a coefficient of determination (R2) = 0.849 and a lower root mean square error (RMSE) = 0.18%, outperforming conventional models. …”
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Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records by Richul Oh, Hyunjoong Kim, Tae-Woo Kim, Eun Ji Lee

“…The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). …”
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Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach by Michael Gyaabeng, Ramadan Ahmed, Nayem Ahmed, Catalin Teodoriu, Deepak Devegowda

“…The chosen modeling techniques were k-nearest neighbors (KNN), random forest (RF), gradient boosting (GB), and decision tree regression (DT). …”
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Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Rea... by Taufiq Rashid, Di Tian

“…The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…Machine learning (ML) techniques have shown promise in addressing these diagnostic challenges due to their higher efficiency and reduced error rates in analyzing complex, high-dimensional datasets, particularly those derived from speech signals. …”
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Validation of the NISAR Multi-Scale Soil Moisture Retrieval Algorithm across Various Spatial Resolutions and Landcovers Using the ALOS-2 SAR Data by Preet Lal, Gurjeet Singh, Narendra N. Das, Rowena B. Lohman

“…This study investigates the performance of soil moisture retrieval across 5 diverse test sites, covering forest, shrubland, cropland, and grassland environments, as well as hydrometeorological conditions ranging from arid to polar. …”
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