Validity and contributions to pain from the central aspects of pain questionnaire in rheumatoid arthritis by Stephanie Louise Smith, Vasileios Georgopoulos, Onosi Sylvia Ifesemen, Richard James, Eamonn Ferguson, Richard J. Wakefield, Deborah Wilson, Philip Buckley, Dorothy Platts, Susan Ledbury, Ernest Choy, Tim Pickles, Zoe Rutter-Locher, Bruce Kirkham, David Andrew Walsh, Daniel F. McWilliams

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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We used multiple imputations to address high rates of missing data, selecting the optimal technique based on out‐of‐sample error. We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. …”
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Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li, Jiusheng Li

“…The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (<i>NRMSE</i>) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…This model improves coefficient of determination (R2) and ratio of performance to interquartile distance (RPIQ) by 0.09 and 0.28, respectively, and reduces root mean square error (RMSE) by 0.52 g kg−1 compared to Model (ii). …”
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Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT by Sinan Andan Diwan

“…The results demonstrate improved precision and accuracy of the proposed RF model, evidenced by significantly lower Mean Square Error (MSE) and Root Mean Square Error (RMSE) as compared to the DNN. …”
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Reforming Real Estate Valuation for Financial Auditors With AI: An In-Depth Exploration of Current Methods and Future Directions by Silviu-Ionut BABTAN

“…Underscoring that AI is meant to support, not substitute, human assessors, the paper presents how these methods can enhance valuation processes, deliver more reliable valuation reports, and decrease errors, while also exploring future innovations and evolving trends in artificial intelligence for real estate industry and related professions.…”
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Research on emulsion concentration detection technology based on interpretable machine learning methods by Jiaxu Kang, Jianwei Li, Meng Wang, Xinwei Guo, Dinghao Liu, Chao Qu, Chengbin Guo

“…Field validation through 20 emulsion preparation trials revealed an average relative error of 4.02 %, demonstrating the system’s reliability and precision for industrial applications.…”
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An An accurate molecular method to sex elephants using PCR amplification of Amelogenin gene by George Lohay

“…This discrepancy observed was due to observational errors in the field, where high grass  reduces the ability to accurately sex young individuals. …”
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Machine Learning Strategies for Forecasting Mannosylerythritol Lipid Production Through Fermentation: A Proof-of-Concept by Carolina A. Vares, Sofia P. Agostinho, Ana L. N. Fred, Nuno T. Faria, Carlos A. V. Rodrigues

“…Three ML models, neural networks (NNs), support vector machines (SVMs), and random forests (RFs), were used. An NN provided predictions with a mean squared error (MSE) of 0.69 for day 4 and 1.63 for day 7 and a mean absolute error (MAE) of 0.58 g/L and 1.1 g/L, respectively. …”
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Assessment of pan coefficient performance: A comparative study of empirical and model-driven approaches using a hill-climbing-based alternating model tree and MOORA by Saad Javed Cheema, Aitazaz A. Farooque, Mehdi Jamei, Khabat Khasravi, Farhat Abbas, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal

“…Statistical indicators, including correlation coefficient (R), root mean square error (RMSE), Kling–Gupta efficiency (KGE), and Vulnerability, evaluated the model output. …”
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Advancing Smart Energy: A Review for Algorithms Enhancing Power Grid Reliability and Efficiency Through Advanced Quality of Energy Services by José M. Liceaga-Ortiz-De-La-Peña, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Ricardo A. Barrera-Cámara, Daniel Robles-Camarillo, Marco A. Márquez-Vera, Francisco R. Trejo-Macotela, Luis A. Ortiz-Suárez

“…The proposed stacking ensemble achieved a forecasting accuracy of 99.06%, with a Mean Absolute Percentage Error (MAPE) of 0.9364% and a Coefficient of Determination (R²) of 0.998345, highlighting its superior performance compared to each individual base model.…”
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Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal by Pragati Tripathi, M. A. Ansari, Tapan Kumar Gandhi, Rajat Mehrotra, Md Belal Bin Heyat, Faijan Akhtar, Chiagoziem C. Ukwuoma, Abdullah Y. Muaad, Yasser M. Kadah, Mugahed A. Al-Antari, Jian Ping Li

“…The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…However, traditional screening methods are often time-consuming, labor-intensive, and prone to human error. Recent advancements in Unmanned aerial vehicle (UAV) and sensor technologies have enabled high-throughput screening of salt-tolerant crops, offering a more efficient alternative. …”
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AI-Driven Insect Detection, Real-Time Monitoring, and Population Forecasting in Greenhouses by Dimitrios Kapetas, Panagiotis Christakakis, Sofia Faliagka, Nikolaos Katsoulas, Eleftheria Maria Pechlivani

“…The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. …”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attribute-selected classifier (ASC) with the random forest (RF) algorithm, resulting in hybrid models (RFC-RF, RBD-RF, and ASC-RF). …”
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Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion by Dagan Avioz, Raphael Linker, Eran Raveh, Shahar Baram, Tarin Paz-Kagan

“…Traditional methods, such as frequent leaf and soil sampling followed by laboratory analysis, are costly, labor-intensive, and prone to human error. Remote sensing (RS) technologies, including unmanned aerial vehicles (UAVs) and satellite platforms, offer scalable and precise alternatives for N management. …”
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Modeling the compressive strength behavior of concrete reinforced with basalt fiber by Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, Viroon Kamchoom, Paul Awoyera, Siva Avudaiappan

“…The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. …”
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A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 by W. Li, W. Li, Z. Yao, Z. Yao, Y. Qu, Y. Qu, H. Yang, Y. Song, L. Song, L. Wu, Y. Cui, Y. Cui

“…After analysis, the framework using the novel bias-corrected RF algorithm achieves excellent performance in both hourly gap-filling and daily prolongation, with mean root mean square error values of 33.86 and 16.58 <span class="inline-formula">W m⁻²</span>, respectively. …”
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Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations by Runze Zhang, Adam Watts, Mohamad Alipour

“…The results demonstrated that the incorporation of dynamic <italic>h</italic> and &#x03C9; parameters, derived on a daily scale, markedly enhanced the soil moisture retrieval performance with an average unbiased root-mean-square error (ubRMSE) of 0.01 (0.02) m³&#x002F;m³ and Pearson correlation (<italic>R</italic>) of 0.95 (0.90) for the SCA (RDCA) algorithms, indicating that dynamic parameterization holds significant promise for improving retrieval accuracy. …”
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