Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais by Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha, Sandra Oda, Hostilio Xavier Ratton Neto

“…The models evaluated included linear regression, random forest, decision tree, and support vector machines. …”
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Optimizing agricultural yield: a predictive model for profitable crop harvesting based on market dynamics by Nilesh P. Sable, Nilesh P. Sable, Vinod Kumar Shukla, Parikshit N. Mahalle, Vijayshri Khedkar

“…After a thorough study using the Mean Squared Error (MSE) and R2 score, it was determined that the DT model performed the best, with an outstanding R2 score of 99%. …”
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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…The datasets, sourced from the UCI repository, were pre-processed and evaluated using metrics such as accuracy, precision, F1-score, log loss, and error rate, with performance further assessed using confusion matrices. …”
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A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. …”
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Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd by Xiao Guo, Hongyu Huang, Haiyan Wang, Chang Cai, Ying Wang, Xiaohua Wu, Jian Wang, Baogen Wang, Biao Zhu, Yun Xiang

“…Among them, ridge regression achieved the optimal performance, with determination coefficients (R²) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. …”
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Genetic structure analysis and core germplasm construction of Robinia pseudoacacia and its closely related species based on SNP by Haoran Wang, Yan Ma, Ruixue Wang, Dekui Zang, Xiaoyan Yu, Jingtao Li, Qichao Wu, Fengqi Zang

“…Abstract Robinia pseudoacacia is a forest biomass energy tree species with substantial potential for development and utilization. …”
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Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones by Ying Yao, Xiaohua Zhao, Chang Liu, Jian Rong, Yunlong Zhang, Zhenning Dong, Yuelong Su

“…All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. …”
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Operational Performance Assessment of PV-Powered Street Lighting: A Comparative Study of Different Machine Learning Prediction Models by Safwan Nadweh, Nabil Mohammed, Charalambos Konstantinou, Shehab Ahmed

“…The results indicate that DNNs and DBNs algorithms achieve the lowest error rate (2.5%) and highest accuracy (97%) with high-quality data. …”
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Evaluation of the biodiversity of arbuscular mycorrhizal fungi during regenerative succession in quarries by A. A. Kryukov, A. P. Yurkov, A. O. Gorbunova, T. R. Kudriashova, A. I. Gorenkova, Y. V. Kosulnikov, Y. V. Laktionov

“…Molecular genetic identification of fungi was carried out using Illumina MiSeq analysis of the ITS1 and ITS2 regions as barcodes for the identification of operational taxonomic units (OTUs) with species-level identification. An adapted and error-checked AMF genetic sequence database from NCBI was used as a reference. …”
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Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones by G. Qiao, Y. Cao, Q. Zhang, J. Sun, H. Yu, L. Bai

“…We propose a quality control procedure utilizing random forest machine learning models. By applying this quality control approach to the selected TCs, we discovered that the performance of the method for labeled data significantly surpassed that for unlabeled data developed in a previous study, reducing the mean absolute error from 3.105 to 0.904 <span class="inline-formula">hPa</span>. …”
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Prediction of retention time in larger antisense oligonucleotide datasets using machine learning by Manal Rahal, Bestoun S. Ahmed, Christoph A. Bauer, Johan Ulander, Jörgen Samuelsson

“…Through feature engineering and grid search optimization, key predictors were identified and compared for model accuracy using root mean square error, coefficient of determination R-squared, and run time. …”
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

“…The optimization algorithm was selected using root mean square error (RMSE) and the coefficient of determination (R²) as evaluation indices and further improved with a genetic algorithm and particle swarm optimization. …”
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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

“…However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. …”
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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 proposed setup demonstrated an enhanced industrial performance in a simulated environment through error reduction by 22.3 % along with a 31.1 % increase in task speed and a 27.8 % improvement in situational awareness recorded through NASA-TLX cognitive load evaluations. …”
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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

“…Classical methods for fault detection, including manual inspections and simple sensor-based systems, face significant challenges, such as high labour costs, human error, and limited detection accuracy under varying environmental conditions. …”
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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

“…Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. …”
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