Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations by Pavel V. Matrenin, Alina I. Stepanova

“…Hourly electricity consumption data for two years, schedules of planned repairs and equipment shutdowns, and meteorological data were utilized. …”
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Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants by Vipin Kumar, Ravi Prakash Tewari, Anubhav Rawat

“…With a performance evaluation of 0.06 mean absolute error (MAE), 0.17 Root Mean Square Error (RMSE), and 0.96 R2, the Random Forest Regression is found to be the best model. …”
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An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields by Yi-Fan Wang, Max Yue-Feng Wang, Li-Ying Tu

“…Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R² value of 0.5760. …”
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors by Chibuike Chiedozie Ibebuchi

“…This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. …”
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Equivariant spherical CNNs for accurate fiber orientation distribution estimation in neonatal diffusion MRI with reduced acquisition time by Haykel Snoussi, Davood Karimi

“…More importantly, it yields FODs and tractography that are quantitatively comparable and qualitatively highly similar to those from a reliable Hybrid-CSD ground truth, despite using only 30% of the full acquisition data. These findings highlight sCNNs' potential for accurate and clinically efficient dMRI analysis, paving the way for improved diagnostic capabilities and characterization of early brain development with shorter scan times.…”
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Regression machine learning-based highly efficient dual band MIMO antenna design for mm-Wave 5G application and gain prediction by Redwan A. Ananta, Md. Ashraful Haque, Geamel Alyami, Md Sharif Ahammed, Md. Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Md Afzalur Rahman, Hussein Shaman, Hanaa A. Abdallah, Abdelhamied A. Ateya

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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. …”
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Research on the prediction model of gas emission based on grey system theory by Liyang Bai, Hui Geng, Guangming Yu

“…In order to improve the prediction accuracy of gas emission volume.A prediction model of gas emission based on grey system theory is proposed.11 indexes such as gas content, coal seam depth, coal seam thickness, coal seam dip angle and inclined length of working face are selected as the influencing factors of gas emission.The weight of each factor is determined by grey correlation analysis. The related factors with a grey correlation degree greater than 0.7, from largest to smallest, are: coal seam gas content X1 > coal seam thickness X3 > mining intensity X11 > coal seam depth X2 > adjacent gas content X8.Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission.…”
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Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN by Chun Wang, Weihua Zhang, Cong Wu, Heng Hu, Wenjia Zhu

“…However, due to the randomness, nonlinearity, and correlation of DHW data, constructing DHW prediction models is difficult. …”
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Time series forecasting of infant mortality rate in India using Bayesian ARIMA models by Anuj Singh, Tripti Tripathi, Rakesh Ranjan, Abhay K. Tiwari

“…Kalman forecast has been performed for infant mortality growth rate data to attain the prospective predictions. Finally, a numerical illustration has been provided for the annual IMR growth rate data of India from 1950-2023. …”
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Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device by Egas Jose Armando, Damien Hanyurwimfura, Omar Gatera, Kwang Soo Kim, Athanase Nduwumuremyi

“…Data analysis was performed using Python 3.12 on ARM v8. …”
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Demographic Assessment of Laser Therapies in 0–18-year-olds: A Retrospective Study by Priyanka, Nancy P Saharia, Shivani Mathur

“…It is one of the most innovative tools, widely being used nowadays in every aspect of dentistry, which increases the efficiency and specificity of dental treatment. Aim: Retrospective analysis of the demographic data for laser therapies in 0–18-year-olds for 5 years. …”
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Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm by Osvaldo Pérez, Brian Diers, Nicolas Martin

“…Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (<i>R</i>² = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). …”
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Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study by Jane Burns, Hannah O’Driscoll, Eamon Loughman

“…Fréchet distance analysis, Mean Square Error and Mean Absolute Error display the level of agreement between features and radiologist following the rescale of the data. …”
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Improving the Estimates of County-Level Forest Attributes Using GEDI and Landsat-Derived Auxiliary Information in Fay–Herriot Models by Okikiola M. Alegbeleye, Krishna P. Poudel, Curtis VanderSchaaf, Yun Yang

“…National-scale forest inventories such as the Forest Inventory and Analysis (FIA) program in the United States are designed to provide data and estimates that meet target precision at the national and state levels. …”
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Prediction of Claim Fund Reserves in Insurance Companies Using the ARIMA Method by Goenawan Brotosaputro, Yohanes Setiawan Japriadi, Wiwin Windihastuty, Rivai Ahsani

“…We use claim value data that has been scaled in millions. 2020 to 2022 as training data and 2023 as test data. …”
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Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid by Mohammad Mahdi Tafarroj, Seyed Soheil Mousavi Ajarostaghi, C.J. Ho, Wei-Mon Yan

“…To apply the artificial neural network analysis, 60 data of experimental works are utilized. …”
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A Fourier Fitting Method for Floating Vehicle Trajectory Lines by Yun Shuai, Pengcheng Liu, Hao Han

“…With the advancement of spatial positioning technology, trajectory data have been growing rapidly. Trajectory data record the spatiotemporal information and behavioral characteristics of moving objects, and in-depth analysis can provide decision support for urban transportation. …”
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Bearing Lifespan Reliability Prediction Method Based on Multiscale Feature Extraction and Dual Attention Mechanism by Xudong Luo, Minghui Wang

“…By employing path weight selection methods, Discrete Fourier transform, and selection mechanisms, the prediction accuracy and generalization ability in complex time series analysis were significantly improved. Evaluation results based on mean absolute error (MAE) and root mean square error (RMSE) indicated that the dual attention mechanism effectively focused on key features, optimized feature extraction, and improved prediction performance. …”
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Recursive State and Random Fault Estimation for Linear Discrete Systems under Dynamic Event-Based Mechanism and Missing Measurements by Xuegang Tian, Shaoying Wang

“…Moreover, the rigorous mathematical analysis is carried out for the exponential boundedness of the estimation error. …”
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