A Collaborative Navigation Model Based on Multi-Sensor Fusion of Beidou and Binocular Vision for Complex Environments by Yongxiang Yang, Zhilong Yu

“…Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. …”
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Vehicles detection through wireless sensors networks and optical fiber sensors by Hacen Khlaifi, Amira Zrelli, Tahar Ezzedine

“…We verify that the wavelength-based pressure detection error rate is small, approximately 0.05 kg/cm2. The wheelbase distance estimate had an error rate of around 0.012 m, while the weight calculation from the pressure had an error rate of about 12 kg. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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Parking lot occupancy prediction using long short-term memory and statistical methods by Ercan Avşar, Yusuf Can Anar, Abdurrahman Özgür Polat

“…The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. …”
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Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems by Xinyue Xu, Julian Wang

“…Model accuracy is assessed using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAE), and Coefficient of Variation of Root Mean Square Error (CVRMSE), while UQ is evaluated through 95% Credible Intervals (CIs), Mean Prediction Interval Width (MPIW), the Quality of Confidence Intervals (QCI), and Coverage Width-based Criterion (CWC). …”
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Predicting Nonlinear Behavior of Cellular Cross-Laminated Timber Under Bending and Rolling Shear by Suman Pradhan, Mostafa Mohammadabadi

“…When the entire CCLT was modeled using shell elements, the error increased to 9%. For the short-span bending, the error remained at 8% regardless of element type. …”
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Prediction of Global Horizontal Irradiance for Composite Climatic Zone in India by Naveen Krishnan, K. Ravi Kumar

“… Prediction of Global Horizontal Irradiance (GHI) is integral for solar energy applications. The current study focused on the prediction of GHI for 14 days ahead in New Delhi, India, by Weather Research Forecasting Solar (WRF-Solar). …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The relative flood peak error, mean relative error, root mean square error, and Nash-Sutcliffe efficiency coefficient of the model’s one-day-ahead prediction are 7.987%, 22.421%, 632.871 m3/s, and 0.771, respectively. …”
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A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang, Patrick Wong

“…The experimental results demonstrate that EOLSO significantly outperforms the SO, achieving reductions of 43.83% in the Sum of Squares Error (SSE), 30.73% in the Mean Absolute Error (MAE), and 25.05% in the Root Mean Square Error (RMSE). …”
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A cyclic redundancy check aided encoding construction method for list sphere polar decoder by Wenbin Hu, Haiqiang Chen, Rui Wang, Qinhua Guo, Shuping Dang, Youming Sun, Xiangcheng Li

“…Abstract Polar codes are the only error-correcting codes that have been mathematically proven to achieve the Shannon limit to date, playing a crucial role in the control channels of 5G mobile communication systems. …”
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Prediction of Global Ionospheric TEC Based on Deep Learning by Zhou Chen, Wenti Liao, Haimeng Li, Jinsong Wang, Xiaohua Deng, Sheng Hong

“…Abstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. …”
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Industrial Design of Yarn Speed Monitoring System in Positive Feed Circular Knitting Machine by Mohammad Ehsan Momeni Heravi

“…Additionally, in the case of any defects, the equalisation is done by the operator using the trial-and-error method, which consequently increases the risk of human error. …”
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A Cognitive Model for Generalization during Sequential Learning by Ashish Gupta, Lovekesh Vig, David C. Noelle

“…For comparison, we measure the generalization exhibited by the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks using a simulated robotic arm.…”
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PENGENALAN EKSPRESI RAUT WAJAH BERBASIS JARINGAN SARAF TIRUAN BACKPROPAGATION DENGAN METODE PRINCIPAL COMPONENT ANALYSIS by Harizahayu Harizahayu

“…The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. …”
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Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data by Tolga Bodrumlu, Fikret Çalışkan

“…In this comparison, the Root Mean Square Error metric was used. As a result of the comparison, it was observed that the error obtained from the designed algorithm is less than that of the Vicon system.…”
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Quick Models for Saccade Amplitude Prediction by Oleg V. Komogortsev, Young Sam Ryu, Do H. Koh

“…The amplitude accuracy results yielded approximately 5.26° prediction error, while the error for direction prediction was 5.3% for the first sample model and 1.5% for the two samples model. …”
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Real-Time Collision Warning System for Over-Height Ships at Bridges Based on Spatial Transformation by Siyang Gu, Jian Zhang

“…The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. …”
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Nowcasting Solar Irradiance Components Using a Vision Transformer and Multimodal Data from All-Sky Images and Meteorological Observations by Onon Bayasgalan, Atsushi Akisawa

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Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks by Mehmet Yüksel, Emre Ünsal

“…Among these, the BR algorithm demonstrated the best performance with an accuracy value of 0.99915, a Mean Absolute Error (MAE) of 2.34 × 10⁻³, and a Mean Squared Error (MSE) of 3.82 × 10⁻⁵, outperforming LM and SCG in in terms of generalization and accuracy. …”
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Supervised and unsupervised machine learning approaches for tree classification using multiwavelength airborne polarimetric LiDAR by Zhong Hu, Songxin Tan

“…The Decision-Tree approach shows a re-substitution error of 0.14 % and a k-fold loss error of 0.57 % for 2,106 tree samples; and the clustering methods provide accuracies at about 80 %. …”
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