Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model by Bingbo Cui, Xinyu Cui, Xinhua Wei, Yongyun Zhu, Zhen Ma, Yan Zhao, Yufei Liu

“…Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. …”
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Autoencoder-Driven Fiducial Landmark Identification in 3D Brain MRI for Neuroimaging Alignment by G. Deepali, H. Anitha, B. P. Swathi, M. V. Suhas

“…However, manual annotation of these landmarks is time-consuming, prone to human error, and further complicated by variations in MRI acquisition protocols and incomplete head coverage. …”
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A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models by Athanasios Donas, George Galanis, Ioannis Pytharoulis, Ioannis Th. Famelis

“…The derived results demonstrate a significant reduction in systematic error, as the bias decreased by up to 88% for 10-meter wind speed and 58% for 2-meter air temperature. …”
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Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues by Lamyae El Youbi El Idrissi, Ismail Akharraz, Aziza El Ouaazizi, Abdelaziz Ahaitouf

“…Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.…”
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The Association of Peroxisome Proliferator-Activated Receptor δ and Additional Gene-Gene Interaction with C-Reactive Protein in Chinese Population by Xiao-Ying Ding, Hao-Zheng Yuan, Ru Gu, Yan-Feng Gao, Xiao-Gang Liu, Ya Gao

“…Generalized multifactor dimensionality reduction (GMDR) was employed to analyze the interaction. …”
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Enabling efficient sizing of hybrid power plants: a surrogate-based approach to energy management system modeling by C. Assaad, J. P. M. Leon, J. Quick, T. Göçmen, S. Ghazouani, K. Das

“…This surrogate achieves a normalized root mean square error of 0.81 % in approximating yearly revenues. …”
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AI-Powered Forecasting of Environmental Impacts and Construction Costs to Enhance Project Management in Highway Projects by Joon-Soo Kim

“…The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. …”
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An Investigation of Interpolation Techniques to Generate 2D Intensity Image From LIDAR Data by Imran Ashraf, Soojung Hur, Yongwan Park

“…Moreover, 3-D to 2-D data transformation also involves data reduction, which further deteriorates the quality of images. …”
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AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

“…The AE-BPNN model demonstrated significant advantages over Gaussian Process Regression (GPR) and Support Vector Regression (SVR), yielding lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), alongside higher R² scores. …”
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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

“…Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (<i>p</i> < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations.…”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…Experimental results show that the HA-LSTM outperforms state-of-the-art baselines, including ARIMA, Prophet, and vanilla LSTM models, achieving a 15% improvement in Mean Absolute Percentage Error (MAPE) and a 12% reduction in Root Mean Square Error (RMSE). …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. …”
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Configurational Comparison of a Binary Logic Transmission Unit Applicable to Agricultural Tractor Hydro-Mechanical Continuously Variable Transmissions and Its Wet Clutch Optimizati... by Wenjie Li, Zhun Cheng, Mengchen Yang

“…Under light-load conditions, the optimized GRNN reduced total relative error by 39.6%, while under heavy-load conditions, it achieved a 61% reduction. …”
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Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms by Zahraa A. Shubber, Thamer M. Jamel, Ali.K. Nahar

“…Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. …”
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NSMO-Based Adaptive Finite-Time Command-Filtered Backstepping Speed Controller for New Energy Hybrid Ship PMSM Propulsion System by Dan Zhang, Suijun Xiao, Hongfen Bai, Diju Gao, Baonan Wang

“…The results of this study demonstrate a significant reduction in speed-tracking overshoot to zero, a substantial decrease in integral squared error by 90.15%, and a notable improvement in response time by 18.6%.…”
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Thermal features prediction in asphalt pavements using ANFIS-based regression by Mohammad Ali Khasawneh, Hiren Mewada, Ahmad Ali Khasawneh, Ansam Adnan Sawalha

“…Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). …”
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Self-Weighted Quantile Estimation for Drift Coefficients of Ornstein–Uhlenbeck Processes with Jumps and Its Application to Statistical Arbitrage by Yuping Song, Ruiqiu Chen, Chunchun Cai, Yuetong Zhang, Min Zhu

“…The estimation performance is evaluated using metrics such as mean, standard deviation, and mean squared error (MSE). The simulation results show that the self-weighted quantile estimator proposed in this paper performs well across different metrics, such as 8.21% and 8.15% reduction of MSE at the 0.9 quantile for drift parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula> compared with the traditional quantile estimator. …”
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Delivering Dual Polarization-Division-Multiplexing Millimeter-Wave Signals at W-Band by One Pair of Antennas by Yifan Chen, Jiangnan Xiao, Ze Dong

“…For simplification of network architecture, apart from being deployed as multiplexer&#x002F;de-multiplexer, two ortho-mode transducer devices are used to be a pair of panel antennas at both the transmitter and the receiver sides in the scheme of the short-haul wireless link employing PDM-16QAM signal. The bit error ratio (BER) is less than the new-generation forward-error-correction (eFEC) threshold of 2&#x00A0;&#x00D7;&#x00A0;10^&#x2212;2 with CMA algorithm for equalization.…”
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Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

“…The model was trained using cutting parameters as inputs and evaluated with performance metrics such as mean absolute error (<i>MAE</i>), mean squared error (<i>MSE</i>), and the coefficient of determination (<i>R</i>²). …”
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Rudraksh: A compact and lightweight post-quantum key-encapsulation mechanism by Suparna Kundu, Archisman Ghosh, Angshuman Karmakar, Shreyas Sen, Ingrid Verbauwhede

“…We have done a scrupulous and extensive analysis and evaluation of different design elements, such as polynomial size, field modulus structure, reduction algorithm, and secret and error distribution of an LWE-based KEM. …”
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