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. …”
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Comparison of the Effectiveness of Dialectical Behavior Therapy and Cognitive-Behavioral Therapy on the level of Attention in Girl students with Obsessive-Compulsive Disorder by Haleh Amighi, Hossein Ebrahimi Moghadam, Leila Kashani Vahid

“…The CBT and DBT (8 sessions of 90 minutes each) were administered to experimental groups 1 and 2, respectively. The analysis of the collected data was conducted using SPSS22, employing the method of multivariate mixed analysis of variance.Findings: The results showed that both methods CBT and DBT improved subscales of omission error (MD=-8.112, -8.252), commission error (MD=-1.269, -2.6), reaction time (MD=-17.227, -11.256) in girls with OCD when compared to the control group (P<0.05). …”
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Research on TOC Log Evaluation Method Based on PCA-BP Neural Network by SHANG Yazhou, XU Duonian, ZHANG Zhaohui, LIU Jianyu, ZHAO Wenwen

“…Then, the principal component analysis (PCA) technology is used to decollinearity and reduce the dimension of the original data set, and two principal components are determined. …”
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Long Short-Term Memory Networks and Bayesian Optimization for Predicting the Time-Weighted Average Pressure of Shield Supporting Cycles by Wanzi Yan, Junhui Wang, Jingyi Cheng, Zhijun Wan, Keke Xing, Kuidong Gao

“…Despite the leap-forward development of underground data collection and transmission, mining and regional correlation analysis of massive shield data remains challenging. …”
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Predicting the Young’s Modulus of Concrete Using a Particle-Based Movable Cellular Automata Method by Dorota Aniszewska, Marek Rybaczuk

“…Results showed high agreement between simulations and experimental data, confirmed by a high coefficient of determination R² of 0.84 and mean squared error of 2.43 GPa². …”
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Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method by Dawei Zhao, Yujie Ning, Chuanzhi Zhang, Jin Ma, Minhui Qian, Yanzhang Liu

“…The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…This configuration adeptly extracts both spatial and temporal features, yielding a 15% reduction in prediction error across various datasets. Additionally, by representing the multivariate time series data as a graph in which variables are nodes connected by edges denoting temporal relationships, TGAMTSA leverages Graph Neural Networks (GNNs) to decode complex inter-variable dependencies, resulting in a 20% improvement in prediction accuracy over traditional methods. …”
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Influences of intelligent predictive networks on thermal efficiency of radiative magnetohydrodynamics hybrid nanofluid flow considering heat absorption-generation aspects by Muhammad Habib Ullah Khan, Waqar Azeem Khan, Taseer Muhammad, Marei S. Alqarni, Hamid Qureshi, Iftikhar Hussain

“…By using artificial neural network on HNF, the following is the graphical outputs of MSE, error histogram, STD, regressions analysis, FSF, solution graph and error analysis outputs. …”
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Method of vibration diagnostics of the technical condition of electronic equipment structures by S. M. Lyshov, S. U. Uvaysov, V. V. Chernoverskaya, Ph.L. Q. Han

“…The transition from the analysis of the frequency response to the resonant frequencies that characterize the physical and mechanical parameters of the structure is due to greater stability and lower measurement error of this characteristic. …”
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Tree volume modeling of different poplar clones in plantations in the Vojvodina region by Pantić Damjan, Borota Dragan, Janjatović Živan

“…Based on preliminary research, the samples were structured in such a way that their implementation would provide data for the development of models for calculating volume (and indirectly volume increment) within acceptable margins of error. …”
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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. …”
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Random Forest-Based Retrieval of XCO₂ Concentration from Satellite-Borne Shortwave Infrared Hyperspectral by Wenhao Zhang, Zhengyong Wang, Tong Li, Bo Li, Yao Li, Zhihua Han

“…This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. …”
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A photogrammetric approach to the estimation of distance to animals in camera trap images by B. Mirka, C.D. Lippitt, G.M. Harris, R. Converse, M. Gurule, S.E. Sesnie, M.J. Butler, D.R. Stewart, Z. Rossman

“…This process has typically been done through manual measurements or using reference points, making fieldwork and analysis labor intensive and error prone. Using Structure from Motion (SfM) photogrammetry and geotagged imagery captured from smartphones offers a simple and cost-effective solution to distance estimation from camera traps. …”
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Efficient Behavior Factor Estimation in Moment-Resisting Reinforced Concrete Frames through Gene Expression Programming by Niloufar Azhdari, Seyed Shaker Hashemi, Saeid Javidi, Abdorreza Fazeli

“…The chosen model demonstrated values of 0.0061, 0.049, and 0.0037 for root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. …”
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Validated low-cost standardized VICON configuration as a practical approach to estimating the minimal accuracy of a specific setup by Adam Chromy, Petr Sopak, Hynek Cigler

“…However, it is often hard to perform since it requires significant effort, time, knowledge of statistical data analysis and often equipment and tools that are not commonly available. …”
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Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Maziani Sabudin

“…The analysis of the results showed that BCOA-E selects a fewer number of features and performed better in terms of error rate. …”
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GRU-LSTM model based on the SSA for short-term traffic flow prediction by Changxi Ma, Xiaoyu Huang, Yongpeng Zhao, Tao Wang, Bo Du

“…This model integrates the predictive efficiency of the GRU, LSTM’s capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. …”
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A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics by Qian Chen, Shengyao Jia, Jianyuan Qin, Yong Du, Zongshan Zhao

“…The partial least squares regression models have high correlation coefficient (>0.99), low root-mean-square error of calibration (<1.5%), low root-mean-square error of cross-validation (<2.4%), and low root-mean-square error of prediction (<2.3%), indicating good quality of prediction for pesticides concentration. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…The experimental results show that the proposed model achieves superior performance in one-step-ahead AQI prediction, with a Root Mean Squared Error (RMSE) of 3.445, a Mean Absolute Percentage Error (MAPE) of 4.737%, a Coefficient of Determination (R2) of 0.993, a Mean Absolute Error (MAE) of 2.263, a Percentage Bias (PBIAS) of −0.511%, and a Willmott Index of Agreement (WI) of 0.998, outperforming other baseline models.…”
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Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm by Lilin Li, Qing He, Hua Wang, Wensheng Wang

“…The DIWPSO-SVM model achieved an average prediction error of 1.43 mm and a root-mean-square error (RMSE) of 2.05, significantly outperforming the original SVM model, which had an average error of 5.29 mm and an RMSE of 5.62. …”
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