Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short‐Term Load Forecasting in Smart Grid by Syed Muhammad Hasanat, Muhammad Haris, Kaleem Ullah, Syed Zarak Shah, Usama Abid, Zahid Ullah

“…The proposed model delivered single‐step forecasting results of 123.91 Root Mean Square Error (RMSE), 92.8 Mean Absolute Error (MAE), and 0.63 Mean Absolute Percentage Error (MAPE) on the AEP dataset and 126.16 RMSE, 64.28 MAE, and 0.44 MAPE on the ISONE dataset. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“…The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. …”
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Polarization Division Multiplexing-Based Hybrid Microwave Photonic Links for Simultaneous mmW and Sub-6 GHz Wireless Transmissions by Dong-Nhat Nguyen, Jan Bohata, Jan Spacil, Matej Komanec, Nobby Stevens, Zabih Ghassemlooy, Pham Tien Dat, Stanislav Zvanovec

“…Extensive simulation analysis is first presented to examine (i) the impact of CD on mmW (25 GHz) and sub-6 GHz (2.6 GHz) signals, envisioned for the 5th generation networks, and (ii) optimization of CD-induced power fading by changing the phase relations between the optical carrier and optical sidebands in each polarization channel using single tunable polarization controller. …”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…Among these, XGBoost demonstrated the highest overall accuracy, achieving an R² value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. …”
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A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach by Haiying Wang, Yang Ao, Chenguang Wang, Yingzhi Zhang, Xiaofeng Zhang

“…Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. …”
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A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine by X. Lin, W. Yao, T. Yan

“…This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. …”
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Rainfall Prediction in Khorasan Razavi Stations Using a Hybrid Neural Network and Genetic Algorithm Approach by Mahdi Naseri, Mahsa Mardani

“…A convergence analysis of the GA was also provided, as well as histograms of the error distributions, which further validated the superior performance of the proposed NARXGA model. …”
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RESEARCH ON THE GEAR OF SHEARER’S CUTTING PART BASED ON MFC-ANNS by ZHAO LiJuan, LI MingHao

“…Combining MFC with ANNs could predict shearer ’s kinematics parameters when the gears was reliable operation,the prediction error reach 5. 6538 × 10^{- 6},the research results provides a unequivocal quantitative basis for the design and optimization of gear type parts which could improve this type of parts’ functional reliability effectively,this method has instruction significance to practical shearer’s productions.…”
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Research on Health Monitoring of Flying-Swallow-Typed Tied Arch Bridge Based on PSO-GRNN Algorithm by Tianpeng Zhang, Long Liu, Pengfei Ji

“…The mean absolute percent error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE) coefficient were used to evaluate the prediction performance of the model. …”
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Crop yield and water productivity modeling using nonlinear growth functions by Iman Hajirad, Khaled Ahmadaali, Abdolmajid Liaghat

“…Abstract Growth curve modeling plays a crucial role in precision agriculture by enabling rapid analysis of plant growth dynamics. Understanding the complex mechanisms of crop growth is essential for optimizing agricultural productivity. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. …”
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Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm by Yuntong Li

“…To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accuracy. The genetic algorithm is applied to optimize the traditional grey wolf algorithm. …”
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Application of improved clustering algorithm in mixed teaching of modern educational technology by Lei Shu, Guirong Li

“…With advances in algorithm optimization and computational capabilities, cluster analysis holds broad potential for educational applications. …”
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Design and Testing of Electric Drive System for Maize Precision Seeder by Lin Ling, Yuejin Xiao, Xinguang Huang, Guangwei Wu, Liwei Li, Bingxin Yan, Duanyang Geng

“…The analysis of motor rotational speed accuracy showed that the relative error of motor rotational speed above 410 rpm did not exceed 2.24%, and the relative error had less influence on the seeding quality. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…Each preprocessing technique addresses specific issues within the spectral data, such as correcting for light scattering, baseline shifts and other interferences, thereby optimizing the data for further analysis. To select the optimal hyperspectral wavelengths for predicting kiwifruit quality, Genetic Algorithm (GA) and Random Frog (RF) methods were employed. …”
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Event-Triggered Adaptive Dynamic Programming Consensus Tracking Control for Discrete-Time Multiagent Systems by Yuyang Zhao, Xiaolin Dai, Dawei Gong, Xinzhi Lv, Yang Liu

“…Third, the stability analysis of the MASs under the event-triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. …”
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Measuring the Effective Factors in the Non-Development of the Sustainable Smart City of Tabriz by Ali Zeynali Azim, Mehri Fadaei Haghi, Amin Alizadeh, Mohammad Jodeiri Abbasi, Reza Fathipour, Mohammadali Sharifi

“…Also, the goodness-of-fit index (GFI) was 0.946, which indicated the acceptability of this amount for the optimal fit of the model. The value of the root mean square error of estimation (RMSEA) was 0.057, which is acceptable considering that it is smaller than 0.08, and it indicates the confirmation of the research model. …”
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Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat by Yuanyuan Pan, Jingyu Li, Jiayi Zhang, Jiaoyang He, Zhihao Zhang, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

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大功率循环泵行星齿轮箱耦合动态特性分析 by 杨长辉, 吴灿元, 谭勇虎, 许洪斌

“…A coupled dynamics model containing gears-shafts-bearings-housing of a high-power circulating pump planetary gearbox is built comprehensively in consideration of the time-varying mesh stiffness and gear error. The coupled modal analysis is performed for this finite element model of the gearbox.The dynamic response is solved by using mode superposition method.Then the time-frequency history of node displacement and speed of the gearbox are obtained.All results provided a theoretical foundation for dynamic characteristic optimization of a high-power circulating pump planetary gearbox transmission system.…”
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