Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Extensive experiments conducted on the Taiwan and Hawaii datasets demonstrate that the proposed TCA method significantly outperforms traditional feature selection techniques and other advanced algorithms in terms of the mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R²). …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. …”
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An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction by Nurmemet Yolwas, Yongchao Li, Lixu Sun, Jian Peng, Zhiwu Sun, Yajie Wei, Yineng Cai

“…Abstract Currently, attention models based on the Conformer architecture have become mainstream in the field of speech recognition due to their integration of self-attention mechanisms and convolutional networks. However, further research indicates that Conformers still exhibit limitations in capturing global information. …”
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Calibration of Integrated Low-Cost Environmental Sensors for Urban Air Temperature Based on Machine Learning by Fang Nan, Chao Zeng, Huanfeng Shen, Liupeng Lin

“…Calibration using this approach markedly improved the sensor data quality, with the R-squared (R²) value of the sensor with the poorest raw data increasing from 0.416 to 0.957, its mean absolute error (MAE) decreasing from 6.255 to 1.680, and its root mean square error (RMSE) being reduced from 7.881 to 2.148. …”
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Three-Dimensional Modelling and Validation for the Ultra-High-Speed EDS Rocket Sled with PM Halbach Array by Yongpan Hu, Baojun Chen, Guobin Lin, Zhiqiang Wang

“…Yet, 2D modelling in our earlier research ignored the magnetic field variation along both widths of the Halbach array and conductor plate. …”
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A Vision-Based Method for Detecting the Position of Stacked Goods in Automated Storage and Retrieval Systems by Chuanjun Chen, Junjie Liu, Haonan Yin, Biqing Huang

“…This research provides a reliable solution for automated cargo stack monitoring in modern logistics systems.…”
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Thermal Modeling and Analysis of a HPMSM Coupling With Magnetic Bearings by Liu Bin, Yu Zhongjun, Fu Jia

“…The range of absolute temperature error between LPTN model and CFD model is 0.2°C~2.2°C in steady state, and the relative temperature error is less than 4.2% in transient state. …”
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Comparison of different numerical methods for calculating stress intensity factors in analysis of fractured structures by H. Rabbani-Zadeh, T. Amiri, S.R. Sabbagh-Yazdi

“…In this research, an efficient Galerkin Finite Volume Method (GFVM) along with the h–refinement adaptive process and post–processing error estimation analysis is presented for fracture analysis. …”
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Incremental Crowd-Source Data Fusion and Map Update Method Based on Driving Data for Traffic Signs by H. Hu, H. Wu, S. Huang, W. Huang, C. Liu

“…The experiments in Shanghai show that the matching method can meet the matching requirements of crowd-source updating; the accuracy of the traffic sign positions after position optimization and crowd-source fusion is obviously improved, with an average plane error of 3.69 m and a standard deviation of error of 3.29 m, which can provide data support for crowd-source updating of the HD map.…”
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Health condition prediction method of the computer numerical control machine tool parts by ensembling digital twins and improved LSTM networks by Chen Guo, Yin Haifang

“…The experimental findings of the model demonstrated that the maximum error between the predicted and true value of the convolution-long short-term memory (LSTM) model in the machine tool part health monitoring data was 0.06, and the minimum error was 0.035. …”
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Topology identification and parameters estimation of LV distribution networks using open GIS data by Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara

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Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors by Aeshah H. Alamri, N. Alhazmi

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Monthly Runoff Forecasting by Non-Generalizing Machine Learning Model and Feature Space Transformation (Vakhsh River Case Study) by Matrenin P.V., Safaraliev M.K., Kiryanova N.G., Sultonov S.M.

“…This task is especially urgent for countries without their own oil-fields and opportunity to invest in the creation of solar or wind power plants. The aim of the research is to decrease the mean absolute forecasting error of the long-term prediction for the Vakhsh River flow (Tajikistan) based on the long-term observations. …”
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Stress and strength analysis of aluminum alloy structures under the effect of thermal and mechanical force by HE Zhiquan, QIU Huihui, SUN Yuheng, GUO Yujie, WEI Xiaohui

“…The typical aluminum alloy structure widely used in slat structure is taken as the main research object, and the stress distribution and structural strength under the combined action of heat and force were studied by experiment and finite element method. …”
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Factors influencing parental role satisfaction among Korea fathers of young children in the COVID-19 endemic era by Eun Ju Choi, Sun Jung Park

“…The Durbin-Watson value was 2.23, which is close to the ideal value of 2, indicating no significant autocorrelation in the error terms. Residual analysis also confirmed that the model met the assumptions of linearity, normality of error terms and homoscedasticity. …”
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Machine learning modeling based on informer for predicting complex unsteady flow fields to reduce consumption of computational fluid dynamics simulation by Mingkun Fang, Fangfang Zhang, Di Zhu, Ruofu Xiao, Ran Tao

“…Accurately predicting the dynamic behaviour of complex flow fields has always been a major challenge in Computational Fluid Dynamics (CFD) research. This paper proposes an innovative approach based on the Informer model for efficient prediction of unsteady flow fields. …”
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Meteorological drought severity forecasting utilizing blended modelling by Aaditya Ahire, Nilima Zade, Umeed Mujawar, Dimple Mehta, Ketan Kotecha

“…Conventional methods lack the intricate time-space correlation in meteorological data. The research proposes an ensemble of Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Tabular Network (TabNet) for a higher accuracy in drought forecasting. …”
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Coordinated Control Strategy for Hybrid Black Start with LCC HVDC System and Auxiliary Synchronous Generator by Yong Li, Hongtao Wang, Huiting Yang

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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms by Guodong Wu, Diangang Hu, Yongrui Zhang, Guangqing Bao, Ting He

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A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…This research establishes a novel methodological framework for analyzing agricultural price forecasting while providing reliable technical support for market monitoring and policy regulation.…”
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