Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare by Gurdeep Singh

“…It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. …”
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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

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Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning by Changfan Zhang, Yuxuan Wang, Jing He

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3D-GloBFP: the first global three-dimensional building footprint dataset by Y. Che, X. Li, X. Liu, X. Liu, Y. Wang, W. Liao, X. Zheng, X. Zhang, X. Xu, Q. Shi, J. Zhu, H. Zhang, H. Zhang, H. Yuan, H. Yuan, Y. Dai, Y. Dai

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A new-generation internal tide model based on 30 years of satellite sea surface height measurements: multiwave decomposition and isolated beams by Z. Zhao, Z. Zhao

“…The model contains 12 internal tide constituents: eight mode-1 constituents (<span class="inline-formula"><i>M</i>₂</span>, <span class="inline-formula"><i>S</i>₂</span>, <span class="inline-formula"><i>N</i>₂</span>, <span class="inline-formula"><i>K</i>₂</span>, <span class="inline-formula"><i>K</i>₁</span>, <span class="inline-formula"><i>O</i>₁</span>, <span class="inline-formula"><i>P</i>₁</span>, and <span class="inline-formula"><i>Q</i>₁</span>) and four mode-2 constituents (<span class="inline-formula"><i>M</i>₂</span>, <span class="inline-formula"><i>S</i>₂</span>, <span class="inline-formula"><i>K</i>₁</span>, and <span class="inline-formula"><i>O</i>₁</span>). Model errors are estimated to be lower than 1 mm in the SSH amplitude on global average, thanks to the long data record and improved mapping technique. …”
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Automating attendance management in human resources: A design science approach using computer vision and facial recognition by Bao-Thien Nguyen-Tat, Minh-Quoc Bui, Vuong M. Ngo

“…It enables efficient storage of attendance records and supports customizable report generation. This comprehensive data management capability ensures that attendance data is readily accessible for monitoring and analysis purposes, contributing to improved decision-making processes. …”
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Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting. by Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola

“…For DataCo smart SC for big data analysis dataset, the MiA + 1D-QNN model achieved the correlation of 0.957, Mean Square Error (MSE) of 6.00, Mean Absolute Error of 1.62, and Root Mean Square Error (RMSE) of 2.45.…”
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Quantitative estimate of several sources of uncertainty in drone-based methane emission measurements by T. H. Mohammadloo, M. Jones, B. van de Kerkhof, K. Dawson, B. J. Smith, S. Conley, A. Corbett, A. Corbett, R. IJzermans

“…In this paper we present a systematic error analysis of physical phenomena affecting the error in the mass balance method for parameters related to the acquisition of methane concentration data and to postprocessing. …”
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ASCAT Wind Superobbing Based on Feature Box by Boheng Duan, Weimin Zhang, Haijin Dai

“…Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. …”
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Use of Machine Learning to Predict California Bearing Ratio of Soils by Semachew Molla Kassa, Betelhem Zewdu Wubineh

“…AASHTO M 145 was used to categorize 252 soil samples that formed the basis of an experimental data set. In this model study, the data were split into 20% test data and 80% training data. …”
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Precision Improvement for the Detection of TGC via RBF Network by Yue Yan

“…Furthermore, the computer simulation and error analysis can be implemented by taking actual SO2 data emitted by one medium-sized coal-fired power plant in China as a training sample. …”
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Video-based pupillometry using Fourier Mellin image correlation by Brett A. Meyers, Pavlos P. Vlachos

“…Our analysis revealed that frame rate had the most significant impact on CV error (bias error: 0.65%, random error: 23.81%), while added noise had the most significant effect on CR error (bias error: 4.83%, random error: 6.38%). …”
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Addressing the challenges of estimating the target population in calculation of routine infant immunization coverage in Kenya. by Christine Karanja-Chege, Ambrose Agweyu, Fred Were, Michael Boele van Hensbroek, William Ogallo

“…A sub-analysis for the period 2020-2023 included ANC1 data. …”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. …”
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A comparative study of interpolation methods for the development of ore distribution maps by Mahinaz M. Shawky

“…The output methods are ranked to perform a comprehensive analysis using the error statistics methods: Mean Error (ME), Root Mean Square Error (RMSE), Mean Standardized Error (MSE) and Root Mean Square Standardized Error (RMSSE). …”
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Interval combined prediction of mine tunnel's air volume considering multiple influencing factors. by Zhen Wang, Erkan Topal, Liangshan Shao, Chen Yang

“…Experimental analysis using data from a coal mine in Inner Mongolia showed that the method could reduce Combined Weighted Mean Absolute Error(CWMAE) to a maximum of 5.0384, Combined Weighted Root of Mean Squares Error(CWRMSE) to 6.8889, and Combined Weighted Mean Absolute Percentage Error(CWMAPE) to 1.4756, which indicates that the method proposed in this study can effectively improve the prediction accuracy of the mine tunnel air volume.…”
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正交直齿面齿轮接触斑点及传动误差的研究 by 汪中厚, 牛波, 张扬扬, 张郑恺

“…The face gear transmission error in different conditions is studied by theoretical method and through simulation analysis. …”
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Research on Calibration Method of Laser Camera Sensor by LIU Shiwang, HU Yunqing, LIN Jun

“…For 800 sets of calibration data, the nonlinear least square method, Gauss-Newton iteration method and the L-M algorithm based on maximum likelihood estimation were used to solve the calibration model parameters; the other 1 000 sets of calibration data were taken for error analysis. …”
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Energy Prediction Method for Metro HVAC Systems based on the ARMA Model by Huang Ronggeng, Long Jing, Pan Zhigang, Chen Huanxin, Liu Jiangyan, Liu Jiahui, Li Zhengfei

“…This paper proposes an energy consumption-prediction method for metro heating, ventilation and air-conditioning (HVAC) systems based on an auto-regressive moving average (ARMA) model using a time-series data analysis. Firstly, stationarity analysis and white-noise analysis (also known as pure stochastic analysis) were carried out on the collected energy-consumption data from actual metro HVAC systems. …”
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Artificial Neural Network [ANN] modeling for tetracycline adsorption on rice husk using continuous system by Husham AbdMunaf Atta

“…The dynamic TC adsorption process was modeled using an artificial neural network (ANN). Various error analysis techniques, such as the correlation coefficient, mean square error (MSE), and error histogram (R2) for the training, testing, and validation data, were employed to compare the model's predicted data with experimental data. …”
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