Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study by Pei-long Li, Rong-wei Huang, Rong-man Xie, Juan Xie, Kai Liu

“…After incorporating the meteorological data into the analysis, the Mean Absolute Percentage Error (MAPE) for predicting ILI incidence using LSTM and attention-based stacked LSTM was 46.31% and 30.74%. …”
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KORELASI ANTARA SIKAP TOLERANSI DENGAN INTERAKSI SOSIAL DI KELAS SISWA KELAS IV SD GUGUS VII KUTA SELATAN TAHUN PELAJARAN 2017/2018 by Ni Kadek Manika Sari Manika Sari

“…The prerequisite test was data distribution normality test. After all the prerequisite test fulfilled, statistics analysis that was used in this research is hypothesis test using correlation analysis product moment. …”
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Outspacing Planar Phased Arrays for Wireless Communications Infrastructure by B. G. M. van Ark, A. B. Smolders

“…The future mobile-data demand, driven by 5G and 6G wireless communications, puts enormous pressure on the required infrastructure. …”
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Corneal Biomechanics as a Causal Factor in Myopia and Astigmatism: Evidence from Mendelian Randomization by Pinghui Wei, PhD, Guoge Han, PhD, Qi Su, MBBS, Lanbo Jia, MBBS, Chao Xue, PhD, Yan Wang, PhD

“…Participants: Corneal biomechanical data were obtained from 97 653 European participants in the UK Biobank, whereas refractive error data were sourced from the UK Biobank and FinnGen consortia. …”
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Segmental Dynamic Duty Cycle Control for Sampling Scheduling in Wireless Sensor Networks by Lufeng Mo, Yujia Jiang, Guoying Wang, Jizhong Zhao

“…Using a priori knowledge obtained by means of analysis on earlier sensing data, dynamic duty cycle is determined according to the linear degree of data in each segment. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. The analysis reveals that XGBoost delivers the best performance, with a Mean Absolute Error (MAE) of 6,665 and an R² score of 0.978, outperforming Random Forest (MAE: 9,209; R²: 0.947) and Linear Regression (MAE: 10,917; R²: 0.896). …”
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Practical use of on-board fuel metering systems of diesel locomotives by K. P. Popov

“…The results of on-board systems data processing are also presented, showing that the effect of the spread in the number of throttle position switches observed in ordinary operation of diesel shunter on fuel consumption is comparable to the error in determining this value according to the on-board systems data. …”
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Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features by Guowei Zhu, Miao Shi, Jia He

“…The new prediction model is essentially a machine learning model based on k-means clustering algorithm, which leverages feature extraction and data analysis to make predictions. Finally, the real data are used to predict the energy consumption of different routes and different driving directions on weekdays, respectively. …”
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UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…Timely, efficient, and accurate acquisition of crop growth data is crucial for agricultural decision-making and management. …”
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Numerical Simulation and Experimental Verification of Rotor Airflow Field Based on Finite Volume Method and Lattice Boltzmann Method by Kun Chang, Shengde Chen, Meimei Wang, Jiapeng Liao, Junwei Liu, Yubin Lan

“…Through comparison with the actual test data, it is found that the relative error of the velocity value of XFlow at 0.2 m and 0.4 m is small, while that of Fluent at 0 and 0.2 m is small. …”
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Sustainable authentication of molasses' botanical origin using infrared spectroscopy: Accuracy and greenness evaluation of spectral techniques by Macarena Rojas-Rioseco, Mecit Öztop, Cristian A. Fuentes, Martin Bravo, Ivan Smajlovic, Margarita Smajlovic, Karol Kołodziejski, Danuta Kruk, Víctor Muñoz, Rosario del P. Castillo

“…In response to increasing demands for sustainable analytical alternatives, this study aimed to develop and compare infrared spectroscopic methods to classify cane and beet molasses, focusing on sustainability of techniques while maintaining analytical performance. Data of portable and benchtop Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Mid-Infrared (FT-IR) spectrometers were evaluated using chemometric approaches, such as Principal Component Analysis (PCA) and classification models like Partial Least Squares Discriminant Analysis (PLS-DA) and k-Nearest Neighbors (k-NN). …”
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To study the anatomic and intraoperative factors responsible for gas bubble migration in the anterior chamber during femtosecond laser-assisted in situ keratomileusis (FS-LASIK) fl... by Suman Lata, Sohini Mandal, Rajesh Sinha, Jeewan S. Titiyal

“…Methods: A retrospective data analysis was conducted on 20 eyes of 12 patients undergoing bilateral laser-assisted in situ keratomileusis surgery, where intraoperative gas bubble migration was observed. …”
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Integrating experimental-based vulnerability mapping with intelligent identification of multi-aquifer groundwater salinization by Mohamed A. Yassin, Sani I. Abba, A.G. Usman, Syed Muzzamil Hussain Shah, Isam H. Aljundi, Shafik S. Shafik, Zaher Mundher Yaseen

“…Model performance was assessed using statistical parameters, including Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), Pearson correlation coefficient (PCC), and mean square error (MSE). …”
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Prediction of Pollutant Emissions from a Low-Speed Marine Engine Based on Harris Hawks Optimization and Lightgbm by Yue Chen, Yulong Shen, Miaomiao Wen, Cunfeng Wei, Junjie Liang, Yuanqiang Li, Ying Sun

“…The results show that changes in engine control parameters have significant influences on NOx and soot emissions from the engine, which can serve as the basis for the selection of the LGB model features; the LGB model was able to accurately predict pollutant concentrations from the engine with much higher accuracy than a single decision tree (DT) model; combining with HHO, the predictive ability of the LGB model was significantly improved, such as for the validation set prediction results, the mean absolute error (MAE) was reduced by about 20%, the mean squared error (MSE) was reduced by about 30%, and the coefficient of determination (R²) was increased by about 0.005; and the importance analysis of the model features indicated that the combustion condition of the fuel was highly correlated with the generation of the pollutants, and the fuel injection phases can be adjusted in practice to achieve highly efficient and low-emission processes of combustion. …”
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Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model by Ahsan Zafar, Yanbo Che, Muneer Ahmed, Muhammad Sarfraz, Ashfaq Ahmad, Mohammad Alibakhshikenari

“…Using real-time solar power production data spanning a year, these models are trained and evaluated using mean absolute error (MAE) and mean squared error (MSE) as performance metrics. …”
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A Multistep Method for Automatic Determination and Optimization of Microseismic P-Phase Arrival Times in a Coal Mine by Quanjie Zhu, Xiaohui Liu, Xiaoyun Liu, Haitao Chai, Jianjun Shi

“…This method is applied for a precise determination of P-phase arrival time; in addition, an automatic quality estimate is obtained to optimize and select the effective stations and used for source location. Through the analysis and validation of seismic data in a mine located in Hebei province, the result shows that this method can pick up the first arrival time of MS signal fast and accurately and automatically judge and remove the error station. …”
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LoD2 Building Reconstruction from Stereo Satellite Imagery using Deep Learning and Model-Driven Approach by Rojgar Qarani Ismael, Haval Sadeq

“…The suggested methodology is tested on the GeoEye-1 satellite imagery dataset for Erbil City, which is validated with ground truth data. The proposed algorithm presented promising results, it is shown that the model can predict building heights for ridge and eave to a mean absolute error of 0.70 m, and in the occluded area was approximately 1.0 m. …”
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Coupling of green building construction based on particle Swarm optimizing neural network algorithm by Wang Leigang, Li Shaohua, Wang Liang, Zhang Zheng, Zhou Yuchen, Chang Long

“…Compared with traditional methods, the prediction error of this algorithm is significantly reduced, and the data fitting accuracy is improved to 0.99809, indicate its effectiveness in predicting construction safety risks. …”
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Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine by Golmar Golmohammadi, Hamid Reza Eslami, Rashid Rasouli Harouni, Kamyab Eslami, Mahin Jamali, Seyed Mostafa Biazar, Kourosh Mohammadi

“…Monitoring changes in lake properties offers valuable insight into water resource management, agricultural demand, watershed analysis, and environmental monitoring. This study introduces an innovative approach that utilizes the Google Earth Engine platform, artificial intelligence (AI), and genetic algorithms to analyze water surface areas and estimate lake volume without the need for bathymetric data. …”
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Meteorological drought severity forecasting utilizing blended modelling by Aaditya Ahire, Nilima Zade, Umeed Mujawar, Dimple Mehta, Ketan Kotecha

“…With the large meteorological dataset that involves temperature, precipitation, humidity, and wind speed as features, the model integrates: • The tree capabilities of XGBoost perform feature selection very effectively. • Temporal Pattern Analysis using LSTM. • Insight obtained from the attention mechanism-based TabNet.Empirical results demonstrate that the proposed ensemble outperforms individual models, achieving the lowest Root Mean Square Error (RMSE: 0.6582) and Mean Absolute Error (MAE: 0.5377), and the highest Coefficient of Determination (R²: 0.5069). …”
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